CN118393570B

CN118393570B - Seismic inversion method and device, storage medium and electronic equipment

Info

Publication number: CN118393570B
Application number: CN202410870728.3A
Authority: CN
Inventors: 李坤; 陈若冰; 郑清文; 印兴耀; 宗兆云
Original assignee: China University of Petroleum East China
Current assignee: China University of Petroleum East China
Priority date: 2024-07-01
Filing date: 2024-07-01
Publication date: 2024-09-17
Anticipated expiration: 2044-07-01
Also published as: CN118393570A

Abstract

The invention provides a seismic inversion method, a seismic inversion device, a storage medium and electronic equipment, wherein the seismic inversion method comprises the following steps: acquiring target post-stack seismic data and acquiring target prior probability density distribution of each of K lithofacies categories; generating initial longitudinal wave impedance populations under each lithofacies category based on target prior probability density distribution of each lithofacies category respectively; determining a target longitudinal wave impedance population set under each lithofacies category based on the initial longitudinal wave impedance population under each lithofacies category; and determining longitudinal wave impedance of the target post-stack seismic data under each lithofacies category based on the target longitudinal wave impedance population set under each lithofacies category respectively, wherein the longitudinal wave impedance support of the target post-stack seismic data under each lithofacies category is used for determining a target longitudinal wave impedance and/or a target lithofacies identification result corresponding to the target post-stack seismic data. The embodiment of the invention can improve the convergence precision of the seismic inversion under the condition of avoiding sinking into a local mechanism.

Description

Seismic inversion method and device, storage medium and electronic equipment

Technical Field

The invention relates to the technical field of geophysical exploration, in particular to a seismic inversion method, a seismic inversion device, a storage medium and electronic equipment.

Background

At present, facing increasingly complex hydrocarbon reservoirs and the like, geophysical researchers have put higher requirements on the precision of seismic inversion, so that probabilistic seismic inversion based on statistics and random sampling is attracting extensive attention of researchers; however, the related art has problems such as a local mechanism being easily involved, and low convergence accuracy. Based on this, there is no better solution at present how to improve the convergence accuracy of seismic inversion without trapping local mechanisms.

Disclosure of Invention

In view of the above, the embodiments of the present invention provide a seismic inversion method, apparatus, storage medium, and electronic device, so as to solve the problems of easy sinking into local mechanism, low convergence accuracy, etc. in the related art; that is, the embodiment of the invention can avoid sinking local mechanism through the longitudinal wave impedance population under each lithofacies category, thereby effectively avoiding sinking local optimum and effectively improving convergence accuracy of seismic inversion; based on the method, the method and the device can improve the convergence accuracy of seismic inversion under the condition of avoiding sinking into a local mechanism.

According to an aspect of an embodiment of the present invention, there is provided a seismic inversion method, the method comprising:

Acquiring target post-stack seismic data, and acquiring target prior probability density distribution of each of K lithofacies categories, wherein one target prior probability density distribution is used for indicating prior probability density distribution obeying longitudinal wave impedance of the target post-stack seismic data under the corresponding lithofacies category, the target post-stack seismic data comprises seismic sampling values of each of N sampling points, and K and N are positive integers;

Generating initial longitudinal wave impedance groups under each lithofacies category based on target prior probability density distribution of each lithofacies category respectively, wherein one longitudinal wave impedance group comprises longitudinal wave impedance of each longitudinal wave impedance individual in NP longitudinal wave impedance individuals, the longitudinal wave impedance of each longitudinal wave impedance individual in the initial longitudinal wave impedance group is obtained by sampling based on the target prior probability density distribution, and NP is an integer greater than 1;

Determining a target longitudinal wave impedance population set under each lithofacies category based on the initial longitudinal wave impedance population under each lithofacies category;

And determining longitudinal wave impedance of the target post-stack seismic data under each lithofacies category based on the target longitudinal wave impedance population set under each lithofacies category, wherein the longitudinal wave impedance support of the target post-stack seismic data under each lithofacies category is used for determining a target longitudinal wave impedance and/or a target lithofacies identification result corresponding to the target post-stack seismic data.

According to another aspect of an embodiment of the present invention, there is provided a seismic inversion apparatus, the apparatus comprising:

The acquisition unit is used for acquiring target post-stack seismic data and acquiring target prior probability density distribution of each lithofacies category in K lithofacies categories, wherein one target prior probability density distribution is used for indicating prior probability density distribution obeying longitudinal wave impedance of the target post-stack seismic data under the corresponding lithofacies category, the target post-stack seismic data comprises seismic sampling values of each sampling point in N sampling points, and K and N are positive integers;

The processing unit is used for generating initial longitudinal wave impedance groups under each lithofacies category based on the target prior probability density distribution of each lithofacies category respectively, one longitudinal wave impedance group comprises longitudinal wave impedance of each longitudinal wave impedance individual in NP longitudinal wave impedance individuals, the longitudinal wave impedance of one longitudinal wave impedance individual in the initial longitudinal wave impedance group is obtained by sampling based on the target prior probability density distribution, and NP is an integer greater than 1;

the processing unit is further used for determining a target longitudinal wave impedance population set under each lithofacies category based on the initial longitudinal wave impedance population under each lithofacies category respectively;

the processing unit is further configured to determine a longitudinal wave impedance of the target post-stack seismic data under each lithofacies category based on the target longitudinal wave impedance population set under each lithofacies category, where the longitudinal wave impedance support of the target post-stack seismic data under each lithofacies category is used for determining a target longitudinal wave impedance and/or a target lithofacies recognition result corresponding to the target post-stack seismic data.

According to another aspect of embodiments of the present invention, there is provided an electronic device comprising a processor, and a memory storing a program, wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the above mentioned method.

According to another aspect of embodiments of the present invention, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the above-mentioned method.

According to another aspect of embodiments of the present invention, there is provided a computer program product comprising a computer program, wherein the computer program, when being executed by a processor, is adapted to cause a computer to carry out the above mentioned method.

According to the embodiment of the invention, after the target post-stack seismic data and the target prior probability density distribution of each of K lithofacies categories are obtained, initial longitudinal wave impedance groups under each lithofacies category are generated based on the target prior probability density distribution of each lithofacies category respectively, one target prior probability density distribution is used for indicating the prior probability density distribution obeyed by the longitudinal wave impedance of the target post-stack seismic data under the corresponding lithofacies category, the target post-stack seismic data comprises seismic sampling values of each sampling point in N sampling points, one longitudinal wave impedance group comprises the longitudinal wave impedance of each longitudinal wave impedance individual in NP longitudinal wave impedance individuals, the longitudinal wave impedance of one longitudinal wave impedance individual in one initial longitudinal wave impedance group is obtained based on one target prior probability density distribution sample, and K and N are positive integers and NP is an integer greater than 1. Further, a set of target longitudinal wave impedance populations under each facies category may be determined based on the initial longitudinal wave impedance populations under each facies category, respectively; and the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category can be determined based on the target longitudinal wave impedance population set under each lithofacies category respectively, and the longitudinal wave impedance support of the target post-stack seismic data under each lithofacies category is used for determining the target longitudinal wave impedance and/or the target lithofacies identification result corresponding to the target post-stack seismic data. Therefore, the embodiment of the invention can avoid sinking local mechanism through longitudinal wave impedance population under each lithofacies category, can effectively avoid sinking local optimum, and can effectively improve convergence accuracy of seismic inversion; that is, the embodiments of the present invention may improve the convergence accuracy of seismic inversion without trapping a local mechanism.

Drawings

Further details, features and advantages of the invention are disclosed in the following description of exemplary embodiments with reference to the following drawings, in which:

FIG. 1 illustrates a flow diagram of a seismic inversion method according to an exemplary embodiment of the invention;

FIG. 2 illustrates a flow diagram of another seismic inversion method according to an exemplary embodiment of the invention;

FIG. 3 shows a schematic diagram of an inversion result according to an exemplary embodiment of the invention;

FIG. 4 shows a schematic diagram of another inversion result according to an exemplary embodiment of the invention;

FIG. 5 shows a schematic diagram of yet another inversion result according to an exemplary embodiment of the invention;

FIG. 6 shows a schematic diagram of yet another inversion result according to an exemplary embodiment of the invention;

FIG. 7 illustrates a schematic diagram of a runtime in accordance with an exemplary embodiment of the present invention;

FIG. 8 illustrates a schematic diagram of a seismic profile inversion result in accordance with an exemplary embodiment of the invention; wherein fig. 8 (a) shows a schematic diagram of an actual post-stack seismic section according to an exemplary embodiment of the present invention, fig. 8 (b) shows a schematic diagram of a low frequency background of a compressional wave impedance according to an exemplary embodiment of the present invention, fig. 8 (c) shows a schematic diagram of a compressional wave impedance according to an exemplary embodiment of the present invention, and fig. 8 (d) shows a schematic diagram of a lithofacies recognition result according to an exemplary embodiment of the present invention;

FIG. 9 shows a schematic block diagram of a seismic inversion apparatus according to an exemplary embodiment of the invention;

fig. 10 shows a block diagram of an exemplary electronic device that can be used to implement an embodiment of the invention.

Detailed Description

Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While the invention is susceptible of embodiment in the drawings, it is to be understood that the invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided to provide a more thorough and complete understanding of the invention. It should be understood that the drawings and embodiments of the invention are for illustration purposes only and are not intended to limit the scope of the present invention.

It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.

The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below. It should be noted that the terms "first," "second," and the like herein are merely used for distinguishing between different devices, modules, or units and not for limiting the order or interdependence of the functions performed by such devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those skilled in the art will appreciate that "one or more" is intended to be construed as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of such messages or information.

It should be noted that, the execution body of the seismic inversion method provided by the embodiment of the invention may be one or more electronic devices, which is not limited in the invention; the electronic device may be a terminal (i.e. a client) or a server, and when the execution body includes a plurality of electronic devices and the plurality of electronic devices include at least one terminal and at least one server, the seismic inversion method provided by the embodiment of the invention may be executed jointly by the terminal and the server. Accordingly, the terminals referred to herein may include, but are not limited to: smart phones, notebook computers, desktop computers, intelligent voice interaction devices, and the like. The server mentioned herein may be an independent physical server, or may be a server cluster or a distributed system formed by a plurality of physical servers, or may be a cloud server that provides cloud services, cloud databases, cloud computing (cloud computing), cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network ), and basic cloud computing services such as big data and artificial intelligence platforms, and so on.

Based on the above description, the embodiments of the present invention propose a seismic inversion method that can be performed by the above-mentioned electronic device (terminal or server); or the seismic inversion method may be performed by both the terminal and the server. For convenience of explanation, the following description will take electronic equipment to execute the seismic inversion method as an example; as shown in FIG. 1, the seismic inversion method may include the following steps S101-S104:

S101, acquiring target post-stack seismic data and acquiring target prior probability density distribution of each of K lithofacies categories, wherein one target prior probability density distribution is used for indicating prior probability density distribution obeyed by longitudinal wave impedance of the target post-stack seismic data under the corresponding lithofacies category, the target post-stack seismic data comprises seismic sampling values of each of N sampling points, and K and N are positive integers.

Alternatively, the K facies categories may include, but are not limited to: mudstones, sandstones, etc., to which embodiments of the present invention are not limited. Optionally, the data related to the embodiment of the present invention may be data in a time domain, etc.; accordingly, a sample point may be a time sample point, i.e., a sample point may be a depth level, etc.

In the embodiment of the present invention, the target post-stack seismic data may be any post-stack seismic data (a post-stack seismic data may also be referred to as post-stack seismic data of one seismic trace), which is not limited in the embodiment of the present invention. Optionally, the number of the target post-stack seismic data may be one or more, which is not limited in the embodiment of the present invention; optionally, when performing seismic inversion on a target area, each channel of post-stack seismic data in the plurality of channels of post-stack seismic data in the target area can be used as target post-stack seismic data, so that seismic inversion is performed on each target post-stack seismic data in the plurality of target post-stack seismic data in the target area, and thus target longitudinal wave impedance and/or target lithology recognition results corresponding to each target post-stack seismic data are obtained, and the target longitudinal wave impedance and/or the target lithology recognition results corresponding to each target post-stack seismic data are spliced, so that seismic inversion results of the target area (namely splicing results of target longitudinal wave impedance corresponding to each target post-stack seismic data and/or splicing results of target lithology recognition results corresponding to each target post-stack seismic data) are obtained, and seismic inversion of the target area is realized; alternatively, the target area may be any area, which is not limited in the embodiment of the present invention. When the plurality of target post-stack seismic data are located on the same section, the seismic inversion result of the target area may also be referred to as the seismic section inversion result of the target area.

It should be noted that, in the framework of hierarchical bayesian inference, the prior probability density distribution of longitudinal wave impedance can be effectively introduced into the seismic inversion. Because of differences in the statistical characteristics of the compressional wave impedance in formations of different lithology (i.e., lithofacies classes), it may be assumed that the formation compressional wave impedance (i.e., compressional wave impedance) follows a mixed probability density distribution; wherein the mixed probability density distribution to which the longitudinal wave impedance is subject may comprise probability density distributions to which the longitudinal wave impedance is subject under each lithofacies class. Alternatively, the longitudinal wave impedance may follow a mixed Laplace probability density distribution, a mixed cauchy probability density distribution, or the like, which is not limited by the embodiment of the present invention. For ease of illustration, longitudinal wave impedance obeying hybrid Laplace probability density distribution will be described hereinafter. Illustratively, the mixed Laplace probability density distribution obeying the longitudinal wave impedance may be as shown in equation 1.1:

1.1

Wherein K is the number of facies categories (also referred to as the number of Laplace components, i.e., one facies category corresponds to one Laplace component, which is the same as the number of facies categories); accordingly, p (m) may represent an a priori probability density distribution of longitudinal wave impedance, then each a priori Laplace component may have a different a priori mean valuePrior covarianceM may represent the longitudinal wave impedance,Represents the kth prior Laplace component (i.e., the kth Laplace prior probability density distribution, K ε [1, K ]), of the K prior Laplace components, and λ _k represents the prior duty cycle of the kth lithofacies class. Alternatively, the prior mean, the prior covariance, and the prior duty ratio of the prior Laplace component may be set empirically, or may be set according to actual requirements, which is not limited by the embodiment of the present invention. For example, a priori mean, a priori covariance, and a priori duty cycle of a priori Laplace component may be obtained from well log data (also referred to as well log interpretation data); as another example, the prior duty cycle for one facies class may be 0.5, etc. when the prior duty cycle cannot be accurately obtained. It should be noted that, one prior mean value may include a longitudinal wave impedance mean value of each sampling point (may also be referred to as a mean value of each sampling point), that is, one prior mean value may be an N-dimensional vector; accordingly, an a priori covariance may include the covariance between any two sample points and may include the longitudinal wave impedance variance of any one sample point. In the embodiment of the present invention, one covariance may be a covariance matrix.

Based on this, a target prior probability density may be a prior Laplace component to which longitudinal wave impedance under the target post-stack seismic data obeys. It should be noted that, the prior mean value and the prior covariance in one target prior probability density distribution correspond to the sub-region where the target post-stack seismic data is located, for example, the prior mean value and the prior covariance in one target prior probability density distribution may be obtained through logging data in the sub-region where the target post-stack seismic data is located, and so on.

Optionally, the target post-stack seismic data may be acquired by, but not limited to, the following:

The first acquisition mode is as follows: a plurality of post-stack seismic data can be stored in the storage space of the electronic device, in which case any post-stack seismic data in the plurality of post-stack seismic data can be used as target post-stack seismic data; or when the seismic inversion is performed on the target area, each post-stack seismic data in the plurality of post-stack seismic data (i.e. a plurality of post-stack seismic data) in the target area can be used as one target post-stack seismic data.

The second acquisition mode is as follows: the electronic device may obtain the seismic data download link and take the seismic data downloaded based on the seismic data download link as the target post-stack seismic data.

The third acquisition mode is as follows: the electronic device may be connected to a plurality of receivers, in which case the electronic device may receive a plurality of channels of seismic data via the plurality of receivers, and obtain target post-stack seismic data based on the received plurality of channels of seismic data, e.g., perform operations such as noise cancellation on the received plurality of channels of seismic data, obtain a plurality of post-stack seismic data, and select one post-stack seismic data from the plurality of post-stack seismic data, to use the selected post-stack seismic data as the target post-stack seismic data, or use each post-stack seismic data in the plurality of post-stack seismic data as the target post-stack seismic data, respectively, and so on.

Correspondingly, the storage space of the electronic device can store prior probability density distribution (such as prior mean value, prior covariance and the like of prior probability density distribution of longitudinal wave impedance compliance of each post-stack seismic data under each lithofacies category) of each post-stack seismic data in a plurality of post-stack seismic data, and the target post-stack seismic data is post-stack seismic data determined in the plurality of post-stack seismic data, in which case, the electronic device can acquire the prior probability density distribution of longitudinal wave impedance compliance of the target post-stack seismic data under each lithofacies category from the storage space of the electronic device so as to acquire the target prior probability density distribution of each lithofacies category; or the electronic equipment can acquire a probability density distribution downloading link and download the target prior probability density distribution of each lithology category based on the probability density distribution downloading link; or the prior probability density distribution obeyed by the longitudinal wave impedance of each subarea in the plurality of subareas under each lithofacies category can be stored in the storage space of the electronic equipment, so that the electronic equipment can determine the subarea where the target post-stack seismic data is positioned, and the prior probability density distribution obeyed by the longitudinal wave impedance of the subarea where the target post-stack seismic data is positioned under each lithofacies category is used as the prior probability density distribution obeyed by the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category so as to realize acquisition of the target prior probability density distribution of each lithofacies category; or the electronic equipment can also acquire logging data of a subarea where the target post-stack seismic data is located, acquire target prior probability density distribution of each lithofacies category based on the logging data, and the like; the embodiment of the present invention is not limited thereto.

S102, generating initial longitudinal wave impedance groups under each lithofacies category based on target prior probability density distribution of each lithofacies category, wherein one longitudinal wave impedance group comprises longitudinal wave impedance of each longitudinal wave impedance individual in NP longitudinal wave impedance individuals, the longitudinal wave impedance of one longitudinal wave impedance individual in the initial longitudinal wave impedance group is obtained by sampling based on the target prior probability density distribution, and NP is an integer greater than 1.

Specifically, for any one of the K facies categories, the electronic device may generate an initial longitudinal wave impedance population under any one facies category based on a target prior probability density distribution for the any one facies category. It should be appreciated that any facies class may correspond to NP longitudinal wave impedance individuals in a single simulation.

Optionally, for any longitudinal wave impedance individual in any lithology category, the electronic device may sample through a target prior probability density distribution of any lithology category to obtain a longitudinal wave impedance of any longitudinal wave impedance individual in an initial longitudinal wave impedance population in any lithology category, and a longitudinal wave impedance of one longitudinal wave impedance individual in one longitudinal wave impedance population in one lithology category may also be referred to as a longitudinal wave impedance of a corresponding longitudinal wave impedance individual in a corresponding longitudinal wave impedance population in a corresponding lithology category; that is, the electronic device may perform NP sampling through the target prior probability density distribution of any lithofacies category to obtain the longitudinal wave impedance of each of NP longitudinal wave impedance individuals under any lithofacies category, so as to obtain an initial longitudinal wave impedance population under any lithofacies category, that is, one longitudinal wave impedance in the initial longitudinal wave impedance population under any lithofacies category is the initial longitudinal wave impedance of one longitudinal wave impedance individual under any lithofacies category, and each longitudinal wave impedance in the initial longitudinal wave impedance population under any lithofacies category is obtained through the target prior probability density distribution sampling of any lithofacies category.

Based on this, NP markov chains (may also be referred to as markov chains) under any lithology category may be obtained, the initial state of one markov chain under any lithology category may be the longitudinal wave impedance of one longitudinal wave impedance individual in the initial longitudinal wave impedance population under any lithology category, and the dimension of one longitudinal wave impedance may be N, that is, one longitudinal wave impedance may be an N-dimensional vector (including the longitudinal wave impedance values of each of the N sampling points). It can be seen that under different facies categories, the initial state of each markov chain may be sampled from the target prior probability density distribution for the corresponding facies category, that is, the initial state of each markov chain under any one facies category may be sampled from the target prior probability density distribution for any one facies category.

S103, determining a target longitudinal wave impedance population set under each lithology category based on the initial longitudinal wave impedance population under each lithology category.

In the embodiment of the invention, aiming at any one of K lithofacies categories, the electronic equipment can iterate each Markov chain under the any one lithofacies category respectively, namely, iteration can be carried out on each longitudinal wave impedance individual under the any one lithofacies category so as to update the longitudinal wave impedance of each longitudinal wave impedance individual under the any one lithofacies category, thereby realizing population update on the longitudinal wave impedance population under the any one lithofacies category and further determining the target longitudinal wave impedance population set under the any one lithofacies category.

Optionally, the electronic device may iterate each markov chain under any lithofacies category at the same time, that is, may iterate each longitudinal wave impedance in the current longitudinal wave impedance population under any lithofacies category (i.e., the longitudinal wave impedance population under the t-th iteration of any lithofacies category) in parallel (i.e., each longitudinal wave impedance individual under the current iteration of any lithofacies category may iterate in parallel) so as to obtain the next longitudinal wave impedance population of the current longitudinal wave impedance population under any lithofacies category (i.e., the longitudinal wave impedance population under the t+1th iteration of any lithofacies category); based on the above, the embodiment of the invention can effectively improve the convergence efficiency and the calculation efficiency of the seismic inversion. Wherein T e [1, T-1], T is the total number of iterations when the iterations are stopped, and T is an integer greater than 1.

S104, determining longitudinal wave impedance of the target post-stack seismic data under each lithofacies category based on the target longitudinal wave impedance population sets under each lithofacies category, wherein the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category is used for determining a target longitudinal wave impedance and/or a target lithofacies identification result corresponding to the target post-stack seismic data.

The longitudinal wave impedance of the target post-stack seismic data under each lithofacies category, the target longitudinal wave impedance corresponding to the target post-stack seismic data, the target lithofacies identification result, and the like may be inversion results of the target post-stack seismic data, that is, the inversion results of the target post-stack seismic data may include at least one of: longitudinal wave impedance of the target post-stack seismic data under each lithofacies category, target longitudinal wave impedance, and target lithofacies identification results.

In one embodiment, the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category may be used to determine a target lithofacies recognition result corresponding to the target post-stack seismic data; the target lithofacies recognition result may include a lithofacies recognition result of each sampling point (i.e., may include a lithofacies recognition result of each sampling point under the target post-stack seismic data), and one longitudinal wave impedance includes a longitudinal wave impedance value of each sampling point (i.e., includes a longitudinal wave impedance value of a corresponding longitudinal wave impedance at each sampling point). Based on the above, the electronic device may traverse each of the N sampling points, and use the currently traversed sampling point as the current sampling point; determining the analog longitudinal wave impedance value of the current sampling point under each lithofacies category from the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category, and determining the reference longitudinal wave impedance value of the current sampling point under each lithofacies category from the reference longitudinal wave impedance under each lithofacies category, wherein one longitudinal wave impedance under one lithofacies category can comprise the longitudinal wave impedance value of each sampling point under the corresponding lithofacies category; further, difference value operation can be performed on the analog longitudinal wave impedance value and the reference longitudinal wave impedance value of the current sampling point under each lithofacies category respectively to obtain a longitudinal wave impedance difference value operation result of the current sampling point under each lithofacies category, and the lithofacies category corresponding to the minimum value in the longitudinal wave impedance difference value operation result of the current sampling point under each lithofacies category is used as a lithofacies identification result of the current sampling point; and after traversing each sampling point in the N sampling points, obtaining a target lithofacies recognition result. Optionally, the reference longitudinal wave impedance under each lithofacies category may be empirically set, or may be set according to actual requirements, which is not limited by the embodiment of the present invention; for example, the reference longitudinal wave impedance under any facies category may be an a priori average in a target a priori probability density distribution for any facies category, or determined from log data for a sub-region where target post-stack seismic data is located, where the log data for a sub-region may include log data for each well in at least one well.

Specifically, for any one of the K lithofacies categories, the electronic device may perform a difference operation on the analog longitudinal wave impedance value and the reference longitudinal wave impedance value of the current sampling point in any one of the K lithofacies categories, to obtain a longitudinal wave impedance difference operation result of the current sampling point in any one of the lithofacies categories, so as to obtain a longitudinal wave impedance difference operation result of the current sampling point in each of the lithofacies categories. The lithofacies category corresponding to the longitudinal wave impedance difference value operation result under one lithofacies category is the corresponding lithofacies category, namely the longitudinal wave impedance difference value operation result under any lithofacies category corresponds to any lithofacies category.

For example, assuming that the K lithofacies categories include mudstone and sandstone, and that the result of the longitudinal wave impedance difference operation between the simulated longitudinal wave impedance value of the current sampling point under the mudstone and the reference longitudinal wave impedance value is smaller than the result of the longitudinal wave impedance difference operation between the simulated longitudinal wave impedance value of the current sampling point under the sandstone and the reference longitudinal wave impedance value, it may be determined that the minimum value in the result of the longitudinal wave impedance difference operation of the current sampling point under each lithofacies category is the result of the longitudinal wave impedance difference operation of the current sampling point under the mudstone, and it may be determined that the lithofacies category corresponding to the minimum value in the result of the longitudinal wave impedance difference operation of the current sampling point under each lithofacies category is the mudstone, and then the electronic device may use the mudstone as the lithofacies identification result of the current sampling point.

In another embodiment, the longitudinal wave impedance of the target post-stack seismic data under each lithofacies class may be used to determine a target longitudinal wave impedance corresponding to the target post-stack seismic data; the target longitudinal wave impedance may include a target longitudinal wave impedance value of each sampling point (i.e., may include a target longitudinal wave impedance value of each sampling point under the target post-stack seismic data). Based on the method, the electronic equipment can take the simulated longitudinal wave impedance value corresponding to the minimum value in the longitudinal wave impedance difference value operation result of the current sampling point under each lithofacies category as the target longitudinal wave impedance value of the current sampling point, so as to obtain the target longitudinal wave impedance after traversing each sampling point in the N sampling points. The analog longitudinal wave impedance value corresponding to the longitudinal wave impedance difference operation result of the current sampling point under one lithofacies category is the analog longitudinal wave impedance value of the current sampling point under the corresponding lithofacies category, namely the analog longitudinal wave impedance value corresponding to the longitudinal wave impedance difference operation result of the current sampling point under any lithofacies category is the analog longitudinal wave impedance value of the current sampling point under any lithofacies category. For example, assuming that the K lithology categories include mudstone and sandstone, and a result of a longitudinal wave impedance difference operation between the simulated longitudinal wave impedance value of the current sampling point under the mudstone and the reference longitudinal wave impedance value is smaller than a result of a longitudinal wave impedance difference operation between the simulated longitudinal wave impedance value of the current sampling point under the sandstone and the reference longitudinal wave impedance value, the electronic device may use the simulated longitudinal wave impedance value of the current sampling point under the mudstone as the target longitudinal wave impedance value of the current sampling point.

According to the embodiment of the invention, after the target post-stack seismic data and the target prior probability density distribution of each of the K lithofacies categories are obtained, initial longitudinal wave impedance groups under each lithofacies category are generated based on the target prior probability density distribution of each lithofacies category respectively, one target prior probability density distribution is used for indicating the prior probability density distribution obeyed by the longitudinal wave impedance of the target post-stack seismic data under the corresponding lithofacies category, the target post-stack seismic data comprises seismic sampling values of each sampling point in N sampling points, one longitudinal wave impedance group comprises the longitudinal wave impedance of each longitudinal wave impedance individual in NP longitudinal wave impedance individuals, the longitudinal wave impedance of one longitudinal wave impedance individual in one initial longitudinal wave impedance group is obtained based on one target prior probability density distribution sample, M and N are positive integers, and NP is an integer greater than 1. Further, a set of target longitudinal wave impedance populations under each facies category may be determined based on the initial longitudinal wave impedance populations under each facies category, respectively; and the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category can be determined based on the target longitudinal wave impedance population set under each lithofacies category respectively, and the longitudinal wave impedance support of the target post-stack seismic data under each lithofacies category is used for determining the target longitudinal wave impedance and/or the target lithofacies identification result corresponding to the target post-stack seismic data. Therefore, the embodiment of the invention can avoid sinking local mechanism through longitudinal wave impedance population under each lithofacies category, can effectively avoid sinking local optimum, and can effectively improve convergence accuracy of seismic inversion; that is, the embodiments of the present invention may improve the convergence accuracy of seismic inversion without trapping a local mechanism.

Based on the above description, the embodiment of the invention also provides a more specific seismic inversion method. Accordingly, the seismic inversion method may be performed by the above-mentioned electronic device (terminal or server); or the seismic inversion method may be performed by both the terminal and the server. For convenience of explanation, the following description will take electronic equipment to execute the seismic inversion method as an example; referring to FIG. 2, the seismic inversion method may include the following steps S201-S206:

s201, acquiring target post-stack seismic data and acquiring target prior probability density distribution of each of K lithofacies categories, wherein one target prior probability density distribution is used for indicating prior probability density distribution obeying longitudinal wave impedance of the target post-stack seismic data under the corresponding lithofacies category, and the target post-stack seismic data comprises seismic sampling values of each of N sampling points.

S202, generating initial longitudinal wave impedance groups under each lithofacies category based on target prior probability density distribution of each lithofacies category, wherein one longitudinal wave impedance group comprises longitudinal wave impedance of each longitudinal wave impedance individual in NP longitudinal wave impedance individuals, and the longitudinal wave impedance of one longitudinal wave impedance individual in the initial longitudinal wave impedance group is obtained by sampling based on the target prior probability density distribution.

Wherein the longitudinal wave impedance of a longitudinal wave impedance individual in an initial longitudinal wave impedance population is the initial state of a Markov chain.

S203, for any one of the K facies categories, carrying out population update on an initial longitudinal wave impedance population under any one facies category through a particle swarm algorithm to obtain an updated longitudinal wave impedance population under any one facies category, and adding the updated longitudinal wave impedance population under any one facies category into a longitudinal wave impedance population set under any one facies category to realize state update of NP Markov chains, wherein one longitudinal wave impedance individual corresponds to one Markov chain.

The initial longitudinal wave impedance population under any facies category may be a longitudinal wave impedance population under any facies category at iteration 1, and the updated longitudinal wave impedance population under any facies category may be a longitudinal wave impedance population under any facies category at iteration 2; that is, when t is 1, the longitudinal wave impedance population of any facies class at the t-th iteration may be the initial longitudinal wave impedance population of any facies class, and the longitudinal wave impedance population of any facies class at the t+1th iteration may be the updated longitudinal wave impedance population of any facies class. The state update of one markov chain may also refer to iteration of the corresponding markov chain, and the one population update under any lithofacies category may include one iteration of each markov chain under any lithofacies category, i.e., one state update of each markov chain under any lithofacies category.

In the embodiment of the present invention, the electronic device may add the longitudinal wave impedance population of any lithofacies class under each iteration to the longitudinal wave impedance population set of any lithofacies class, that is, the longitudinal wave impedance population set of any lithofacies class may sequentially include the longitudinal wave impedance population of any lithofacies class under each iteration. It should be appreciated that after the t+1st iteration is completed, the set of longitudinal wave impedance populations under any facies category may include the longitudinal wave impedance population under any facies category at the 1 st iteration, … …, and the longitudinal wave impedance population under any facies category at the t+1st iteration.

S204, determining a current longitudinal wave impedance inversion result under any lithofacies category based on the updated longitudinal wave impedance population under any lithofacies category, and judging whether the longitudinal wave impedance inversion result under any lithofacies category tends to be stable or not based on the current longitudinal wave impedance inversion result.

It should be appreciated that during the t+1st iteration, the electronic device may determine a current longitudinal wave impedance inversion result for any lithofacies class (i.e., a longitudinal wave impedance inversion result for any lithofacies class for the t+1st iteration) based on the longitudinal wave impedance population for any lithofacies class for the t+1st iteration, to determine whether the longitudinal wave impedance inversion result for any lithofacies class tends to be stable based on the longitudinal wave impedance inversion result for any lithofacies class for the t+1st iteration.

In one embodiment, when determining a longitudinal wave impedance inversion result of any lithofacies class at the t+1st iteration (i.e., a current longitudinal wave impedance inversion result of any lithofacies class), the average value operation can be performed on the longitudinal wave impedance population of any lithofacies class at the t+1st iteration to obtain a longitudinal wave impedance inversion result of any lithofacies class at the t+1st iteration (i.e., an average value operation result between the longitudinal wave impedances of any lithofacies class in the longitudinal wave impedance population at the t+1st iteration); that is, when t is 1, the mean value operation can be performed on the updated longitudinal wave impedance population under any lithofacies category, so as to obtain the current longitudinal wave impedance inversion result under any lithofacies category.

In another embodiment, the electronic device may determine a determined longitudinal wave impedance population set under any facies category from the longitudinal wave impedance population set under any facies category, where the number of iterations corresponding to any longitudinal wave impedance population set in the determined longitudinal wave impedance population set under any facies category is greater than the number of iterations corresponding to all longitudinal wave impedance populations in the longitudinal wave impedance population set under any facies category except the determined longitudinal wave impedance population set under any facies category; when t+1 is smaller than H, the determined longitudinal wave impedance population set under any lithofacies category may be a longitudinal wave impedance population set under any lithofacies category, that is, the determined longitudinal wave impedance population set under any lithofacies category may include all longitudinal wave impedance population sets in the longitudinal wave impedance population set under any lithofacies category, and at this time, the number of longitudinal wave impedance population sets in the longitudinal wave impedance population set under any lithofacies category is smaller than H, where H is an integer greater than 1; when t+1 is greater than or equal to H, the determined longitudinal wave impedance population set under any facies category may include the longitudinal wave impedance population at each of the subsequent H iterations in the longitudinal wave impedance population set under any facies category, i.e., may include the subsequent H longitudinal wave impedance populations in the longitudinal wave impedance population set under any facies category. Optionally, the value of H may be the same as or different from the number of longitudinal wave impedance populations in the target longitudinal wave impedance population set in any lithofacies category, which is not limited in the embodiment of the present invention. Based on the above, the electronic device may perform a mean value operation on the determined longitudinal wave impedance population set under any lithofacies category, that is, may perform a mean value operation on each longitudinal wave impedance in the determined longitudinal wave impedance population set under any lithofacies category, to obtain a longitudinal wave impedance inversion result under the t+1th iteration for any lithofacies category, where the longitudinal wave impedance inversion result under the t+1th iteration for any lithofacies category may be a mean value operation result between each longitudinal wave impedance in the determined longitudinal wave impedance population set under any lithofacies category (that is, each longitudinal wave impedance included in all longitudinal wave impedance populations in the determined longitudinal wave impedance population set under any lithofacies category), and so on; the embodiment of the present invention is not limited thereto.

Optionally, when judging whether the longitudinal wave impedance inversion result under any lithofacies category tends to be stable based on the current longitudinal wave impedance inversion result, the electronic device may determine P historical longitudinal wave impedance inversion results under any lithofacies identification, and judge whether the longitudinal wave impedance inversion result under any lithofacies category tends to be stable based on the current longitudinal wave impedance inversion result and the P historical longitudinal wave impedance inversion results, where P is a positive integer; the P historical longitudinal wave impedance inversion results include a longitudinal wave impedance inversion result of any lithology category at each iteration in the previous P iterations of the t+1st iteration, that is, the current longitudinal wave impedance inversion result and the P historical longitudinal wave impedance inversion result may include a longitudinal wave impedance inversion result of any lithology category at each iteration in the subsequent p+1st iterations (i.e., the t-p+1st iteration to the t+1st iteration). Optionally, the electronic device may further determine whether t+1 is greater than P; if t+1 is greater than P, triggering and executing P historical longitudinal wave impedance inversion results under any lithofacies identification; if t+1 is less than or equal to P, determining that the longitudinal wave impedance inversion result under any lithofacies category does not tend to be stable.

Optionally, when judging whether the longitudinal wave impedance inversion result under any lithofacies category tends to be stable based on the current longitudinal wave impedance inversion result and the P historical longitudinal wave impedance inversion results, the electronic device may judge whether the current longitudinal wave impedance inversion result and the P historical longitudinal wave impedance inversion results obey steady distribution (may also be referred to as steady distribution), that is, may judge whether the P historical longitudinal wave impedance inversion results and the current longitudinal wave impedance inversion result are steady sequences; if the current longitudinal wave impedance inversion result and the P historical longitudinal wave impedance inversion results are subjected to steady distribution, determining that the longitudinal wave impedance inversion result under any lithofacies category tends to be stable, wherein the P historical longitudinal wave impedance inversion results and the current longitudinal wave impedance inversion result are stable sequences; if the current longitudinal wave impedance inversion result and the P historical longitudinal wave impedance inversion results do not follow steady-state distribution, determining that the longitudinal wave impedance inversion result under any lithofacies category does not tend to be stable, and at the moment, the P historical longitudinal wave impedance inversion results and the current longitudinal wave impedance inversion result are not stable sequences.

And S205, when the longitudinal wave impedance inversion result under any lithofacies category does not tend to be stable, continuing to update the population of the updated longitudinal wave impedance population under any lithofacies category until the longitudinal wave impedance inversion result under any lithofacies identification tends to be stable, so as to determine the target longitudinal wave impedance population set under any lithofacies category from the longitudinal wave impedance population set under any lithofacies category.

In the embodiment of the invention, when the longitudinal wave impedance inversion result under any lithofacies category tends to be stable, the population update under any lithofacies category can be stopped, and at this time, the longitudinal wave impedance population set under any lithofacies category can include the longitudinal wave impedance population under each iteration of any lithofacies category in T iterations, where T is the iteration number when the population update under any lithofacies category is stopped, i.e. T is the total iteration number under any lithofacies category.

Based on this, the set of longitudinal wave impedance populations under any facies class may include a longitudinal wave impedance population under each of T iterations for any facies class, the longitudinal wave impedance population under the t+1th iteration of the T iterations being obtained by population updating the longitudinal wave impedance population under the T iteration, T being an integer greater than 1, T e [1, T-1]. The determination mode of the longitudinal wave impedance population of any lithofacies category at the t+1st iteration can be as follows.

Correspondingly, when determining the longitudinal wave impedance population of any lithofacies category under the t+1th iteration, aiming at the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category under the t iteration and the nth sampling point in the N sampling points, the electronic equipment can take the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category under the t iteration as the ith longitudinal wave impedance under the t iteration, i epsilon [1, NP ], N epsilon [1, N ]; and determining a candidate longitudinal wave impedance value of the nth sampling point (namely, a candidate longitudinal wave impedance value of the nth sampling point in the ith longitudinal wave impedance of the (t+1) th iteration) based on the longitudinal wave impedance value of the nth sampling point in the ith longitudinal wave impedance of the (t) th iteration by a particle swarm algorithm, so as to obtain a candidate longitudinal wave impedance, wherein the candidate longitudinal wave impedance comprises candidate longitudinal wave impedance values of all the sampling points. Specifically, the electronic device may calculate the candidate longitudinal wave impedance value of the nth sampling point using equation 2.1:

2.1

Where t represents the number of iterations, m _(i,n) (t+1) represents the ith longitudinal wave impedance at the t+1 iteration (i.e., the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies class at the t+1 iteration, i.e., the longitudinal wave impedance of the ith longitudinal wave impedance individual at the t+1 iteration) and v _(i,n) (t+1) represents the speed of the nth sampling point in the ith longitudinal wave impedance at the t+1 iteration (i.e., the speed of the nth dimension of the ith Markov chain at the t+1 iteration). Based on this, when determining the candidate longitudinal wave impedance value of the nth sampling point based on the longitudinal wave impedance value of the nth sampling point in the ith longitudinal wave impedance at the nth iteration by the particle swarm optimization, the electronic apparatus may calculate the candidate longitudinal wave impedance value of the nth sampling point based on the longitudinal wave impedance value of the nth sampling point in the ith longitudinal wave impedance at the nth iteration and the speed of the nth sampling point in the ith longitudinal wave impedance at the t+1th iteration.

Specifically, the electronic device may calculate the speed of the nth sampling point in the ith longitudinal wave impedance of any lithofacies class under the t+1st iteration by using formula 2.2:

2.2

Wherein ω is an inertial weight, c ₁、c₂ are normal numbers (also referred to as acceleration coefficients), r ₁、r₂ are random numbers (i.e., randomly generated) that vary within the range of [0,1 ]; alternatively, ω, c ₁, and c ₂ may be empirically set, or may be set according to actual requirements, which is not limited in the embodiment of the present invention. Accordingly, pbest _(i,n) (t) may represent the value of the longitudinal wave impedance at the nth sampling point in the optimal longitudinal wave impedance of the ith longitudinal wave impedance individual (i.e., the optimal position in the ith longitudinal wave impedance of any lithofacies class at each iteration in the previous t iterations, which may also be referred to as the individual history optimal position experienced by the ith longitudinal wave impedance individual in the previous t iterations); gbest _n (t) may represent the longitudinal wave impedance value of the nth sampling point included in the optimal longitudinal wave impedance of all longitudinal wave impedances of each longitudinal wave impedance individual in the previous t iterations (i.e., the historical optimal position of all individual positions experienced by all longitudinal wave impedance individuals under any lithofacies classification). Optionally, the electronic device may calculate an evaluation value of the longitudinal wave impedance by using a target evaluation function, and then the electronic device may determine, by using the target evaluation function, an evaluation value of each longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies class under each iteration; based on the above, the longitudinal wave impedance corresponding to the maximum value in the evaluation value of the ith longitudinal wave impedance (i.e. the longitudinal wave impedance of the ith longitudinal wave impedance in the previous t iterations of any lithofacies class) in each longitudinal wave impedance population in the previous t iterations can be used as the optimal longitudinal wave impedance of the ith longitudinal wave impedance (also referred to as the optimal longitudinal wave impedance of the ith longitudinal wave impedance in the previous t iterations); correspondingly, the longitudinal wave impedance corresponding to the maximum value in the evaluation value of each longitudinal wave impedance in the previous t iterations of any lithology category can be used as the optimal longitudinal wave impedance in all the longitudinal wave impedances of each longitudinal wave impedance individual in the previous t iterations of any lithology category. Optionally, the objective evaluation function may be set empirically, or may be set according to actual requirements, which is not limited in the embodiment of the present invention; for example, the objective evaluation function may be the loss function described below, or the like.

Further, the electronic device may calculate a reception probability of the candidate longitudinal wave impedance based on the ith longitudinal wave impedance, the candidate longitudinal wave impedance, and the target post-stack seismic data in the t-th iteration, and determine whether to receive the candidate longitudinal wave impedance based on the reception probability of the candidate longitudinal wave impedance; if the candidate longitudinal wave impedance is determined to be received, the candidate longitudinal wave impedance is used as the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies class under the t+1st iteration; if the candidate longitudinal wave impedance is not received, the ith longitudinal wave impedance in the t iteration is used as the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category in the t+1th iteration, so that the longitudinal wave impedance population of any lithofacies category in the t+1th iteration is determined. Optionally, the electronic device may determine a reception probability threshold, and if the reception probability of the candidate longitudinal wave impedance is greater than or equal to the reception probability threshold, may determine to receive the candidate longitudinal wave impedance; if the probability of receipt of the candidate longitudinal wave impedance is less than the probability of receipt threshold, it may be determined that the candidate longitudinal wave impedance is not acceptable. Optionally, the receiving probability threshold may be set according to experience or actual requirements, or may be obtained through random sampling, which is not limited in the embodiment of the present invention; illustratively, the probability of receipt threshold may be obtained by log (U [0,1 ]) sampling.

Optionally, when any of the facies classes is a kth facies class of the K facies classes, K e [1, K ], the electronic device may calculate the reception probability of the candidate longitudinal wave impedance using formula 2.3:

2.3

Wherein, The candidate longitudinal wave impedance (i.e., the candidate longitudinal wave impedance) of the ith longitudinal wave impedance individual in the t+1th iteration process (i.e., under the t+1th iteration) can be also referred to as the candidate state of the ith longitudinal wave impedance individual generated in the t+1th iteration process; The longitudinal wave impedance of the ith longitudinal wave impedance individual in the t-th iteration process can be expressed, and the current state of the ith longitudinal wave impedance individual in the t-th iteration process can be also called. Alternatively, p _k (m|d) may be the kth Laplace component of the posterior probability density distribution of longitudinal wave impedance, and the posterior probability density distribution p (m|d) may be a hybrid Laplace probability density distribution; l _k (m|d) may represent a loss function (also referred to as a target functional) equivalent to p _k (m|d).

Accordingly, the electronic device may calculate the loss function at the kth component using equation 2.4:

2.4

Where D may represent the target post-stack seismic data (as a vector), W may represent the seismic wavelet matrix (which may be obtained by sampling the number of seismic wavelets (e.g., rake wavelets) and sampling points, or may be set empirically or practically, without limitation, and D may be a differential matrix operator (which may be set empirically or practically, etc.), C _d may represent the covariance matrix of the noise in the observed seismic data (used to measure the uncertainty of the seismic data, with a larger covariance representing a lower signal-to-noise ratio of the seismic data, which may be determined by observing the noise data in the seismic data, or may be set empirically or practically, etc.). In addition, in the case of the optical fiber,The variance of the nth sample point (i.e. the variance of the longitudinal wave impedance of the nth sample point under the kth facies class) in the target prior probability density distribution (e.g. the kth Laplace probability density distribution) of the kth facies class may be represented,The center position of the nth sampling point in the target prior probability density distribution of the kth lithofacies class (i.e., the longitudinal wave impedance mean of the nth sampling point under the kth lithofacies class) may be represented. It should be noted that, the target post-stack seismic data may be represented as a convolution of the seismic wavelet and the seismic reflection coefficient, that is, the target post-stack seismic data may be represented as d= WDm +ε; the epsilon can represent the noise corresponding to the target post-stack seismic data, one seismic channel can correspond to one noise, namely the noise corresponding to the seismic channel where the target post-stack seismic data is located, and the dimension of one noise is equal to the number of sampling points.

In the embodiment of the invention, the Gaussian likelihood function p (d|m) of the target post-stack seismic data and the mixed Laplace probability density distribution (namely the sum of the target prior probability density distribution of each lithofacies category) are combined through a layered Bayesian formula, so that the posterior probability density distribution of the longitudinal wave impedance can be obtained, as shown in a formula 2.5:

2.5

Based on this, the posterior probability density distribution p (m|d) may be a hybrid Laplace probability density distribution, and then p (m|d) may be as shown in equation 2.6:

2.6

Wherein, based on the equation 2.5 and the equation 2.6, the kth Laplace component p _k (m|d) of the posterior probability density distribution can be as shown in the equation 2.7:

2.7

Further, the gaussian likelihood function p (d|m) =n (d-WDm; 0, c _d) and the kth Laplace prior probability density distribution (i.e., the target prior probability density distribution of the kth lithofacies class) may be substituted into formula 2.7, and natural logarithms of the posterior probability density distribution may be used to construct an equivalent target functional (i.e., natural logarithms may be taken for the equivalent two parts in formula 2.7 respectively), so as to equivalently obtain formula 2.4. The k-th Laplace prior probability density distribution may be as shown in formula 2.8:

2.8

Based on the above, when calculating the reception probability of the candidate longitudinal wave impedance based on the ith longitudinal wave impedance, the candidate longitudinal wave impedance and the target post-stack seismic data under the t-th iteration, the electronic device may determine the longitudinal wave impedance mean and the longitudinal wave impedance variance of each sampling point under any lithofacies class, e.g., determine the longitudinal wave impedance mean and the longitudinal wave impedance variance of each sampling point under any lithofacies class from the target prior probability density distribution of any lithofacies class; calculating the receiving probability of the candidate longitudinal wave impedance based on the ith longitudinal wave impedance, the candidate longitudinal wave impedance, the target post-stack seismic data and the longitudinal wave impedance mean value and the longitudinal wave impedance variance of each sampling point under any lithology category; that is, the i-th longitudinal wave impedance at the t-th iteration, the candidate longitudinal wave impedance, the target post-stack seismic data, and the longitudinal wave impedance mean and the longitudinal wave impedance variance of each sampling point under any lithofacies class may be substituted into equation 2.3 to calculate the reception probability of the candidate longitudinal wave impedance. Specifically, the electronic device may calculate the reception probability of the candidate longitudinal wave impedance by using the ith longitudinal wave impedance, the candidate longitudinal wave impedance, the target post-stack seismic data, the seismic wavelet matrix, the differential matrix operator, the covariance matrix of noise, the longitudinal wave impedance mean value and the longitudinal wave impedance variance of each sampling point under any lithofacies category, and the like.

Further, when determining the target longitudinal wave impedance population group under any lithofacies category from the longitudinal wave impedance population group under any lithofacies category, adding the last Q longitudinal wave impedance populations in the longitudinal wave impedance population group under any lithofacies category to the target longitudinal wave impedance population group under any lithofacies category to determine the target longitudinal wave impedance population group under any lithofacies category, wherein Q is a positive integer; optionally, Q may be set empirically or according to actual requirements, or may be determined according to a preset ratio threshold, etc.; for example, when Q is determined according to a preset proportion threshold, Q may be equal to a multiplication result between the preset proportion threshold and the number of longitudinal wave impedance populations in the set of longitudinal wave impedance populations under any lithofacies class; alternatively, the preset proportional threshold may be set empirically, or may be set according to actual requirements, which is not limited in the embodiment of the present invention.

S206, determining longitudinal wave impedance of the target post-stack seismic data under each lithofacies category based on the target longitudinal wave impedance population sets under each lithofacies category, wherein the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category is used for determining a target longitudinal wave impedance and/or a target lithofacies identification result corresponding to the target post-stack seismic data.

Specifically, when determining the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category based on the target longitudinal wave impedance population set under each lithofacies category, the electronic device may perform a mean operation on each longitudinal wave impedance in the target longitudinal wave impedance population set under any lithofacies category for any of the K lithofacies categories, to obtain the longitudinal wave impedance of the target post-stack seismic data under any lithofacies category, that is, the longitudinal wave impedance of the target post-stack seismic data under any lithofacies category may be a mean operation result between all the longitudinal wave impedances in the target longitudinal wave impedance population set under any lithofacies category. The longitudinal wave impedance of the target post-stack seismic data under any lithology category can also be called a posterior mean value of the longitudinal wave impedance under any lithology category.

Furthermore, the electronic device may further determine a longitudinal wave impedance covariance matrix, confidence interval indication data, and the like of the target post-stack seismic data under any lithofacies category based on the target longitudinal wave impedance population set under any lithofacies category, which is not limited by the embodiment of the present invention. Optionally, the confidence interval indication data of the target post-stack seismic data under any lithofacies category may include confidence intervals of each sampling point of the target post-stack seismic data under any lithofacies category; for any of the N sampling points, the longitudinal wave impedance variance of any of the sampling points in any of the lithofacies categories may be calculated based on all the longitudinal wave impedance values of any of the sampling points in the target longitudinal wave impedance population set in any of the lithofacies categories, so as to determine a confidence interval of the target post-stack seismic data at any of the sampling points in any of the lithofacies categories based on the longitudinal wave impedance variance of any of the sampling points in any of the lithofacies categories, e.g., a range of longitudinal wave impedance variances may correspond to a confidence interval.

Optionally, the electronic device may perform multiple simulations on the target post-stack seismic data to obtain a target longitudinal wave impedance and/or a target lithology recognition result of the target post-stack seismic data under each simulation in the multiple simulations; correspondingly, the average value operation can be carried out on the target longitudinal wave impedance of the target post-stack seismic data under each simulation, so as to obtain the target longitudinal wave impedance corresponding to the target post-stack seismic data; and counting the target lithofacies recognition results of the target post-stack seismic data under each simulation, for example, counting the lithofacies recognition results of any sampling point of the target post-stack seismic data under each simulation, and taking the lithofacies recognition result with the largest counting number as the lithofacies recognition result of any sampling point in the target lithofacies recognition results, thereby obtaining the target lithofacies recognition result.

In summary, in the embodiment of the present invention, the particle swarm algorithm may be introduced into a markov chain monte carlo algorithm (MCMC algorithm, a method for performing random simulation by using a markov chain), and the MCMC algorithm may be a Metropolis-hastens algorithm (a specific MCMC algorithm), so that the particle swarm algorithm may be introduced into the Metropolis-hastens algorithm to determine the candidate longitudinal wave impedance in the t+1st iteration process; therefore, the embodiment of the invention provides a seismic inversion algorithm combining a particle swarm optimization algorithm and a Monte Carlo model, namely an improved seismic probabilistic inversion and lithology recognition method of the particle swarm-Metropolis Hastings algorithm. Based on the method, under the framework of a Metropolis-Hastings algorithm, the PSO-MCMC algorithm combines the global optimization characteristic of a particle swarm algorithm, improves the generation process of candidate states through the motion state of particles (namely longitudinal wave impedance individuals), and can realize synchronous optimization by a plurality of Markov chains, so that the receiving probability of the candidate states can be improved, and the convergence efficiency of model parameters (namely longitudinal wave impedance) can be improved; PSO-MCMC has the advantages of particle swarm optimization algorithm and Markov (Markov) chain Monte Carlo model, and has important significance in improving the stability of longitudinal wave impedance seismic probabilistic inversion in the process of synchronously calculating the posterior mean value, covariance, confidence interval and other uncertainty quantization characteristics of parameters such as longitudinal wave impedance in the process of optimizing a plurality of Markov chain (synchronous optimization of a plurality of longitudinal wave impedance individuals).

In the embodiment of the invention, in order to further verify the feasibility of the seismic inversion mentioned in the embodiment of the invention, on one hand, a synthetic seismic record is established based on a conventional numerical model (namely, the complete data of a virtual underground geologic body matched with the wave impedance data of the seismic data can be simulated and calculated above), and inversion is carried out by using the seismic inversion method mentioned in the embodiment of the invention under different signal to noise ratio conditions; as shown in fig. 3-6, after 50 times of simulation under different signal-to-noise ratio conditions, the inversion result and the synthesized data keep high consistency, and the feasibility and the stability of the seismic inversion method are verified; that is, experiments are carried out under the condition of different signal to noise ratios, so that the influence of noise on the longitudinal wave impedance estimation precision of the seismic inversion method is small, the inversion error of the posterior mean value is small, and the feasibility and the stability of the seismic inversion method in the aspect of collaborative prediction of stratum lithofacies and longitudinal wave impedance are verified. The longitudinal wave impedance in each of fig. 3-6 may be a longitudinal wave impedance obtained by inversion of 10 times of earthquake, that is, may include the longitudinal wave impedance under each simulation of 10 times of simulation, and the abscissa of the longitudinal wave impedance in each of fig. 3-6 may be a longitudinal wave impedance value (unit: kg/m ² s (i.e., kg/square meter s)); the seismic data in each of fig. 3-6 may include seismic data synthesized from a mixed domain convolution model and zero-phase 30Hz Ricker wavelet (rake wavelet), seismic data fitted by 50 simulation results of two gaussian components (e.g., sandstone and mudstone), theoretical seismic data in the absence of noise pollution, etc., and the abscissa of the seismic data in each of fig. 3-6 may be in amplitude (units: m); the actual facies and the abscissa of the facies recognition results in each of fig. 3-6 may be the formation width, and are only exemplarily represented laterally with one color when the facies of one sampling point (e.g., the actual facies or the facies recognition results) are sandstone, and with another color when the facies of one sampling point are mudstone, so as to realize the facies representation of each sampling point in a width range, wherein the color used for representing the mudstone is darker than the color used for representing the sandstone.

On the other hand, the method provided by the embodiment of the invention tests the effectiveness of the seismic inversion method by comparing the operation speeds of the original inversion method and the seismic inversion method based on the particle swarm-MCMC algorithm; as shown in FIG. 7, the operation speed of the seismic inversion method in the embodiment of the invention is faster than that of the conventional MCMC algorithm under each simulation, i.e. the embodiment of the invention can effectively reduce the operation time and improve the calculation efficiency.

On the other hand, the embodiment of the invention verifies the effectiveness of actual seismic data processing, selects a two-dimensional seismic section with a complicated geological structure, and verifies the stability and effectiveness of the seismic inversion method by comparing the seismic inversion method with actual logging data. Illustratively, as shown in fig. 8, the (a) (i.e., the (a) marked sub-graph) in fig. 8 is a true post-stack seismic section (i.e., an actual post-stack seismic section, and contains 255 channels of seismic data in the lateral direction), the (b) (i.e., the (b) marked sub-graph) in fig. 8 is a low-frequency background of longitudinal wave impedance (i.e., a known low-frequency prior background, such as the data of the low-frequency portion of the longitudinal wave impedance obtained by logging, which can be established by means of software such as Jason (a kind of a geoscience platform), geoview (a kind of pre-stack and post-stack joint inversion software)), the (c) (i.e., the (c) marked sub-graph) in fig. 8 is the longitudinal wave impedance obtained by seismic inversion (here, the 10 simulated longitudinal wave impedance inversion result), and the (d) (i.e., the (d) marked sub-graph) in fig. 8 is a lithofacies recognition result (here, the 10 simulated lithofacies recognition statistic result); wherein, the abscissa is the width (unit: m) of the sampled stratum, and the ordinate is the depth in the time domain; alternatively, the longitudinal sampling interval may be 2 milliseconds (ms). Therefore, the longitudinal wave impedance and the lithology recognition result are well matched with logging data (namely, a true post-stack seismic section and the like), and the effectiveness and the practicability of the seismic inversion method provided by the embodiment of the invention are verified. The seismic profile inversion result shown in fig. 8 may include a longitudinal wave impedance and/or a lithology recognition result corresponding to each of 255 post-stack seismic data.

According to the embodiment of the invention, after the target post-stack seismic data and the target prior probability density distribution of each of the K lithofacies categories are acquired, initial longitudinal wave impedance groups under each lithofacies category are generated based on the target prior probability density distribution of each lithofacies category respectively, one longitudinal wave impedance group comprises longitudinal wave impedance of each longitudinal wave impedance individual in NP longitudinal wave impedance individuals, and the longitudinal wave impedance of one longitudinal wave impedance individual in one initial longitudinal wave impedance group is obtained based on sampling of one target prior probability density distribution; wherein the longitudinal wave impedance of a longitudinal wave impedance individual in an initial longitudinal wave impedance population is the initial state of a Markov chain. Based on the method, for any one of the K facies categories, the initial longitudinal wave impedance population under any one facies category can be subjected to population update through a particle swarm algorithm to obtain an updated longitudinal wave impedance population under any one facies category, and the updated longitudinal wave impedance population under any one facies category is added into the longitudinal wave impedance population set under any one facies category so as to realize state update of NP Markov chains, wherein one longitudinal wave impedance individual corresponds to one Markov chain. Further, the current longitudinal wave impedance inversion result under any lithofacies category can be determined based on the updated longitudinal wave impedance population under any lithofacies category, and whether the longitudinal wave impedance inversion result under any lithofacies category tends to be stable or not is judged based on the current longitudinal wave impedance inversion result; and when the longitudinal wave impedance inversion result under any lithofacies category does not tend to be stable, continuing to update the population of the updated longitudinal wave impedance population under any lithofacies category until the longitudinal wave impedance inversion result under any lithofacies identification tends to be stable, so as to determine the target longitudinal wave impedance population set under any lithofacies category from the longitudinal wave impedance population set under any lithofacies category. Correspondingly, the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category can be determined based on the target longitudinal wave impedance population set under each lithofacies category respectively, and the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category is used for determining the target longitudinal wave impedance and/or the target lithofacies identification result corresponding to the target post-stack seismic data. Therefore, the embodiment of the invention provides a seismic inversion method combining a particle swarm optimization algorithm (namely a particle swarm algorithm) and a Monte Carlo model, which can effectively solve the problems of lower convergence efficiency, lower calculation efficiency, easy sinking into a local mechanism, longer time consumption and the like of the related technology; that is, the embodiment of the invention can improve the calculation efficiency, convergence accuracy and the like of the seismic probabilistic inversion (namely, the seismic inversion), and can realize the stable and efficient prediction of parameters such as longitudinal wave impedance of the oil and gas reservoir, thereby focusing on the problem of the seismic probabilistic inversion in the complex oil and gas reservoir.

Based on the description of the related embodiments of the seismic inversion method, the embodiments of the present invention also provide a seismic inversion apparatus, which may be a computer program (including program code) running in an electronic device; as shown in fig. 9, the seismic inversion apparatus may include an acquisition unit 901 and a processing unit 902. The seismic inversion apparatus may perform the seismic inversion method shown in fig. 1 or fig. 2, that is, the seismic inversion apparatus may operate the above units:

an obtaining unit 901, configured to obtain target post-stack seismic data, and obtain target prior probability density distribution of each of K facies categories, where one target prior probability density distribution is used to indicate prior probability density distribution obeying longitudinal wave impedance of the target post-stack seismic data under a corresponding facies category, and the target post-stack seismic data includes seismic sampling values of each of N sampling points, where K and N are positive integers;

The processing unit 902 is configured to generate an initial longitudinal wave impedance population under each lithofacies category based on the target prior probability density distribution of each lithofacies category, where one longitudinal wave impedance population includes longitudinal wave impedances of each longitudinal wave impedance individual in NP longitudinal wave impedance individuals, and the longitudinal wave impedance of one longitudinal wave impedance individual in the initial longitudinal wave impedance population is obtained by sampling based on the target prior probability density distribution, and NP is an integer greater than 1;

the processing unit 902 is further configured to determine a set of target longitudinal wave impedance populations under each of the lithofacies categories based on the initial longitudinal wave impedance populations under each of the lithofacies categories, respectively;

The processing unit 902 is further configured to determine a longitudinal wave impedance of the target post-stack seismic data under each lithofacies category based on the target longitudinal wave impedance population set under each lithofacies category, where the longitudinal wave impedance support of the target post-stack seismic data under each lithofacies category is used to determine a target longitudinal wave impedance and/or a target lithofacies recognition result corresponding to the target post-stack seismic data.

In one embodiment, the target lithofacies recognition result includes lithofacies recognition results of the respective sampling points, and one longitudinal wave impedance includes a longitudinal wave impedance value of the respective sampling points, the processing unit 902 is further configured to: traversing each sampling point in the N sampling points, and taking the currently traversed sampling point as a current sampling point; determining the simulated longitudinal wave impedance value of the current sampling point under each lithofacies category from the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category, and determining the reference longitudinal wave impedance value of the current sampling point under each lithofacies category from the reference longitudinal wave impedance of the current sampling point under each lithofacies category; respectively carrying out difference value operation on the analog longitudinal wave impedance value and the reference longitudinal wave impedance value of the current sampling point under each lithofacies category to obtain a longitudinal wave impedance difference value operation result of the current sampling point under each lithofacies category, and taking the lithofacies category corresponding to the minimum value in the longitudinal wave impedance difference value operation result of the current sampling point under each lithofacies category as a lithofacies identification result of the current sampling point; and after traversing each sampling point in the N sampling points, obtaining the target lithology recognition result.

In another embodiment, the target longitudinal wave impedance includes a target longitudinal wave impedance value of each sampling point, and the processing unit 902 is further configured to: and taking the simulated longitudinal wave impedance value corresponding to the minimum value in the longitudinal wave impedance difference value operation result of the current sampling point under each lithofacies category as the target longitudinal wave impedance value of the current sampling point so as to obtain the target longitudinal wave impedance after traversing each sampling point in the N sampling points.

In another embodiment, when the longitudinal wave impedance of one longitudinal wave impedance individual in one initial longitudinal wave impedance population is the initial state of one markov chain, the processing unit 902 may be specifically configured to: for any one of the K facies categories, carrying out population update on an initial longitudinal wave impedance population under the any one facies category through a particle swarm algorithm to obtain an updated longitudinal wave impedance population under the any one facies category, and adding the updated longitudinal wave impedance population under the any one facies category into a longitudinal wave impedance population set under the any one facies category to realize state update of NP Markov chains, wherein one longitudinal wave impedance individual corresponds to one Markov chain; determining a current longitudinal wave impedance inversion result under any lithofacies category based on the updated longitudinal wave impedance population under the any lithofacies category, and judging whether the longitudinal wave impedance inversion result under the any lithofacies category tends to be stable or not based on the current longitudinal wave impedance inversion result; and when the longitudinal wave impedance inversion result under any lithofacies category does not tend to be stable, continuing to update the population of the updated longitudinal wave impedance population under any lithofacies category until the longitudinal wave impedance inversion result under any lithofacies identification tends to be stable, so as to determine the target longitudinal wave impedance population set under any lithofacies category from the longitudinal wave impedance population set under any lithofacies category.

In another embodiment, the longitudinal wave impedance population set under any lithofacies category includes a longitudinal wave impedance population of the any lithofacies category under each iteration of T iterations, where the t+1th longitudinal wave impedance population in the T iterations is obtained by population updating the longitudinal wave impedance population under the T iterations, T is an integer greater than 1, and T e [1, T-1]; the processing unit 902 is further configured to determine a longitudinal wave impedance population of any lithology category at the t+1st iteration, where the determining method of the longitudinal wave impedance population of any lithology category at the t+1st iteration includes: regarding the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category under the t-th iteration and the nth sampling point in the N sampling points, taking the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category under the t-th iteration as the ith longitudinal wave impedance under the t-th iteration, i epsilon [1, NP ], N epsilon [1, N ]; determining candidate longitudinal wave impedance values of the nth sampling point based on the longitudinal wave impedance values of the nth sampling point in the ith longitudinal wave impedance under the nth iteration through a particle swarm algorithm to obtain candidate longitudinal wave impedance, wherein the candidate longitudinal wave impedance comprises candidate longitudinal wave impedance values of all the sampling points; calculating the receiving probability of the candidate longitudinal wave impedance based on the ith longitudinal wave impedance, the candidate longitudinal wave impedance and the target post-stack seismic data under the t-th iteration, and judging whether to receive the candidate longitudinal wave impedance based on the receiving probability of the candidate longitudinal wave impedance; if the candidate longitudinal wave impedance is determined to be received, the candidate longitudinal wave impedance is used as the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category under the t+1th iteration; and if the candidate longitudinal wave impedance is not received, taking the ith longitudinal wave impedance in the t iteration as the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category in the t+1th iteration to realize the determination of the longitudinal wave impedance population of any lithofacies category in the t+1th iteration.

In another embodiment, the processing unit 902, when calculating the reception probability of the candidate longitudinal wave impedance based on the ith longitudinal wave impedance at the t-th iteration, the candidate longitudinal wave impedance, and the target post-stack seismic data, may be specifically configured to: determining a longitudinal wave impedance mean value and a longitudinal wave impedance variance of each sampling point under any lithofacies category; and calculating the receiving probability of the candidate longitudinal wave impedance based on the ith longitudinal wave impedance, the candidate longitudinal wave impedance, the target post-stack seismic data and the longitudinal wave impedance mean value and the longitudinal wave impedance variance of each sampling point under any lithology category.

In another embodiment, when determining the longitudinal wave impedance of the target post-stack seismic data under the respective facies categories based on the target set of longitudinal wave impedance groups under the respective facies categories, the processing unit 902 may be specifically configured to: and carrying out mean value operation on each longitudinal wave impedance in the target longitudinal wave impedance population set under any one of the K lithofacies categories to obtain the longitudinal wave impedance of the target post-stack seismic data under any one of the lithofacies categories.

According to one embodiment of the present invention, each unit in the seismic inversion apparatus shown in fig. 9 may be formed by combining one or several additional units separately or all, or some unit(s) thereof may be formed by splitting a plurality of units having smaller functions, which may achieve the same operation without affecting the implementation of the technical effects of the embodiments of the present invention. The above units are divided based on logic functions, and in practical applications, the functions of one unit may be implemented by a plurality of units, or the functions of a plurality of units may be implemented by one unit. In other embodiments of the invention, any of the seismic inversion apparatus may also include other units, and in actual practice, these functions may be facilitated by other units and may be cooperatively implemented by a plurality of units.

According to another embodiment of the present invention, the seismic inversion apparatus as shown in fig. 9 may be constructed by running a computer program (including program code) capable of executing the steps involved in the respective methods as shown in fig. 1 or 2 on a general-purpose electronic device such as a computer including a processing element such as a Central Processing Unit (CPU), a random access storage medium (RAM), a read only storage medium (ROM), and the like, and a storage element, and the seismic inversion method of the embodiments of the present invention may be implemented. The computer program may be recorded on, for example, a computer storage medium, and loaded into and run in the above-described electronic device through the computer storage medium.

Based on the description of the method embodiment and the apparatus embodiment, the exemplary embodiment of the present invention further provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores a computer program executable by the at least one processor for causing the electronic device to perform a method according to an embodiment of the invention when executed by the at least one processor.

The exemplary embodiments of the present invention also provide a non-transitory computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is for causing the computer to perform a method according to an embodiment of the present invention.

The exemplary embodiments of the invention also provide a computer program product comprising a computer program, wherein the computer program, when being executed by a processor of a computer, is for causing the computer to perform a method according to an embodiment of the invention.

Referring to fig. 10, a block diagram of an electronic device 1000 that may be a server or a client of the present invention will now be described, which is an example of a hardware device that may be applied to aspects of the present invention. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 10, the electronic device 1000 includes a computing unit 1001 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a Random Access Memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the electronic apparatus 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other by a bus 1004. An input/output (I/O) interface 1005 is also connected to bus 1004.

Various components in the electronic device 1000 are connected to the I/O interface 1005, including: an input unit 1006, an output unit 1007, a storage unit 1008, and a communication unit 1009. The input unit 1006 may be any type of device capable of inputting information to the electronic device 1000, and the input unit 1006 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device. The output unit 1007 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage unit 1008 may include, but is not limited to, magnetic disks, optical disks. Communication unit 1009 allows electronic device 1000 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.

The computing unit 1001 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 1001 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1001 performs the respective methods and processes described above. For example, in some embodiments, the seismic inversion method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 1008. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 1000 via the ROM 1002 and/or the communication unit 1009. In some embodiments, the computing unit 1001 may be configured to perform the seismic inversion method by any other suitable means (e.g., by means of firmware).

Program code for carrying out methods of the present invention may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

It is also to be understood that the foregoing is merely illustrative of the present invention and is not to be construed as limiting the scope of the invention, which is defined by the appended claims.

Claims

1. A method of seismic inversion, comprising:

Generating initial longitudinal wave impedance groups under each lithofacies category based on target prior probability density distribution of each lithofacies category respectively, wherein one longitudinal wave impedance group comprises longitudinal wave impedance of each longitudinal wave impedance individual in NP longitudinal wave impedance individuals, the longitudinal wave impedance of each longitudinal wave impedance individual in the initial longitudinal wave impedance group is obtained by sampling based on the target prior probability density distribution, and NP is an integer greater than 1; the longitudinal wave impedance of a longitudinal wave impedance individual in an initial longitudinal wave impedance population is the initial state of a Markov chain;

Determining a set of target longitudinal wave impedance populations under each lithofacies category based on the initial longitudinal wave impedance populations under each lithofacies category, respectively, comprising: for any one of the K facies categories, carrying out population update on an initial longitudinal wave impedance population under the any one facies category through a particle swarm algorithm to obtain an updated longitudinal wave impedance population under the any one facies category, and adding the updated longitudinal wave impedance population under the any one facies category into a longitudinal wave impedance population set under the any one facies category to realize state update of NP Markov chains, wherein one longitudinal wave impedance individual corresponds to one Markov chain; determining a current longitudinal wave impedance inversion result under any lithofacies category based on the updated longitudinal wave impedance population under the any lithofacies category, and judging whether the longitudinal wave impedance inversion result under the any lithofacies category tends to be stable or not based on the current longitudinal wave impedance inversion result; when the longitudinal wave impedance inversion result under any lithofacies category does not tend to be stable, continuing to update the population of the updated longitudinal wave impedance population under any lithofacies category until the longitudinal wave impedance inversion result under any lithofacies identification tends to be stable, so as to determine a target longitudinal wave impedance population set under any lithofacies category from the longitudinal wave impedance population set under any lithofacies category;

Determining longitudinal wave impedance of the target post-stack seismic data under each lithofacies category based on the target longitudinal wave impedance population set under each lithofacies category, wherein the longitudinal wave impedance support of the target post-stack seismic data under each lithofacies category is used for determining a target longitudinal wave impedance and/or a target lithofacies identification result corresponding to the target post-stack seismic data;

the longitudinal wave impedance population set under any lithofacies category comprises longitudinal wave impedance populations of the lithofacies category under each iteration in T iterations, wherein the longitudinal wave impedance populations under the t+1th iteration in the T iterations are obtained by carrying out population updating on the longitudinal wave impedance populations under the T iterations, T is an integer greater than 1, and T epsilon [1, T-1]; the determination mode of the longitudinal wave impedance population of any lithofacies category under the t+1th iteration comprises the following steps:

Regarding the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category under the t-th iteration and the nth sampling point in the N sampling points, taking the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category under the t-th iteration as the ith longitudinal wave impedance under the t-th iteration, i epsilon [1, NP ], N epsilon [1, N ];

Determining candidate longitudinal wave impedance values of the nth sampling point based on the longitudinal wave impedance values of the nth sampling point in the ith longitudinal wave impedance under the nth iteration through a particle swarm algorithm to obtain candidate longitudinal wave impedance, wherein the candidate longitudinal wave impedance comprises candidate longitudinal wave impedance values of all the sampling points;

Calculating the receiving probability of the candidate longitudinal wave impedance based on the ith longitudinal wave impedance, the candidate longitudinal wave impedance and the target post-stack seismic data under the t-th iteration, and judging whether to receive the candidate longitudinal wave impedance based on the receiving probability of the candidate longitudinal wave impedance;

If the candidate longitudinal wave impedance is determined to be received, the candidate longitudinal wave impedance is used as the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category under the t+1th iteration;

If the candidate longitudinal wave impedance is not received, the ith longitudinal wave impedance in the t iteration is used as the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category in the t+1th iteration, so that the longitudinal wave impedance population of any lithofacies category in the t+1th iteration is determined;

The candidate longitudinal wave impedance value of the nth sampling point is determined based on the longitudinal wave impedance value of the nth sampling point in the ith longitudinal wave impedance of the nth iteration and the speed of the nth sampling point in the ith longitudinal wave impedance of the (t+1) th iteration of any lithofacies category; the speed of the nth sampling point in the ith longitudinal wave impedance at the t+1th iteration is determined based on the longitudinal wave impedance value of the nth sampling point in the optimal longitudinal wave impedance of the ith longitudinal wave impedance individual at the any lithofacies classification, the longitudinal wave impedance value of the nth sampling point included in the optimal longitudinal wave impedance of the any of the plurality of lithofacies classification in the previous t iterations, and the longitudinal wave impedance value of the nth sampling point in the ith longitudinal wave impedance at the t iteration, the optimal longitudinal wave impedance of the ith longitudinal wave impedance individual and the optimal longitudinal wave impedance of the any of the plurality of longitudinal wave impedance individuals being determined based on the evaluation value of the any of the plurality of lithofacies classification in each of the previous t iterations; the evaluation value of the longitudinal wave impedance under any one of the lithology categories is determined by a target evaluation function, and when the any one of the lithology categories is the kth lithology category of the K lithology categories, the target evaluation function is: a target functional equivalent to the kth component of the posterior probability density distribution of longitudinal wave impedance, k e [1, k ].

2. The method of claim 1, wherein the target lithofacies recognition result comprises lithofacies recognition results for the respective sampling points, and one longitudinal wave impedance comprises a longitudinal wave impedance value for the respective sampling points, the method further comprising:

Traversing each sampling point in the N sampling points, and taking the currently traversed sampling point as a current sampling point;

Determining the simulated longitudinal wave impedance value of the current sampling point under each lithofacies category from the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category, and determining the reference longitudinal wave impedance value of the current sampling point under each lithofacies category from the reference longitudinal wave impedance of the current sampling point under each lithofacies category;

Respectively carrying out difference value operation on the analog longitudinal wave impedance value and the reference longitudinal wave impedance value of the current sampling point under each lithofacies category to obtain a longitudinal wave impedance difference value operation result of the current sampling point under each lithofacies category, and taking the lithofacies category corresponding to the minimum value in the longitudinal wave impedance difference value operation result of the current sampling point under each lithofacies category as a lithofacies identification result of the current sampling point;

and after traversing each sampling point in the N sampling points, obtaining the target lithology recognition result.

3. The method of claim 2, wherein the target longitudinal wave impedance comprises a target longitudinal wave impedance value for the respective sampling point, the method further comprising:

And taking the simulated longitudinal wave impedance value corresponding to the minimum value in the longitudinal wave impedance difference value operation result of the current sampling point under each lithofacies category as the target longitudinal wave impedance value of the current sampling point so as to obtain the target longitudinal wave impedance after traversing each sampling point in the N sampling points.

4. A method according to any one of claims 1-3, wherein said calculating a reception probability of the candidate longitudinal wave impedance based on the ith longitudinal wave impedance at the t-th iteration, the candidate longitudinal wave impedance, and the target post-stack seismic data comprises:

determining a longitudinal wave impedance mean value and a longitudinal wave impedance variance of each sampling point under any lithofacies category;

And calculating the receiving probability of the candidate longitudinal wave impedance based on the ith longitudinal wave impedance, the candidate longitudinal wave impedance, the target post-stack seismic data and the longitudinal wave impedance mean value and the longitudinal wave impedance variance of each sampling point under any lithology category.

5. A method according to any one of claims 1-3, wherein said determining the longitudinal wave impedance of the target post-stack seismic data under the respective facies categories based on the respective sets of target longitudinal wave impedance populations under the respective facies categories, comprises:

And carrying out mean value operation on each longitudinal wave impedance in the target longitudinal wave impedance population set under any one of the K lithofacies categories to obtain the longitudinal wave impedance of the target post-stack seismic data under any one of the lithofacies categories.

6. A seismic inversion apparatus, the apparatus comprising:

The processing unit is used for generating initial longitudinal wave impedance groups under each lithofacies category based on the target prior probability density distribution of each lithofacies category respectively, one longitudinal wave impedance group comprises longitudinal wave impedance of each longitudinal wave impedance individual in NP longitudinal wave impedance individuals, the longitudinal wave impedance of one longitudinal wave impedance individual in the initial longitudinal wave impedance group is obtained by sampling based on the target prior probability density distribution, and NP is an integer greater than 1; the longitudinal wave impedance of a longitudinal wave impedance individual in an initial longitudinal wave impedance population is the initial state of a Markov chain;

The processing unit is further configured to determine a target population of longitudinal wave impedances under each lithofacies category based on the initial population of longitudinal wave impedances under each lithofacies category, respectively, and includes: for any one of the K facies categories, carrying out population update on an initial longitudinal wave impedance population under the any one facies category through a particle swarm algorithm to obtain an updated longitudinal wave impedance population under the any one facies category, and adding the updated longitudinal wave impedance population under the any one facies category into a longitudinal wave impedance population set under the any one facies category to realize state update of NP Markov chains, wherein one longitudinal wave impedance individual corresponds to one Markov chain; determining a current longitudinal wave impedance inversion result under any lithofacies category based on the updated longitudinal wave impedance population under the any lithofacies category, and judging whether the longitudinal wave impedance inversion result under the any lithofacies category tends to be stable or not based on the current longitudinal wave impedance inversion result; when the longitudinal wave impedance inversion result under any lithofacies category does not tend to be stable, continuing to update the population of the updated longitudinal wave impedance population under any lithofacies category until the longitudinal wave impedance inversion result under any lithofacies identification tends to be stable, so as to determine a target longitudinal wave impedance population set under any lithofacies category from the longitudinal wave impedance population set under any lithofacies category;

The processing unit is further configured to determine a longitudinal wave impedance of the target post-stack seismic data under each lithofacies category based on the target longitudinal wave impedance population set under each lithofacies category, where the longitudinal wave impedance support of the target post-stack seismic data under each lithofacies category is used for determining a target longitudinal wave impedance and/or a target lithofacies recognition result corresponding to the target post-stack seismic data; the longitudinal wave impedance population set under any lithofacies category comprises longitudinal wave impedance populations of the lithofacies category under each iteration in T iterations, wherein the longitudinal wave impedance populations under the t+1th iteration in the T iterations are obtained by carrying out population updating on the longitudinal wave impedance populations under the T iterations, T is an integer greater than 1, and T epsilon [1, T-1]; the determination mode of the longitudinal wave impedance population of any lithofacies category under the t+1th iteration comprises the following steps: regarding the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category under the t-th iteration and the nth sampling point in the N sampling points, taking the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category under the t-th iteration as the ith longitudinal wave impedance under the t-th iteration, i epsilon [1, NP ], N epsilon [1, N ]; determining candidate longitudinal wave impedance values of the nth sampling point based on the longitudinal wave impedance values of the nth sampling point in the ith longitudinal wave impedance under the nth iteration through a particle swarm algorithm to obtain candidate longitudinal wave impedance, wherein the candidate longitudinal wave impedance comprises candidate longitudinal wave impedance values of all the sampling points; calculating the receiving probability of the candidate longitudinal wave impedance based on the ith longitudinal wave impedance, the candidate longitudinal wave impedance and the target post-stack seismic data under the t-th iteration, and judging whether to receive the candidate longitudinal wave impedance based on the receiving probability of the candidate longitudinal wave impedance; if the candidate longitudinal wave impedance is determined to be received, the candidate longitudinal wave impedance is used as the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category under the t+1th iteration; if the candidate longitudinal wave impedance is not received, the ith longitudinal wave impedance in the t iteration is used as the ith longitudinal wave impedance in the longitudinal wave impedance population of any lithofacies category in the t+1th iteration, so that the longitudinal wave impedance population of any lithofacies category in the t+1th iteration is determined;

7. An electronic device, comprising:

A processor; and

A memory in which a program is stored,

Wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the method according to any of claims 1-5.

8. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-5.