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

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, device, storage medium and electronic equipment

Technical Field

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

Background Art

At present, facing the increasingly complex oil and gas reservoirs, geophysical researchers have put forward higher requirements for the accuracy of seismic inversion, which has attracted widespread attention from researchers for probabilistic seismic inversion based on statistics and random sampling; however, related technologies are prone to fall into local mechanisms and have low convergence accuracy. Based on this, there is currently no good solution for how to improve the convergence accuracy of seismic inversion while avoiding falling into local mechanisms.

Summary of the invention

In view of this, the embodiments of the present invention provide a seismic inversion method, device, storage medium and electronic device to solve the problems that the related technologies are prone to fall into local mechanisms and have low convergence accuracy. That is to say, the embodiments of the present invention can avoid falling into local mechanisms through the longitudinal wave impedance population under each lithology category, which can effectively avoid falling into local optimality and effectively improve the convergence accuracy of seismic inversion. Based on this, the embodiments of the present invention can improve the convergence accuracy of seismic inversion while avoiding falling into local mechanisms.

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

Obtain target post-stack seismic data, and obtain target prior probability density distribution of each lithofacies category in K lithofacies categories, wherein a target prior probability density distribution is used to indicate the prior probability density distribution of the longitudinal wave impedance of the target post-stack seismic data in the corresponding lithofacies category, and the target post-stack seismic data includes seismic sampling values of each sampling point in N sampling points, where K and N are both positive integers;

Based on the target prior probability density distribution of each lithofacies category, an initial P-wave impedance population under each lithofacies category is generated, wherein one P-wave impedance population includes the P-wave impedance of each P-wave impedance individual in NP P-wave impedance individuals, and the P-wave impedance of one P-wave impedance individual in an initial P-wave impedance population is obtained by sampling based on a 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;

The longitudinal wave impedance of the target post-stack seismic data under each lithofacies category is determined based on the target longitudinal wave impedance population set under each lithofacies category, and the longitudinal wave impedance support of the target post-stack seismic data under each lithofacies category is used to determine the target longitudinal wave impedance and/or target lithofacies identification result corresponding to the target post-stack seismic data.

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

an acquisition unit, used for acquiring target post-stack seismic data and acquiring a target priori probability density distribution of each lithofacies category in K lithofacies categories, wherein a target priori probability density distribution is used for indicating a priori probability density distribution obeyed by the longitudinal wave impedance of the target post-stack seismic data in the corresponding lithofacies category, wherein the target post-stack seismic data includes seismic sampling values of each sampling point in N sampling points, and K and N are both positive integers;

A processing unit is used to generate an initial P-wave impedance population under each lithofacies category based on a target prior probability density distribution of each lithofacies category, wherein one P-wave impedance population includes the P-wave impedance of each P-wave impedance individual in NP P-wave impedance individuals, and the P-wave impedance of one P-wave impedance individual in an initial P-wave impedance population is obtained by sampling based on a target prior probability density distribution, and NP is an integer greater than 1;

The processing unit is further used to determine the target longitudinal wave impedance population set under each lithofacies category based on the initial longitudinal wave impedance population under each lithofacies category;

The processing unit is also used to determine the longitudinal wave impedance of the target post-stack seismic data in each lithofacies category based on the target longitudinal wave impedance population set in each lithofacies category. The longitudinal wave impedance support of the target post-stack seismic data in each lithofacies category is used to determine the target longitudinal wave impedance and/or target lithofacies identification result corresponding to the target post-stack seismic data.

According to another aspect of an embodiment of the present invention, an electronic device is provided, which includes a processor and a memory storing a program, wherein the program includes instructions, and when the instructions are executed by the processor, the processor executes the above-mentioned method.

According to another aspect of an embodiment of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the above-mentioned method.

According to another aspect of an embodiment of the present invention, a computer program product is provided, comprising a computer program, wherein the computer program is used to enable a computer to perform the above-mentioned method when executed by a processor.

The embodiment of the present invention can generate an initial P-wave impedance population under each lithofacies category based on the target prior probability density distribution of each lithofacies category after acquiring the target post-stack seismic data and the target prior probability density distribution of each lithofacies category in K lithofacies categories, wherein a target prior probability density distribution is used to indicate the prior probability density distribution that the P-wave impedance of the target post-stack seismic data under the corresponding lithofacies category obeys, the target post-stack seismic data includes the seismic sampling value of each sampling point in N sampling points, a P-wave impedance population includes the P-wave impedance of each P-wave impedance individual in NP P-wave impedance individuals, and the P-wave impedance of a P-wave impedance individual in an initial P-wave impedance population is obtained by sampling based on a target prior probability density distribution, K and N are both positive integers, and NP is an integer greater than 1. Further, the target P-wave impedance population set under each lithofacies category can be determined based on the initial P-wave impedance population under each lithofacies category; and the P-wave impedance of the target post-stack seismic data under each lithofacies category can be determined based on the target P-wave impedance population set under each lithofacies category, and the P-wave impedance support of the target post-stack seismic data under each lithofacies category is used to determine the target P-wave impedance and/or target lithofacies identification result corresponding to the target post-stack seismic data. It can be seen that the embodiment of the present invention can avoid falling into a local mechanism through the P-wave impedance population under each lithofacies category, which can effectively avoid falling into a local optimum and effectively improve the convergence accuracy of seismic inversion; that is, the embodiment of the present invention can improve the convergence accuracy of seismic inversion while avoiding falling into a local mechanism.

BRIEF DESCRIPTION OF THE DRAWINGS

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

FIG1 shows a schematic flow chart of a seismic inversion method according to an exemplary embodiment of the present invention;

FIG2 shows a schematic flow chart of another seismic inversion method according to an exemplary embodiment of the present invention;

FIG3 is a schematic diagram showing an inversion result according to an exemplary embodiment of the present invention;

FIG4 is a schematic diagram showing another inversion result according to an exemplary embodiment of the present invention;

FIG5 is a schematic diagram showing another inversion result according to an exemplary embodiment of the present invention;

FIG6 is a schematic diagram showing another inversion result according to an exemplary embodiment of the present invention;

FIG7 shows a schematic diagram of a running time according to an exemplary embodiment of the present invention;

FIG8 shows a schematic diagram of a seismic profile inversion result according to an exemplary embodiment of the present invention; wherein FIG8 (a) shows a schematic diagram of an actual post-stack seismic profile according to an exemplary embodiment of the present invention, FIG8 (b) shows a schematic diagram of a low-frequency background of a longitudinal wave impedance according to an exemplary embodiment of the present invention, FIG8 (c) shows a schematic diagram of a longitudinal wave impedance according to an exemplary embodiment of the present invention, and FIG8 (d) shows a schematic diagram of a lithofacies identification result according to an exemplary embodiment of the present invention;

FIG9 shows a schematic block diagram of a seismic inversion device according to an exemplary embodiment of the present invention;

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

DETAILED DESCRIPTION

Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and/or in parallel. In addition, the method embodiments may include additional steps and/or omit the steps shown. The scope of the present invention is not limited in this respect.

The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

It should be noted that the execution subject of the seismic inversion method provided in the embodiment of the present invention may be one or more electronic devices, which is not limited by the present invention; wherein, the electronic device may be a terminal (i.e., a client) or a server, then when the execution subject includes multiple electronic devices, and the multiple electronic devices include at least one terminal and at least one server, the seismic inversion method provided in the embodiment of the present invention may be jointly executed by the terminal and the server. Accordingly, the terminal mentioned here may include but is not limited to: smart phones, laptops, desktop computers, intelligent voice interaction devices, and the like. The server mentioned here may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing (cloud computing), cloud functions, cloud storage, network services, cloud communications, 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 the like.

Based on the above description, an embodiment of the present invention proposes a seismic inversion method, which can be executed by the electronic device (terminal or server) mentioned above; or, the seismic inversion method can be executed by the terminal and the server together. For the convenience of explanation, the following description is taken as an example of an electronic device executing the seismic inversion method; as shown in FIG1 , the seismic inversion method may include the following steps S101-S104:

S101, obtaining target post-stack seismic data, and obtaining target prior probability density distribution of each lithofacies category in K lithofacies categories, a target prior probability density distribution is used to indicate the prior probability density distribution of the longitudinal wave impedance of the target post-stack seismic data under the corresponding lithofacies category, the target post-stack seismic data includes the seismic sampling value of each sampling point in N sampling points, and K and N are both positive integers.

Optionally, the K lithofacies categories may include but are not limited to mudstone and sandstone, etc., which are not limited in the embodiment of the present invention. Optionally, the data involved in the embodiment of the present invention may be data in the time domain, etc.; accordingly, a sampling point may be a time sampling point, that is, a sampling 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 a seismic trace), which is not limited in the embodiment of the present invention. Optionally, the number of 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 post-stack seismic data in multiple post-stack seismic data in the target area may be used as the target post-stack seismic data, so that seismic inversion is performed on each target post-stack seismic data in multiple target post-stack seismic data in the target area, so as to obtain the target P-wave impedance and/or target lithofacies identification result corresponding to each target post-stack seismic data, so as to splice the target P-wave impedance and/or target lithofacies identification result corresponding to each target post-stack seismic data, so as to obtain the seismic inversion result of the target area (i.e., the splicing result of the target P-wave impedance corresponding to each target post-stack seismic data and/or the splicing result of the target lithofacies identification result corresponding to each target post-stack seismic data), so as to realize the seismic inversion of the target area; Optionally, the target area may be any area, which is not limited in the embodiment of the present invention. When multiple target post-stack seismic data are located in the same section, the seismic inversion result of the target area can also be called the seismic section inversion result of the target area.

It should be noted that, under the framework of hierarchical Bayesian inference, the prior probability density distribution of P-wave impedance can be effectively introduced into seismic inversion. Since the statistical characteristics of P-wave impedance in strata of different lithology (i.e., lithofacies categories) are different, it can be assumed that the P-wave impedance of the stratum (i.e., P-wave impedance) obeys a mixed probability density distribution; wherein, the mixed probability density distribution obeyed by the P-wave impedance may include the probability density distribution obeyed by the P-wave impedance under each lithofacies category. Optionally, the P-wave impedance may obey a mixed Laplace probability density distribution, or a mixed Cauchy probability density distribution, etc., which is not limited in the embodiments of the present invention. For ease of explanation, the following description will be made by taking the P-wave impedance obeying a mixed Laplace probability density distribution as an example. Exemplarily, the mixed Laplace probability density distribution obeyed by the P-wave impedance may be as shown in Formula 1.1:

Formula 1.1

Where K is the number of lithofacies categories (also called the number of Laplace components, that is, one lithofacies category corresponds to one Laplace component, and the number of Laplace components is the same as the number of lithofacies categories); accordingly, p(m) can represent the prior probability density distribution of the longitudinal wave impedance, then each prior Laplace component can have a different prior mean and the prior covariance , m can represent the longitudinal wave impedance, represents the kth prior Laplace component among the K prior Laplace components (i.e., the kth Laplace prior probability density distribution, k∈[1,K]), and λ _k represents the prior proportion of the kth lithofacies category. Optionally, the prior mean, prior covariance, and prior proportion of a prior Laplace component can be set according to experience or according to actual needs, and the embodiments of the present invention do not limit this. For example, the prior mean, prior covariance, and prior proportion of a prior Laplace component can be obtained through well logging data (also referred to as well logging interpretation data); for another example, when the prior proportion cannot be accurately obtained, the prior proportion of a lithofacies category can be 0.5, and so on. It should be noted that a prior mean may include the mean of the longitudinal wave impedance of each sampling point (also referred to as the mean of each sampling point), that is, a prior mean may be an N-dimensional vector; accordingly, a prior covariance may include the covariance between any two sampling points, and may include the variance of the longitudinal wave impedance of any sampling point. In the embodiment of the present invention, a covariance may be a covariance matrix.

Based on this, a target prior probability density can be a prior Laplace component obeyed by the longitudinal wave impedance under the target post-stack seismic data. It should be noted that the prior mean and prior covariance in a target prior probability density distribution correspond to the sub-region where the target post-stack seismic data is located. For example, the prior mean and prior covariance in a target prior probability density distribution can be obtained through the 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 obtained in the following ways, but not limited to:

The first acquisition method: a plurality of post-stack seismic data may be stored in the storage space of the electronic device itself. In this case, any one of the plurality of post-stack seismic data may be used as target post-stack seismic data; or, when performing seismic inversion on the target area, each of the plurality of post-stack seismic data (i.e., multi-channel post-stack seismic data) in the target area may be used as a target post-stack seismic data.

The second acquisition method: the electronic device can obtain a seismic data download link, and use the seismic data downloaded based on the seismic data download link as the target post-stack seismic data.

The third acquisition method: the electronic device can be connected to multiple receivers. In this case, the electronic device can receive multi-channel seismic data through the multiple receivers, and acquire target post-stack seismic data based on the received multi-channel seismic data, such as performing noise elimination and other operations on the received multi-channel seismic data to obtain multiple post-stack seismic data, and select one post-stack seismic data from the multiple 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 multiple post-stack seismic data as the target post-stack seismic data, and so on.

Accordingly, the electronic device's own storage space may store a priori probability density distribution of the longitudinal wave impedance of each post-stack seismic data in each lithofacies category among multiple post-stack seismic data (such as storing a priori mean, a priori covariance, etc. of the prior probability density distribution of the longitudinal wave impedance of each post-stack seismic data in each lithofacies category), and the target post-stack seismic data is the post-stack seismic data determined from the multiple post-stack seismic data. In this case, the electronic device may obtain the priori probability density distribution of the longitudinal wave impedance of the target post-stack seismic data in each lithofacies category from its own storage space to achieve the acquisition of the target priori probability density distribution of each lithofacies category; or, the electronic device may obtain a probability density distribution download link, and download the target post-stack seismic data for each lithofacies category based on the probability density distribution download link. Other target prior probability density distributions; or, the electronic device's own storage space may store a prior probability density distribution of the longitudinal wave impedance of each sub-region in each lithofacies category among multiple sub-regions, then the electronic device may determine the sub-region where the target post-stack seismic data is located, and use the prior probability density distribution of the longitudinal wave impedance of the sub-region where the target post-stack seismic data is located in each lithofacies category as the prior probability density distribution of the longitudinal wave impedance of the target post-stack seismic data in each lithofacies category, so as to achieve the acquisition of the target prior probability density distribution of each lithofacies category; or, the electronic device may also obtain the logging data of the sub-region where the target post-stack seismic data is located, and obtain the target prior probability density distribution of each lithofacies category based on the logging data, and so on; the embodiments of the present invention are not limited to this.

S102, based on the target prior probability density distribution of each lithofacies category, generate an initial P-wave impedance population under each lithofacies category, wherein a P-wave impedance population includes the P-wave impedance of each P-wave impedance individual among NP P-wave impedance individuals, and the P-wave impedance of a P-wave impedance individual in an initial P-wave impedance population is obtained by sampling based on a target prior probability density distribution, and NP is an integer greater than 1.

Specifically, for any of the K lithofacies categories, the electronic device may generate an initial P-wave impedance population under any lithofacies category based on the target prior probability density distribution of any lithofacies category. It should be understood that any lithofacies category may correspond to NP P-wave impedance individuals in one simulation process.

Optionally, for any longitudinal wave impedance individual under any lithofacies category, the electronic device can perform sampling through the target prior probability density distribution of any lithofacies category to obtain the longitudinal wave impedance of any longitudinal wave impedance individual in the initial longitudinal wave impedance population under any lithofacies category. The longitudinal wave impedance of a longitudinal wave impedance individual in a longitudinal wave impedance population under a lithofacies category can also be called the longitudinal wave impedance of the corresponding longitudinal wave impedance individual under the corresponding lithofacies category in the corresponding longitudinal wave impedance population; that is, the electronic device can perform NP times of sampling through the target prior probability density distribution of any lithofacies category to obtain the longitudinal wave impedance of each longitudinal wave impedance individual in the NP longitudinal wave impedance individuals under any lithofacies category, thereby obtaining the initial longitudinal wave impedance population under any lithofacies category, that is, a longitudinal wave impedance in the initial longitudinal wave impedance population under any lithofacies category is the initial longitudinal wave impedance of a 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 by sampling through the target prior probability density distribution of any lithofacies category.

Based on this, NP Markov chains (also called Markov chains) under any lithofacies category can be obtained. The initial state of a Markov chain under any lithofacies category can be the P-wave impedance of a P-wave impedance individual in the initial P-wave impedance population under any lithofacies category, and the dimension of a P-wave impedance can be N, that is, a P-wave impedance can be an N-dimensional vector (including the P-wave impedance value of each sampling point among the N sampling points). It can be seen that under different lithofacies categories, the initial state of each Markov chain can be sampled from the target prior probability density distribution of the corresponding lithofacies category, that is, the initial state of each Markov chain under any lithofacies category can be sampled from the target prior probability density distribution of any lithofacies category.

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

In an embodiment of the present invention, for any of the K lithofacies categories, the electronic device can iterate each Markov chain under any lithofacies category respectively, so as to iterate each longitudinal wave impedance individual under any lithofacies category to update the longitudinal wave impedance of each longitudinal wave impedance individual under any lithofacies category, thereby realizing population update of the longitudinal wave impedance population under any lithofacies category, and further determining the target longitudinal wave impedance population set under any lithofacies category.

Optionally, the electronic device can iterate each Markov chain under any lithofacies category at the same time, that is, each P-wave impedance in the current P-wave impedance population under any lithofacies category (i.e., the P-wave impedance population under the tth iteration of any lithofacies category) can be iterated in parallel (i.e., each P-wave impedance individual under the current iteration of any lithofacies category can be iterated in parallel) to obtain the next P-wave impedance population of the current P-wave impedance population under any lithofacies category (i.e., the P-wave impedance population under the t+1th iteration of any lithofacies category); based on this, the embodiment of the present invention can effectively improve the convergence efficiency and the computational efficiency of seismic inversion. Wherein, t∈[1, T-1], T is the total number of iterations when the iteration is stopped, and T is an integer greater than 1.

S104, determining the P-wave impedance of the target post-stack seismic data in each lithofacies category based on the target P-wave impedance population set in each lithofacies category, wherein the P-wave impedance support of the target post-stack seismic data in each lithofacies category is used to determine the target P-wave impedance and/or target lithofacies identification result corresponding to the target post-stack seismic data.

Among them, the longitudinal wave impedance of the target post-stack seismic data in each lithofacies category, the target longitudinal wave impedance corresponding to the target post-stack seismic data and/or the target lithofacies identification result can all be the inversion results of the target post-stack seismic data. That is to say, the inversion results of the target post-stack seismic data may include at least one of the following: the longitudinal wave impedance of the target post-stack seismic data in each lithofacies category, the target longitudinal wave impedance and the target lithofacies identification result.

In one embodiment, the longitudinal wave impedance of the target post-stack seismic data under each lithofacies category can be used to determine the target lithofacies identification result corresponding to the target post-stack seismic data; wherein the target lithofacies identification result may include the lithofacies identification results of each sampling point (that is, it may include the lithofacies identification results of each sampling point under the target post-stack seismic data), and a longitudinal wave impedance includes the longitudinal wave impedance value of each sampling point (that is, it includes the longitudinal wave impedance value of the corresponding longitudinal wave impedance at each sampling point). Based on this, the electronic device can traverse each sampling point among the N sampling points, and use the currently traversed sampling point as the current sampling point; and respectively determine the simulated P-wave impedance value of the current sampling point under each lithofacies category from the P-wave impedance of the target post-stack seismic data under each lithofacies category, and respectively determine the reference P-wave impedance value of the current sampling point under each lithofacies category from the reference P-wave impedance under each lithofacies category, wherein a P-wave impedance under one lithofacies category may include the P-wave impedance value of each sampling point under the corresponding lithofacies category; further, the simulated P-wave impedance value and the reference P-wave impedance value of the current sampling point under each lithofacies category can be respectively subjected to difference calculation to obtain the P-wave impedance difference calculation result of the current sampling point under each lithofacies category, and the lithofacies category corresponding to the minimum value in the P-wave impedance difference calculation result of the current sampling point under each lithofacies category is used as the lithofacies identification result of the current sampling point; after traversing each sampling point among the N sampling points, the target lithofacies identification result is obtained. Optionally, the reference P-wave impedance under each lithofacies category may be set according to experience or according to actual needs, which is not limited in the embodiments of the present invention; illustratively, the reference P-wave impedance under any lithofacies category may be a prior mean in the target prior probability density distribution of any lithofacies category, or may be determined by the logging data of the sub-area where the target post-stack seismic data is located, and the logging data of a sub-area may include the logging data of each well in at least one well.

Specifically, for any of the K lithofacies categories, the electronic device can perform a difference operation on the simulated longitudinal wave impedance value and the reference longitudinal wave impedance value of the current sampling point under any lithofacies category to obtain a longitudinal wave impedance difference operation result of the current sampling point under any lithofacies category, thereby obtaining a longitudinal wave impedance difference operation result of the current sampling point under each lithofacies category. Among them, the lithofacies category corresponding to the longitudinal wave impedance difference operation result under a lithofacies category is the corresponding lithofacies category, that is, the longitudinal wave impedance difference operation result under any lithofacies category corresponds to any lithofacies category.

Exemplarily, assuming that K lithofacies categories include mudstone and sandstone, and the P-wave impedance difference calculation result between the simulated P-wave impedance value and the reference P-wave impedance value of the current sampling point under mudstone is smaller than the P-wave impedance difference calculation result between the simulated P-wave impedance value and the reference P-wave impedance value of the current sampling point under sandstone, then it can be determined that the minimum value of the P-wave impedance difference calculation results of the current sampling point under each lithofacies category is the P-wave impedance difference calculation result of the current sampling point under mudstone, and it can be determined that the lithofacies category corresponding to the minimum value of the P-wave impedance difference calculation results of the current sampling point under each lithofacies category is mudstone, and the electronic device can use mudstone as the lithofacies identification result of the current sampling point.

In another embodiment, the P-wave impedance of the target post-stack seismic data under each lithofacies category can be used to determine the target P-wave impedance corresponding to the target post-stack seismic data; wherein the target P-wave impedance may include the target P-wave impedance value of each sampling point (i.e., the target P-wave impedance value of each sampling point under the target post-stack seismic data). Based on this, the electronic device may use the simulated P-wave impedance value corresponding to the minimum value in the P-wave impedance difference calculation result of the current sampling point under each lithofacies category as the target P-wave impedance value of the current sampling point, so as to obtain the target P-wave impedance after traversing each sampling point among the N sampling points. wherein the simulated P-wave impedance value corresponding to the P-wave impedance difference calculation result of the current sampling point under a lithofacies category is the simulated P-wave impedance value of the current sampling point under the corresponding lithofacies category, that is, the simulated P-wave impedance value corresponding to the P-wave impedance difference calculation result of the current sampling point under any lithofacies category is the simulated P-wave impedance value of the current sampling point under any lithofacies category. Exemplarily, assuming that K lithofacies categories include mudstone and sandstone, and the result of the P-wave impedance difference calculation between the simulated P-wave impedance value of the current sampling point under the mudstone and the reference P-wave impedance value is smaller than the result of the P-wave impedance difference calculation between the simulated P-wave impedance value of the current sampling point under the sandstone and the reference P-wave impedance value, then the electronic device may use the simulated P-wave impedance value of the current sampling point under the mudstone as the target P-wave impedance value of the current sampling point.

The embodiment of the present invention can generate an initial P-wave impedance population under each lithofacies category based on the target prior probability density distribution of each lithofacies category after acquiring the target post-stack seismic data and the target prior probability density distribution of each lithofacies category in K lithofacies categories, wherein a target prior probability density distribution is used to indicate the prior probability density distribution that the P-wave impedance of the target post-stack seismic data under the corresponding lithofacies category obeys, the target post-stack seismic data includes the seismic sampling value of each sampling point in N sampling points, a P-wave impedance population includes the P-wave impedance of each P-wave impedance individual in NP P-wave impedance individuals, and the P-wave impedance of a P-wave impedance individual in an initial P-wave impedance population is obtained by sampling based on a target prior probability density distribution, M and N are both positive integers, and NP is an integer greater than 1. Further, the target P-wave impedance population set under each lithofacies category can be determined based on the initial P-wave impedance population under each lithofacies category; and the P-wave impedance of the target post-stack seismic data under each lithofacies category can be determined based on the target P-wave impedance population set under each lithofacies category, and the P-wave impedance support of the target post-stack seismic data under each lithofacies category is used to determine the target P-wave impedance and/or target lithofacies identification result corresponding to the target post-stack seismic data. It can be seen that the embodiment of the present invention can avoid falling into a local mechanism through the P-wave impedance population under each lithofacies category, which can effectively avoid falling into a local optimum and effectively improve the convergence accuracy of seismic inversion; that is, the embodiment of the present invention can improve the convergence accuracy of seismic inversion while avoiding falling into a local mechanism.

Based on the above description, the embodiment of the present invention further proposes a more specific seismic inversion method. Accordingly, the seismic inversion method can be executed by the electronic device (terminal or server) mentioned above; or, the seismic inversion method can be executed by the terminal and the server together. For the convenience of explanation, the following description is taken as an example of an electronic device executing the seismic inversion method; please refer to Figure 2, the seismic inversion method may include the following steps S201-S206:

S201, obtaining target post-stack seismic data, and obtaining a target prior probability density distribution of each lithofacies category in K lithofacies categories, a target prior probability density distribution is used to indicate the prior probability density distribution of the longitudinal wave impedance of the target post-stack seismic data under the corresponding lithofacies category, the target post-stack seismic data includes the seismic sampling value of each sampling point in N sampling points.

S202, based on the target prior probability density distribution of each lithofacies category, generate an initial P-wave impedance population under each lithofacies category, wherein a P-wave impedance population includes the P-wave impedance of each P-wave impedance individual among NP P-wave impedance individuals, and the P-wave impedance of a P-wave impedance individual in an initial P-wave impedance population is obtained based on sampling of a target prior probability density distribution.

Among them, 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 lithofacies category among the K lithofacies categories, the initial P-wave impedance population under any lithofacies category is updated through the particle swarm algorithm to obtain an updated P-wave impedance population under any lithofacies category, and the updated P-wave impedance population under any lithofacies category is added to the P-wave impedance population set under any lithofacies category to realize the state update of NP Markov chains, and one P-wave impedance individual corresponds to one Markov chain.

The initial P-wave impedance population under any lithofacies category may be the P-wave impedance population of any lithofacies category at the first iteration, and the updated P-wave impedance population under any lithofacies category may be the P-wave impedance population of any lithofacies category at the second iteration; that is, when t is 1, the P-wave impedance population of any lithofacies category at the tth iteration may be the initial P-wave impedance population under any lithofacies category, and the P-wave impedance population of any lithofacies category at the t+1th iteration may be the updated P-wave impedance population under any lithofacies category. The state update of a Markov chain may also refer to the iteration of the corresponding Markov chain, and a population update under any lithofacies category may include one iteration of each Markov chain under any lithofacies category, that is, one state update of each Markov chain under any lithofacies category.

In the embodiment of the present invention, the electronic device may add the P-wave impedance population of any lithofacies category in each iteration to the P-wave impedance population set under any lithofacies category, that is, the P-wave impedance population set under any lithofacies category may sequentially include the P-wave impedance population of any lithofacies category in each iteration. It should be understood that after completing the t+1th iteration, the P-wave impedance population set under any lithofacies category may include the P-wave impedance population of any lithofacies category in the 1st iteration, ..., the P-wave impedance population of any lithofacies category in the t+1th iteration.

S204, based on the updated P-wave impedance population under any lithofacies category, determine the current P-wave impedance inversion result under any lithofacies category, and judge whether the P-wave impedance inversion result under any lithofacies category tends to be stable based on the current P-wave impedance inversion result.

It should be understood that during the t+1th iteration, the electronic device can determine the current P-wave impedance inversion result under any lithofacies category (i.e., the P-wave impedance inversion result of any lithofacies category at the t+1th iteration) based on the P-wave impedance population of any lithofacies category at the t+1th iteration, so as to judge whether the P-wave impedance inversion result of any lithofacies category tends to be stable based on the P-wave impedance inversion result of any lithofacies category at the t+1th iteration.

In one embodiment, when determining the P-wave impedance inversion result of any lithofacies category at the t+1th iteration (i.e., the current P-wave impedance inversion result of any lithofacies category), a mean operation can be performed on the P-wave impedance population of any lithofacies category at the t+1th iteration to obtain the P-wave impedance inversion result of any lithofacies category at the t+1th iteration (at this time, it is the mean operation result between the P-wave impedances in the P-wave impedance population of any lithofacies category at the t+1th iteration); that is, when t is 1, a mean operation can be performed on the updated P-wave impedance population under any lithofacies category to obtain the current P-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 lithofacies category from the longitudinal wave impedance population set under any lithofacies category, and the number of iterations corresponding to any longitudinal wave impedance population set in the determined longitudinal wave impedance population set under any lithofacies category is greater than the number of iterations corresponding to all longitudinal wave impedance populations in the longitudinal wave impedance population set under any lithofacies category except the determined longitudinal wave impedance population set under any lithofacies category; wherein, when t+1 is less than H, the determined longitudinal wave impedance population set under any lithofacies category may be the longitudinal wave impedance population set under any lithofacies category, That is, the determined P-wave impedance population set under any lithofacies category may include all P-wave impedance population sets in the P-wave impedance population set under any lithofacies category, and at this time, the number of P-wave impedance population sets in the P-wave impedance population set under any lithofacies category is less than H, and H is an integer greater than 1; when t+1 is greater than or equal to H, the determined P-wave impedance population set under any lithofacies category may include the P-wave impedance populations in each iteration of the last H iterations in the P-wave impedance population set under any lithofacies category, that is, it may include the last H P-wave impedance populations in the P-wave impedance population set under any lithofacies category. Optionally, the value of H may be the same as or different from the number of P-wave impedance populations in the target P-wave impedance population set under any lithofacies category, and this is not limited in the embodiment of the present invention. Based on this, the electronic device can perform mean operation on the determined longitudinal wave impedance population set under any facies category, that is, perform mean operation on each longitudinal wave impedance in the determined longitudinal wave impedance population set under any facies category, and obtain the longitudinal wave impedance inversion result of any facies category under the t+1th iteration. At this time, the longitudinal wave impedance inversion result of any facies category under the t+1th iteration can be the mean operation result between each longitudinal wave impedance in the determined longitudinal wave impedance population set under any facies category (that is, each longitudinal wave impedance included in all longitudinal wave impedance populations in the determined longitudinal wave impedance population set under any facies category), and so on; the embodiment of the present invention is not limited to this.

Optionally, when judging whether the P-wave impedance inversion result under any facies category tends to be stable based on the current P-wave impedance inversion result, the electronic device may determine P historical P-wave impedance inversion results under any facies identification, and judge whether the P-wave impedance inversion result under any facies category tends to be stable based on the current P-wave impedance inversion result and the P historical P-wave impedance inversion results, where P is a positive integer; wherein the P historical P-wave impedance inversion results include the P-wave impedance inversion result of any facies category under each iteration in the P iterations before the t+1th iteration, that is, the current P-wave impedance inversion result and the P historical P-wave impedance inversion results may include the P-wave impedance inversion result of any facies category under each iteration in the subsequent P+1 iterations (i.e., from the t-P+1th iteration to the t+1th iteration). Optionally, the electronic device can also determine whether t+1 is greater than P; if t+1 is greater than P, it triggers the execution of the above-mentioned determination of P historical longitudinal wave impedance inversion results under any lithofacies identification; if t+1 is less than or equal to P, it determines that the longitudinal wave impedance inversion results under any lithofacies category have not stabilized.

Optionally, when judging whether the P-wave impedance inversion result under any facies category tends to be stable based on the current P-wave impedance inversion result and P historical P-wave impedance inversion results, the electronic device can judge whether the current P-wave impedance inversion result and the P historical P-wave impedance inversion results obey a steady-state distribution (also called a stable distribution), that is, it can judge whether the P historical P-wave impedance inversion results and the current P-wave impedance inversion result are a stable sequence; if the current P-wave impedance inversion result and the P historical P-wave impedance inversion results obey a steady-state distribution, it is determined that the P-wave impedance inversion result under any facies category tends to be stable, and at this time, the P historical P-wave impedance inversion results and the current P-wave impedance inversion result are a stable sequence; if the current P-wave impedance inversion result and the P historical P-wave impedance inversion results do not obey a steady-state distribution, it is determined that the P-wave impedance inversion result under any facies category does not tend to be stable, and at this time, the P historical P-wave impedance inversion results and the current P-wave impedance inversion result are not a stable sequence.

S205, when the P-wave impedance inversion result under any lithofacies category has not tended to be stable, continue to update the updated P-wave impedance population under any lithofacies category until the P-wave impedance inversion result under any lithofacies identification tends to be stable, so as to determine the target P-wave impedance population set under any lithofacies category from the P-wave impedance population set under any lithofacies category.

In an embodiment of the present 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. At this time, the longitudinal wave impedance population set under any lithofacies category may include the longitudinal wave impedance population of any lithofacies category in each iteration of T iterations, where T is the number of iterations when the population update under any lithofacies category is stopped, that is, T is the total number of iterations under any lithofacies category.

Based on this, the set of P-wave impedance populations under any lithofacies category may include the P-wave impedance populations of any lithofacies category in each iteration in T iterations, and the P-wave impedance population at the t+1th iteration in T iterations is obtained by updating the P-wave impedance population at the tth iteration, where T is an integer greater than 1, t∈[1, T-1]. The determination method of the P-wave impedance population of any lithofacies category at the t+1th iteration may be as follows.

Then correspondingly, when determining the P-wave impedance population of any lithofacies category at the t+1th iteration, for the i-th P-wave impedance in the P-wave impedance population of any lithofacies category at the tth iteration, and the n-th sampling point in the N sampling points, the electronic device can use the i-th P-wave impedance in the P-wave impedance population of any lithofacies category at the tth iteration as the i-th P-wave impedance at the tth iteration, i∈[1, NP], n∈[1, N]; and through the particle swarm algorithm, based on the P-wave impedance value of the n-th sampling point in the i-th P-wave impedance at the tth iteration, determine the candidate P-wave impedance value of the n-th sampling point (i.e., the candidate P-wave impedance value of the n-th sampling point in the i-th P-wave impedance at the t+1th iteration) to obtain the candidate P-wave impedance, which includes the candidate P-wave impedance values of each sampling point. Specifically, the electronic device can use formula 2.1 to calculate the candidate P-wave impedance value of the n-th sampling point:

Formula 2.1

Wherein, t represents the number of iterations, m _(i,n) (t+1) represents the candidate P-wave impedance value of the n-th sampling point in the ith P-wave impedance at the t+1-th iteration (i.e., the ith P-wave impedance in the P-wave impedance population of any lithofacies category at the t+1-th iteration, i.e., the P-wave impedance of the ith P-wave impedance individual of any lithofacies category at the t+1-th iteration) (i.e., the value of the n-th dimension of the ith Markov chain at the t+1-th iteration), and v _(i,n) (t+1) represents the velocity of the n-th sampling point in the ith P-wave impedance of any lithofacies category at the t+1-th iteration (i.e., the velocity of the n-th dimension of the ith Markov chain at the t+1-th 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 i-th longitudinal wave impedance at the t-th iteration through the particle swarm algorithm, the electronic device can 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 i-th longitudinal wave impedance at the t-th iteration and the velocity of the nth sampling point in the i-th longitudinal wave impedance at the t+1-th iteration.

Specifically, the electronic device can use Formula 2.2 to calculate the velocity of the nth sampling point in the ith longitudinal wave impedance of any lithofacies category at the t+1th iteration:

Formula 2.2

Wherein, ω is the inertia weight, c ₁ and c ₂ are both positive constants (also known as acceleration coefficients), r ₁ and r ₂ are both random numbers varying in the range of [0, 1] (i.e., they can be randomly generated); optionally, ω, c ₁ and c ₂ can be set according to experience or according to actual needs, which is not limited in the embodiment of the present invention. Correspondingly, Pbest _(i,n) (t) can represent the P-wave impedance value of the nth sampling point in the optimal P-wave impedance of the i-th P-wave impedance individual (i.e., the optimal position in the i-th P-wave impedance of any lithofacies category in each iteration in the first t iterations, which can also be called the individual historical optimal position experienced by the i-th P-wave impedance individual in the first t iterations); Gbest _n (t) can represent the P-wave impedance value of the nth sampling point included in the optimal P-wave impedance of all P-wave impedances of each P-wave impedance individual in any lithofacies category in the first t iterations (i.e., the historical optimal position in all individual positions experienced by all P-wave impedance individuals under any lithofacies category). Optionally, the electronic device may calculate an evaluation value of a longitudinal wave impedance using a target evaluation function, and then the electronic device may determine the evaluation value of each longitudinal wave impedance in the longitudinal wave impedance population of any facies category in each iteration through the target evaluation function; based on this, the longitudinal wave impedance corresponding to the maximum value in the evaluation value of the i-th longitudinal wave impedance in each longitudinal wave impedance population of any facies category in the first t iterations (i.e., the longitudinal wave impedance of the i-th longitudinal wave impedance individual under any facies category in the first t iterations) may be used as the optimal longitudinal wave impedance of the i-th longitudinal wave impedance individual (also referred to as the optimal longitudinal wave impedance of the i-th longitudinal wave impedance individual in the first t iterations); accordingly, the longitudinal wave impedance corresponding to the maximum value in the evaluation value of each longitudinal wave impedance of any facies category in the first t iterations may be used as the optimal longitudinal wave impedance of all longitudinal wave impedances of each longitudinal wave impedance individual in any facies category in the first t iterations. Optionally, the target evaluation function may be set according to experience or according to actual needs, and the embodiment of the present invention does not limit this; for example, the target evaluation function may be the following loss function, etc.

Further, the electronic device may calculate the reception probability of the candidate P-wave impedance based on the i-th P-wave impedance at the t-th iteration, the candidate P-wave impedance and the target post-stack seismic data, and determine whether to receive the candidate P-wave impedance based on the reception probability of the candidate P-wave impedance; if it is determined to receive the candidate P-wave impedance, the candidate P-wave impedance is used as the i-th P-wave impedance in the P-wave impedance population of any facies category at the t+1-th iteration; if it is determined not to receive the candidate P-wave impedance, the i-th P-wave impedance at the t-th iteration is used as the i-th P-wave impedance in the P-wave impedance population of any facies category at the t+1-th iteration, so as to determine the P-wave impedance population of any facies category at the t+1-th iteration. Optionally, the electronic device may determine a reception probability threshold, and if the reception probability of the candidate P-wave impedance is greater than or equal to the reception probability threshold, it may be determined to receive the candidate P-wave impedance; if the reception probability of the candidate P-wave impedance is less than the acceptance probability threshold, it may be determined not to accept the candidate P-wave impedance. Optionally, the reception probability threshold may be set according to experience or actual needs, or may be obtained through random sampling, which is not limited in the embodiments of the present invention; illustratively, the reception probability threshold may be obtained through log(U[0,1]) sampling.

Optionally, when any lithofacies category is the kth lithofacies category among K lithofacies categories, k∈[1, K], the electronic device may use formula 2.3 to calculate the reception probability of the candidate longitudinal wave impedance:

Formula 2.3

in, It can represent the candidate longitudinal wave impedance (i.e., the above-mentioned candidate longitudinal wave impedance) of the i-th longitudinal wave impedance individual in the t+1-th iteration process (i.e., the t+1-th iteration), and can also be called the candidate state of the i-th longitudinal wave impedance individual generated in the t+1-th iteration process; It can represent the longitudinal wave impedance of the i-th longitudinal wave impedance individual in the t-th iteration process, and can also be called the current state of the i-th longitudinal wave impedance individual in the t-th iteration process. Optionally, p _k (m|d) can be the k-th Laplace component of the posterior probability density distribution of the longitudinal wave impedance, and the posterior probability density distribution p(m|d) can be a mixed Laplace probability density distribution; L _k (m|d) can represent a loss function (also called a target functional) equivalent to p _k (m|d).

Correspondingly, the electronic device can use formula 2.4 to calculate the loss function under the kth component:

Formula 2.4

Wherein, d may represent the target post-stack seismic data (a vector), W may represent the seismic wavelet matrix (which may be obtained by sampling the seismic wavelet (such as Ricker wavelet) and the number of sampling points, or may be set according to experience or actual needs, which is not limited in the embodiment of the present invention), D may be a difference matrix operator (which may be set according to experience or actual needs, etc.), and _Cd may represent the covariance matrix of the noise in the observed seismic data (which is used to measure the uncertainty of the seismic data, the larger the covariance, the lower the signal-to-noise ratio of the seismic data, which may be determined by the noise data in the observed seismic data, or may be set according to experience or actual needs, etc.). In addition, It can represent the variance of the nth sampling point in the target prior probability density distribution of the kth lithofacies category (such as the kth Laplace probability density distribution) (i.e., the longitudinal wave impedance variance of the nth sampling point under the kth lithofacies category), It can represent the center position of the nth sampling point in the target prior probability density distribution of the kth lithofacies category (i.e., the mean value of the P-wave impedance of the nth sampling point under the kth lithofacies category). It should be noted that the target post-stack seismic data can be represented as the convolution of the seismic wavelet and the seismic reflection coefficient, that is, the target post-stack seismic data can be represented as d=WDm+ε; where ε can represent the noise corresponding to the target post-stack seismic data, and one seismic trace can correspond to one noise, that is, it can represent the noise corresponding to the seismic trace 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 present invention, the Gaussian likelihood function p(d|m) of the target post-stack seismic data and the mixed Laplace probability density distribution (i.e., the sum of the target prior probability density distributions of each lithofacies category) are combined through the hierarchical Bayesian formula to obtain the posterior probability density distribution of the P-wave impedance, as shown in Formula 2.5:

Formula 2.5

Based on this, the posterior probability density distribution p(m|d) can be a mixed Laplace probability density distribution, then p(m|d) can be shown as formula 2.6:

Formula 2.6

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

Formula 2.7

Furthermore, 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 category) can be substituted into Formula 2.7, and the natural logarithm of the posterior probability density distribution can be used to construct an equivalent target functional (i.e., the natural logarithm of the two equivalent parts in Formula 2.7 can be taken respectively), thereby equivalently obtaining Formula 2.4. Among them, the kth Laplace prior probability density distribution can be shown as Formula 2.8:

Formula 2.8

Based on this, when calculating the reception probability of the candidate P-wave impedance based on the i-th P-wave impedance, the candidate P-wave impedance and the target post-stack seismic data at the t-th iteration, the electronic device can determine the mean P-wave impedance and the variance of the P-wave impedance of each sampling point in any lithofacies category, such as determining the mean P-wave impedance and the variance of the P-wave impedance of each sampling point in any lithofacies category from the target prior probability density distribution of any lithofacies category; and based on the i-th P-wave impedance, the candidate P-wave impedance, the target post-stack seismic data and the mean P-wave impedance and the variance of the P-wave impedance of each sampling point in any lithofacies category at the t-th iteration, the reception probability of the candidate P-wave impedance is calculated; that is, the i-th P-wave impedance, the candidate P-wave impedance, the target post-stack seismic data and the mean P-wave impedance and the variance of the P-wave impedance of each sampling point in any lithofacies category at the t-th iteration can be substituted into formula 2.3 to calculate the reception probability of the candidate P-wave impedance. Specifically, the electronic device can use the i-th P-wave impedance under the t-th iteration, the candidate P-wave impedance, the target post-stack seismic data, the seismic wavelet matrix, the difference matrix operator, the covariance matrix of the noise, and the mean P-wave impedance and the P-wave impedance variance of each sampling point under any lithofacies category to calculate the reception probability of the candidate P-wave impedance.

Further, when determining the target P-wave impedance population set under any facies category from the P-wave impedance population set under any facies category, the last Q P-wave impedance populations in the P-wave impedance population set under any facies category can be added to the target P-wave impedance population cluster under any facies category to determine the target P-wave impedance population set under any facies category, where Q is a positive integer; optionally, Q can be set according to experience or actual needs, or can be determined according to a preset ratio threshold, and so on; illustratively, when Q is determined according to a preset ratio threshold, Q can be equal to the multiplication result between the preset ratio threshold and the number of P-wave impedance populations in the P-wave impedance population set under any facies category; optionally, the preset ratio threshold can be set according to experience, or can be set according to actual needs, and the embodiments of the present invention are not limited to this.

S206, based on the target P-wave impedance population set under each lithofacies category, determine the P-wave impedance of the target post-stack seismic data under each lithofacies category, and the P-wave impedance support of the target post-stack seismic data under each lithofacies category is used to determine the target P-wave impedance and/or target lithofacies identification result corresponding to the target post-stack seismic data.

Specifically, when determining the P-wave impedance of the target post-stack seismic data in each lithofacies category based on the target P-wave impedance population set in each lithofacies category, for any lithofacies category among the K lithofacies categories, the electronic device may perform mean calculation on each P-wave impedance in the target P-wave impedance population set in any lithofacies category to obtain the P-wave impedance of the target post-stack seismic data in any lithofacies category, that is, the P-wave impedance of the target post-stack seismic data in any lithofacies category may be the mean calculation result between all P-wave impedances in the target P-wave impedance population set in any lithofacies category. The P-wave impedance of the target post-stack seismic data in any lithofacies category may also be referred to as the posterior mean of the P-wave impedance in any lithofacies category.

Further, the electronic device may also determine the P-wave impedance covariance matrix and confidence interval indication data of the target post-stack seismic data under any lithofacies category based on the target P-wave impedance population set under any lithofacies category, and the embodiment of the present invention is not limited to this. Optionally, the confidence interval indication data of the target post-stack seismic data under any lithofacies category may include the confidence interval of each sampling point of the target post-stack seismic data under any lithofacies category; illustratively, for any sampling point among the N sampling points, the P-wave impedance variance of any sampling point under any lithofacies category may be calculated based on all P-wave impedance values of any sampling point in the target P-wave impedance population set under any lithofacies category, so as to determine the confidence interval of any sampling point of the target post-stack seismic data under any lithofacies category based on the P-wave impedance variance of any sampling point under any lithofacies category, such as a P-wave impedance variance range may correspond to one confidence interval.

Optionally, the electronic device may perform multiple simulations on the target post-stack seismic data to obtain the target P-wave impedance and/or target lithofacies identification result of the target post-stack seismic data in each simulation in the multiple simulations; accordingly, the target P-wave impedance of the target post-stack seismic data in each simulation may be averaged to obtain the target P-wave impedance corresponding to the target post-stack seismic data; and, the target lithofacies identification result of the target post-stack seismic data in each simulation may be statistically analyzed, such as the lithofacies identification result of any sampling point of the target post-stack seismic data in each simulation may be statistically analyzed, and the lithofacies identification result with the largest statistical number may be used as the lithofacies identification result of any sampling point in the target lithofacies identification result, thereby obtaining the target lithofacies identification result.

In summary, the embodiment of the present invention can introduce the particle swarm algorithm into the Markov chain Monte Carlo algorithm (MCMC algorithm, a method of random simulation using Markov chain), and the MCMC algorithm can be the Metropolis-Hastings algorithm (a specific MCMC algorithm), then the particle swarm algorithm can be introduced into the Metropolis-Hastings algorithm to determine the above-mentioned candidate longitudinal wave impedance in the t+1th iteration process; it can be seen that the embodiment of the present invention proposes a seismic inversion algorithm that combines the particle swarm optimization algorithm and the Monte Carlo model, that is, an improved particle swarm-Metropolis Hastings algorithm seismic probabilistic inversion and lithofacies identification method is proposed. Based on this, the PSO-MCMC algorithm combines the global optimization characteristics of the particle swarm algorithm under the framework of the Metropolis-Hastings algorithm, and improves the generation process of candidate states through the motion state of particles (i.e., P-wave impedance individuals). Multiple Markov chains can be optimized synchronously, which can not only improve the acceptance probability of candidate states, but also improve the convergence efficiency of model parameters (i.e., P-wave impedance). PSO-MCMC combines the advantages of the particle swarm optimization algorithm and the Markov (i.e., Markov) chain Monte Carlo model. In the group optimization process of multiple Markov chains (synchronous optimization of multiple P-wave impedance individuals), the uncertainty quantification characteristics such as the posterior mean, covariance, and confidence interval of parameters such as P-wave impedance are calculated synchronously, which is of great significance to improving the stability of P-wave impedance seismic probabilistic inversion.

In an embodiment of the present invention, in order to further verify the feasibility of the seismic inversion mentioned in the embodiment of the present invention, on the one hand, a synthetic seismic record is established based on a conventional numerical model (i.e., complete data of a virtual underground geological body matched with seismic data wave impedance data, on which simulation calculations can be performed), and the seismic inversion method mentioned in the embodiment of the present invention is used for inversion under different signal-to-noise ratio conditions; as shown in Figures 3-6, after 50 simulations under different signal-to-noise ratios, the inversion results maintain a high degree of consistency with the synthetic data, verifying the feasibility and stability of the seismic inversion method mentioned in the embodiment of the present invention; that is, by conducting experiments under different signal-to-noise ratios, it can be seen that the longitudinal wave impedance estimation accuracy of the seismic inversion method mentioned in the embodiment of the present invention is less affected by noise, and the inversion error of the posterior mean is small, verifying the feasibility and stability of the seismic inversion method proposed in the embodiment of the present invention in the coordinated prediction of stratigraphic facies and longitudinal wave impedance. The longitudinal wave impedance in each of Figures 3 to 6 may be the longitudinal wave impedance obtained by 10 seismic inversions, that is, it may include the longitudinal wave impedance in each simulation in the 10 simulations, and the abscissa of the longitudinal wave impedance in each of Figures 3 to 6 may be the longitudinal wave impedance value (unit: kg/m ² ·s (i.e., kilograms/square meter.second)); the seismic data in each of Figures 3 to 6 may include the seismic data obtained by the mixed domain convolution model and the zero-phase 30 Hz (Hertz) Seismic data synthesized by Ricker wavelet, seismic data fitted by 50 simulation results of two Gaussian components (such as sandstone and mudstone), theoretical seismic data without noise pollution, etc., and the abscissa of the seismic data in each of Figures 3 to 6 can be amplitude (unit: m); the abscissa of the actual lithofacies and lithofacies identification results in each of Figures 3 to 6 can be formation width, and only exemplarily, when the lithofacies (such as actual lithofacies or lithofacies identification results) of a sampling point is sandstone, one color is used for horizontal representation, and when the lithofacies of a sampling point is mudstone, another color is used for horizontal representation, thereby realizing the lithofacies representation of each sampling point in a width range, wherein the color used to represent mudstone here is darker than the color used to represent sandstone.

On the other hand, the embodiment of the present invention tests the effectiveness of the seismic inversion method mentioned in the embodiment of the present invention by comparing the running speeds of the original inversion method and the seismic inversion method based on the particle swarm-MCMC algorithm; as shown in Figure 7, the running speed of the seismic inversion method mentioned in the embodiment of the present invention is faster than the existing MCMC algorithm in each simulation, that is, the embodiment of the present invention can effectively reduce the running time and improve the computing efficiency.

On the other hand, the embodiment of the present invention verifies the effectiveness of actual seismic data processing. A two-dimensional seismic profile with complex geological structure is selected, and the seismic inversion method proposed in the embodiment of the present invention is adopted. By comparing with actual well logging data, the stability and effectiveness of the seismic inversion method proposed in the embodiment of the present invention are verified. Exemplarily, as shown in FIG8 , (a) in FIG8 (i.e., the sub-graph marked with (a)) is a real post-stack seismic profile (i.e., the actual post-stack seismic profile, which contains 255 seismic data in the horizontal direction), (b) in FIG8 (i.e., the sub-graph marked with (b)) is a low-frequency background of P-wave impedance (i.e., a known low-frequency prior background, such as the data of the low-frequency part of the P-wave impedance obtained by logging, which can be established with the help of software such as Jason (a geological platform) and Geoview (a pre-stack and post-stack joint inversion software)), (c) in FIG8 (i.e., the sub-graph marked with (c)) is the P-wave impedance obtained by seismic inversion (here, the P-wave impedance inversion result after 10 simulations), and (d) in FIG8 (i.e., the sub-graph marked with (d)) is a lithofacies identification result (here, the lithofacies identification statistical result after 10 simulations); wherein, the abscissa is the width of the sampled stratum (unit: m), and the ordinate is the depth in the time domain; optionally, the longitudinal sampling interval can be 2 milliseconds (ms). It can be seen that the P-wave impedance and lithofacies identification results are well consistent with the logging data (i.e., the real post-stack seismic profile, etc.), which verifies the effectiveness and practicality of the seismic inversion method proposed in the embodiment of the present invention. The seismic profile inversion result shown in FIG8 may include the P-wave impedance and/or lithofacies identification results corresponding to each of the 255 post-stack seismic data.

The embodiment of the present invention can generate an initial P-wave impedance population under each lithofacies category based on the target prior probability density distribution of each lithofacies category after acquiring the target post-stack seismic data and the target prior probability density distribution of each lithofacies category in the K lithofacies categories, wherein a P-wave impedance population includes the P-wave impedance of each P-wave impedance individual among NP P-wave impedance individuals, and the P-wave impedance of a P-wave impedance individual in an initial P-wave impedance population is obtained based on sampling of a target prior probability density distribution; wherein the P-wave impedance of a P-wave impedance individual in an initial P-wave impedance population is the initial state of a Markov chain. Based on this, for any lithofacies category in the K lithofacies categories, the initial P-wave impedance population under any lithofacies category can be updated by using a particle swarm algorithm to obtain an updated P-wave impedance population under any lithofacies category, and the updated P-wave impedance population under any lithofacies category is added to the P-wave impedance population set under any lithofacies category to realize the state update of NP Markov chains, and one P-wave impedance individual corresponds to one Markov chain. Furthermore, the current P-wave impedance inversion result under any lithofacies category can be determined based on the updated P-wave impedance population under any lithofacies category, and whether the P-wave impedance inversion result under any lithofacies category tends to be stable can be judged based on the current P-wave impedance inversion result; when the P-wave impedance inversion result under any lithofacies category does not tend to be stable, the updated P-wave impedance population under any lithofacies category is continuously updated until the P-wave impedance inversion result under any lithofacies identification tends to be stable, so as to determine the target P-wave impedance population set under any lithofacies category from the P-wave impedance population set under any lithofacies category. Accordingly, the P-wave impedance of the target post-stack seismic data under each lithofacies category can be determined based on the target P-wave impedance population set under each lithofacies category, and the P-wave impedance of the target post-stack seismic data under each lithofacies category supports the determination of the target P-wave impedance and/or the target lithofacies identification result corresponding to the target post-stack seismic data. It can be seen that the embodiment of the present invention proposes a seismic inversion method that combines a particle swarm optimization algorithm (i.e., a particle swarm algorithm) with a Monte Carlo model, which can effectively solve the problems of low convergence efficiency, low computational efficiency, easy to fall into local mechanisms, and long time consumption in related technologies; that is, the embodiment of the present invention can improve the computational efficiency and convergence accuracy of seismic probabilistic inversion (i.e., seismic inversion), and can achieve stable and efficient prediction of parameters such as longitudinal wave impedance of oil and gas reservoirs, thereby focusing on the problem of seismic probabilistic inversion in complex oil and gas reservoirs.

Based on the description of the relevant embodiments of the above-mentioned seismic inversion method, the embodiment of the present invention further proposes a seismic inversion device, which can be a computer program (including program code) running in an electronic device; as shown in FIG9 , the seismic inversion device can include an acquisition unit 901 and a processing unit 902. The seismic inversion device can execute the seismic inversion method shown in FIG1 or FIG2 , that is, the seismic inversion device can run the above-mentioned units:

An acquisition unit 901 is used to acquire target post-stack seismic data and a target priori probability density distribution of each lithofacies category in K lithofacies categories, wherein a target priori probability density distribution is used to indicate a priori probability density distribution that the longitudinal wave impedance of the target post-stack seismic data in the corresponding lithofacies category obeys, and the target post-stack seismic data includes seismic sampling values of each sampling point in N sampling points, where K and N are both positive integers;

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

The processing unit 902 is further configured to determine a target longitudinal wave impedance population set under each lithofacies category based on the initial longitudinal wave impedance population under each lithofacies category;

The processing unit 902 is also used to determine the longitudinal wave impedance of the target post-stack seismic data in each lithofacies category based on the target longitudinal wave impedance population set in each lithofacies category. The longitudinal wave impedance support of the target post-stack seismic data in each lithofacies category is used to determine the target longitudinal wave impedance and/or target lithofacies identification result corresponding to the target post-stack seismic data.

In one embodiment, the target lithofacies identification result includes the lithofacies identification results of the sampling points, and a longitudinal wave impedance includes the longitudinal wave impedance values of the sampling points. The processing unit 902 can also be used to: traverse each sampling point in the N sampling points, and use the currently traversed sampling point as the current sampling point; determine the simulated longitudinal wave impedance value of the current sampling point in each lithofacies category from the longitudinal wave impedance of the target post-stack seismic data in each lithofacies category, and determine the reference longitudinal wave impedance value of the current sampling point in each lithofacies category from the reference longitudinal wave impedance in each lithofacies category; perform difference calculation on the simulated longitudinal wave impedance value and the reference longitudinal wave impedance value of the current sampling point in each lithofacies category, respectively, to obtain the longitudinal wave impedance difference calculation result of the current sampling point in each lithofacies category, and use the lithofacies category corresponding to the minimum value in the longitudinal wave impedance difference calculation result of the current sampling point in each lithofacies category as the lithofacies identification result of the current sampling point; after traversing each sampling point in the N sampling points, the target lithofacies identification result is obtained.

In another embodiment, the target longitudinal wave impedance includes the target longitudinal wave impedance value of each sampling point, and the processing unit 902 can also be used to: use the simulated longitudinal wave impedance value corresponding to the minimum value in the longitudinal wave impedance difference calculation results of the current sampling point under the various lithology categories as the target longitudinal wave impedance value of the current sampling point, so as to obtain the target longitudinal wave impedance after traversing each of the N sampling points.

In another embodiment, 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. When the processing unit 902 determines the target longitudinal wave impedance population set under each lithofacies category based on the initial longitudinal wave impedance population under each lithofacies category, it can be specifically used to: for any lithofacies category among the K lithofacies categories, use the particle swarm algorithm to update the initial longitudinal wave impedance population under any lithofacies category to obtain an updated longitudinal wave impedance population under any lithofacies category, and add the updated longitudinal wave impedance population under any lithofacies category to the longitudinal wave impedance population set under any lithofacies category to achieve NP Markov chain. The state of the Markov chain is updated, and one P-wave impedance individual corresponds to one Markov chain; based on the updated P-wave impedance population under any lithofacies category, the current P-wave impedance inversion result under any lithofacies category is determined, and based on the current P-wave impedance inversion result, whether the P-wave impedance inversion result under any lithofacies category tends to be stable is judged; when the P-wave impedance inversion result under any lithofacies category does not tend to be stable, the updated P-wave impedance population under any lithofacies category is continuously updated until the P-wave impedance inversion result under any lithofacies identification tends to be stable, so as to determine the target P-wave impedance population set under any lithofacies category from the P-wave impedance population set under any lithofacies category.

In another embodiment, the P-wave impedance population set under any facies category includes the P-wave impedance population of any facies category in each iteration in T iterations, the P-wave impedance population at the t+1th iteration in the T iterations is obtained by updating the P-wave impedance population at the t-th iteration, T is an integer greater than 1, t∈[1, T-1]; the processing unit 902 can also be used to determine the P-wave impedance population of any facies category at the t+1th iteration, and the determination method of the P-wave impedance population of any facies category at the t+1th iteration includes: for the i-th P-wave impedance in the P-wave impedance population of any facies category at the t-th iteration, and the n-th sampling point among the N sampling points, the i-th P-wave impedance in the P-wave impedance population of any facies category at the t-th iteration is used as the i-th P-wave impedance at the t-th iteration, i∈[1, NP], n∈[1, N]; through the particle swarm algorithm, based on the t-th iteration, the P-wave impedance of the facies category is determined. The method comprises the following steps: determining a candidate P-wave impedance value of the n-th sampling point in the i-th P-wave impedance of the t-th iteration, determining a candidate P-wave impedance value of the n-th sampling point, so as to obtain a candidate P-wave impedance, wherein the candidate P-wave impedance includes the candidate P-wave impedance values of the respective sampling points; calculating a reception probability of the candidate P-wave impedance based on the i-th P-wave impedance of the t-th iteration, the candidate P-wave impedance and the target post-stack seismic data, and judging whether to receive the candidate P-wave impedance based on the reception probability of the candidate P-wave impedance; if it is determined that the candidate P-wave impedance is received, taking the candidate P-wave impedance as the i-th P-wave impedance in the P-wave impedance population of any lithofacies category at the t+1-th iteration; and if it is determined that the candidate P-wave impedance is not received, taking the i-th P-wave impedance of the t-th iteration as the i-th P-wave impedance in the P-wave impedance population of any lithofacies category at the t+1-th iteration, so as to determine the P-wave impedance population of any lithofacies category at the t+1-th iteration.

In another embodiment, when the processing unit 902 calculates the reception probability of the candidate P-wave impedance based on the i-th P-wave impedance at the t-th iteration, the candidate P-wave impedance and the target post-stack seismic data, it can be specifically used to: determine the mean P-wave impedance and the variance of the P-wave impedance of each sampling point under any lithofacies category; calculate the reception probability of the candidate P-wave impedance based on the i-th P-wave impedance at the t-th iteration, the candidate P-wave impedance, the target post-stack seismic data and the mean P-wave impedance and the variance of the P-wave impedance of each sampling point under any lithofacies category.

In another embodiment, when the processing unit 902 determines the longitudinal wave impedance of the target post-stack seismic data in each lithofacies category based on the target longitudinal wave impedance population set under each lithofacies category, it can be specifically used to: for any lithofacies category among the K lithofacies categories, perform mean operation on each longitudinal wave impedance in the target longitudinal wave impedance population set under any lithofacies category to obtain the longitudinal wave impedance of the target post-stack seismic data in any lithofacies category.

According to one embodiment of the present invention, each unit in the seismic inversion device shown in FIG. 9 can be separately or completely combined into one or several other units to form a structure, or one (or some) of the units can be further divided into multiple functionally smaller units to form a structure, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above-mentioned units are divided based on logical functions. In practical applications, the function of one unit can also be realized by multiple units, or the functions of multiple units can be realized by one unit. In other embodiments of the present invention, any seismic inversion device can also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

According to another embodiment of the present invention, a seismic inversion device as shown in FIG. 9 can be constructed and a seismic inversion method of the embodiment of the present invention can be implemented by running a computer program (including program code) capable of executing each step involved in the corresponding method as shown in FIG. 1 or FIG. 2 on a general electronic device such as a computer including processing elements and storage elements such as a central processing unit (CPU), a random access storage medium (RAM), and a read-only storage medium (ROM). The computer program can be recorded on, for example, a computer storage medium, and loaded into the above-mentioned electronic device through the computer storage medium and run therein.

Based on the description of the above method embodiment and device embodiment, the exemplary embodiment of the present invention further provides an electronic device, including: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to enable the electronic device to perform the method according to the embodiment of the present invention when executed by the at least one processor.

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 used to cause the computer to perform a method according to an embodiment of the present invention.

An exemplary embodiment of the present invention further provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor of a computer, the computer is used to enable the computer to perform a method according to an embodiment of the present invention.

With reference to Figure 10, the structural block diagram of the electronic device 1000 that can be used as the server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer equipment, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples, and are not intended to limit the implementation of the present invention described herein and/or required.

As shown in FIG10 , the electronic device 1000 includes a computing unit 1001, which 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 device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input/output (I/O) interface 1005 is also connected to the bus 1004.

A plurality of 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 digital or character information, and generate key signal inputs related to user settings and/or function control 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, a speaker, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 1008 may include but is not limited to a disk, an optical disk. The communication unit 1009 allows the electronic device 1000 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks, and may include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and/or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and/or the like.

The computing unit 1001 may be a variety of general and/or special processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1001 performs the various methods and processes described above. For example, in some embodiments, the seismic inversion method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 1008. In some embodiments, part or all of the computer program may be loaded and/or installed on 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 in any other appropriate manner (e.g., by means of firmware).

The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions/operations specified in the flow chart and/or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk 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., disk, optical disk, memory, programmable logic device (PLD)) for providing 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 for providing machine instructions and/or data to a programmable processor.

To provide interaction with a user, the systems and techniques described herein 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 the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

Furthermore, it should be understood that what is disclosed above is only a preferred embodiment of the present invention, and certainly cannot be used to limit the scope of rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope covered by the present invention.

Claims (8)

1. A method of seismic inversion, 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; 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 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 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;

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 ].

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.