CN109100786B

CN109100786B - Method and device for determining quality factor of depth domain

Info

Publication number: CN109100786B
Application number: CN201810642901.9A
Authority: CN
Inventors: 王磊; 梁兼栋; 张少华; 钱忠平; 薛红刚
Original assignee: China National Petroleum Corp; BGP Inc
Current assignee: China National Petroleum Corp; BGP Inc
Priority date: 2018-06-21
Filing date: 2018-06-21
Publication date: 2020-09-08
Anticipated expiration: 2038-06-21
Also published as: CN109100786A

Abstract

The embodiment of the application provides a method and a device for determining a depth domain quality factor, wherein the method comprises the following steps: acquiring a seismic record of a target area; acquiring a time domain migration gather and a seismic reflection interface according to the seismic record; performing autocorrelation processing on the time domain migration gather at the seismic reflection interface to obtain an autocorrelation function of the time domain migration gather; obtaining an amplitude spectrum of an autocorrelation function of the time domain migration gather; determining an effective quality factor according to the amplitude spectrum; and determining the depth domain quality factor according to the effective quality factor. According to the scheme, the autocorrelation processing is firstly carried out on the time domain offset gather, and the amplitude spectrum is obtained according to the processed autocorrelation function, so that the interference of noise in data is eliminated, and a more accurate amplitude spectrum is obtained; and determining the effective quality factor by using the amplitude spectrum to determine the quality factor of the depth domain, thereby solving the technical problems of poor precision and low resolution in determining the quality factor of the depth domain in the existing method.

Description

Method and device for determining quality factor of depth domain

Technical Field

The application relates to the technical field of seismic exploration, in particular to a method and a device for determining a depth domain quality factor.

Background

Migration imaging is often required in seismic exploration by using seismic data (such as seismic records) of a target area so as to perform specific seismic exploration on the target area according to the imaging result.

When seismic data is used for migration imaging, in order to reduce errors caused by amplitude abnormality due to absorption attenuation of the seismic data, a corresponding depth domain quality factor (or called quality factor, effective Q value) is usually calculated for the seismic data of a target area to measure a ratio of total energy to absorption energy in the seismic data, and then the depth domain quality factor is used for prestack depth migration imaging, so that influences caused by absorption attenuation can be reduced.

At present, in order to determine a quality factor of a depth domain, most of the existing methods firstly acquire a time domain migration gather according to seismic data, and then directly perform fourier transform on the time domain migration gather to extract an amplitude spectrum; and further determining the quality factor of the depth domain according to the amplitude spectrum. However, the above method has two problems in implementation: firstly, noise data generally exists in the acquired time domain offset gather, the signal-to-noise ratio is relatively low, and the accuracy of the depth domain quality factor determined based on the time domain offset gather is often poor; secondly, the determination of the depth domain quality factor according to the amplitude spectrum is realized on the assumption that the wavelet is a rake wavelet, but the spectral morphology of the wavelet of the amplitude spectrum directly acquired according to the time domain offset gather in the method is often different from that of the rake wavelet, and the resolution of the determined depth domain quality factor is influenced. In summary, the existing method is often implemented to have the technical problems of poor accuracy and low resolution in determining the quality factor of the depth domain.

In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The embodiment of the application provides a method and a device for determining a depth domain quality factor, which are used for solving the technical problems of poor precision and low resolution of the depth domain quality factor determined by the conventional method, and achieving the technical effects of obtaining the depth domain quality factor with high resolution and high precision and improving the seismic exploration precision.

The embodiment of the application provides a method for determining a quality factor of a depth domain, which comprises the following steps:

acquiring a seismic record of a target area;

acquiring a time domain migration gather and a seismic reflection interface according to the seismic record;

performing autocorrelation processing on the time domain migration gather at the seismic reflection interface to obtain an autocorrelation function of the time domain migration gather;

obtaining an amplitude spectrum of an autocorrelation function of the time domain migration gather;

determining an effective quality factor according to the amplitude spectrum of the autocorrelation function of the time domain migration gather;

and determining a depth domain quality factor according to the effective quality factor.

In one embodiment, acquiring a seismic reflection interface from the seismic record includes:

establishing a depth domain velocity model by using the seismic records;

determining a depth migration profile according to the seismic record and the depth domain velocity model;

and acquiring a seismic reflection interface according to the depth migration profile.

In one embodiment, acquiring a time domain offset gather from the seismic record comprises:

establishing a depth domain velocity model by using the seismic records;

determining a depth domain migration gather according to the seismic record and the depth domain velocity model;

establishing a depth relation according to the depth domain speed model;

and according to the time-depth relation, performing deep-time conversion on the depth domain migration gather to obtain the time domain migration gather.

In one embodiment, auto-correlating the time domain migrated gather at the seismic reflection interface to obtain an auto-correlation function for the time domain migrated gather includes:

performing autocorrelation processing on the time domain offset gather according to the following formula:

in the above formula, c (u) is the autocorrelation function of the time domain offset trace set, u is the delay of autocorrelation, f (x) is the amplitude value at the x sampling point in the time domain offset trace set, x is the sampling point in the time domain offset trace set, and window is the length of the time window for autocorrelation processing.

In one embodiment, obtaining an amplitude spectrum of an autocorrelation function of the time domain offset gather comprises:

determining the position of the amplitude peak of the autocorrelation function of the time domain offset gather;

and acquiring signal data with a preset length at the position of an amplitude peak value in the autocorrelation function of the time domain offset gather, and determining an amplitude spectrum of the autocorrelation function of the time domain offset gather according to the signal data with the preset length, wherein the preset length is a wavelet length.

In one embodiment, determining an effective quality factor from an amplitude spectrum of an autocorrelation function of the time domain offset gather comprises:

establishing a fitted parabola of an amplitude spectrum of an autocorrelation function for the time domain offset gather;

solving a first derivative of the fitted parabola;

determining a frequency value corresponding to a point at which the first derivative of the fitted parabola is zero as a peak frequency;

calculating the effective quality factor according to the peak frequency.

In one embodiment, calculating the effective quality factor from the peak frequency comprises:

the effective quality factor is calculated according to the following formula:

in the above formula, Q_eff(t) is the effective quality factor, f_pIs the peak frequency, f_p0Is the dominant frequency of the seismic record, t is time, and pi is the circumferential frequency.

In one embodiment, determining a depth domain quality factor from the effective quality factor comprises:

performing kinematic ray tracing according to the seismic record and the seismic reflection interface to obtain analog values of inversion travel time and effective quality factors;

solving an inversion equation according to the effective quality factor, the inversion travel time and the analog value of the effective quality factor to obtain a correction value of the quality factor;

acquiring logging data of a target area;

determining an initial quality factor according to the logging data;

and correcting the initial quality factor by using the quality factor correction amount to obtain the depth domain quality factor.

In one embodiment, solving an inversion equation according to the effective quality factor, the inversion travel time, and the analog value of the effective quality factor to obtain a correction value of the quality factor includes:

solving the following inversion equation according to the effective quality factor, the inversion travel time and the analog value of the effective quality factor to obtain the correction value of the quality factor:

in the above formula, Q_eff(t) is the effective quality factor, t is when the performance travel is reached, Q_eff ^mod(t) is the analog value of the effective quality factor, ijk is the number of the inverse grid, t_ijkThe inverse travel time, numbered ijk, Ray is the Ray path,

the correction amount of the quality factor is designated ijk.

The embodiment of the present application further provides a device for determining a quality factor in a depth domain, including:

the first acquisition module is used for acquiring the seismic record of the target area;

the second acquisition module is used for acquiring a time domain migration gather and a seismic reflection interface according to the seismic record;

the autocorrelation processing module is used for carrying out autocorrelation processing on the time domain migration gather at the seismic reflection interface to obtain an autocorrelation function of the time domain migration gather;

a third obtaining module, configured to obtain an amplitude spectrum of an autocorrelation function of the time domain offset gather;

a first determining module, configured to determine an effective quality factor according to an amplitude spectrum of an autocorrelation function of the time domain offset gather;

and the second determining module is used for determining the quality factor of the depth domain according to the effective quality factor.

In one embodiment, the second acquisition module comprises the following structural units:

the first establishing unit is used for establishing a depth domain velocity model by using the seismic records;

the first determining unit is used for determining a depth migration profile according to the seismic records and the depth domain velocity model;

and the first acquisition unit is used for acquiring the seismic reflection interface according to the depth migration profile.

In one embodiment, the second obtaining module further comprises the following structural units:

the second establishing unit is used for establishing a depth relation according to the depth domain speed model;

and the conversion unit is used for carrying out deep time conversion on the depth domain migration gather according to the time-depth relation to obtain the time domain migration gather.

In the embodiment of the application, the autocorrelation processing is firstly carried out on the time domain migration gather, and the amplitude spectrum is obtained according to the processed autocorrelation function, so that the interference of noise in data is eliminated, and a more accurate amplitude spectrum is obtained; and determining the effective quality factor with higher precision by using the amplitude spectrum to determine the depth domain quality factor, thereby solving the technical problems of poor precision and lower resolution ratio of determining the depth domain quality factor in the existing method, and achieving the technical effects of obtaining the depth domain quality factor with high resolution ratio and high precision and improving the seismic exploration precision.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

Fig. 1 is a process flow diagram of a method for determining a depth domain quality factor according to an embodiment of the present application;

fig. 2 is a block diagram of a depth domain quality factor determination apparatus according to an embodiment of the present disclosure;

fig. 3 is a schematic structural diagram of an electronic device based on a method for determining a depth domain quality factor according to an embodiment of the present disclosure;

FIG. 4 is a schematic illustration of a seismic section obtained without offset imaging using depth domain quality factors in one example scenario;

FIG. 5 is a schematic diagram illustrating an amplitude spectrum obtained by applying the method and apparatus for determining a depth domain quality factor according to an embodiment of the present application in a scene example, and comparing the amplitude spectrum obtained by a conventional method with a Ricker wavelet;

FIG. 6 is a schematic diagram of a depth domain quality factor obtained by applying the method and apparatus for determining a depth domain quality factor provided by an embodiment of the present application in an example scenario;

FIG. 7 is a schematic diagram illustrating a comparison between a stacked profile of a prestack depth migration obtained by applying the method and apparatus for determining a depth domain quality factor provided by the embodiments of the present application and a stacked profile of a prestack depth migration obtained based on a conventional method in a scene example;

fig. 8 is a schematic diagram illustrating a comparison between a pre-stack depth migration co-imaging point gather obtained by applying the method and apparatus for determining a depth domain quality factor provided by the embodiment of the present application and a pre-stack depth migration co-imaging point gather obtained based on a conventional method in a scene example.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Considering that the existing method mostly acquires a time domain migration gather according to seismic data; extracting an amplitude spectrum by using the time domain offset gather; and further extracting peak frequency according to the amplitude spectrum to determine a specific depth domain quality factor. However, when the method is implemented, the accuracy of the acquired depth domain quality factor is relatively low because the depth domain quality factor is acquired directly based on the time domain migration gather and the interference of noise in seismic data is not eliminated; in addition, the spectral shape of the amplitude spectrum extracted from the time domain offset gather is different from the characteristics of the rake wavelet, and the amplitude spectrum does not completely conform to the characteristics of the rake wavelet, so that the resolution of the depth domain quality factor obtained based on the amplitude spectrum is relatively low. Therefore, when the depth domain quality factor is determined based on the existing method, the technical problems that the determined depth domain quality factor is poor in precision and low in resolution ratio often exist. For the root cause of the technical problem, the method considers that the autocorrelation processing can be performed on the acquired time domain offset gather first to eliminate the interference of noise and obtain a corresponding autocorrelation function; and then, an amplitude spectrum which is closer to a Rake wavelet is obtained based on the autocorrelation function to obtain a depth domain quality factor with higher resolution, so that the technical problems of poorer precision and lower resolution of the quality factor for determining the depth domain in the existing method are solved, and the technical effects of obtaining the depth domain quality factor with high resolution and high precision and improving the seismic exploration precision are achieved.

Based on the thought, the embodiment of the application provides a method for determining a depth domain quality factor. Please refer to fig. 1, which is a flowchart illustrating a method for determining a depth domain quality factor according to an embodiment of the present disclosure. The method for determining the quality factor of the depth domain provided by the embodiment of the present application may be implemented as follows.

S11: seismic records of the target area are acquired.

In this embodiment, the seismic record (also referred to as a time domain shot gather seismic record) may be specifically understood as a seismic data or seismic data. Of course, the seismic records listed above are only illustrative, and in particular, other types of seismic data may be acquired for subsequent determination of the depth-domain quality factor. The present application is not limited thereto.

In the present embodiment, it should be noted that a strong absorption region, such as a shallow cloud region, is experienced during the process of acquiring the seismic record of the target region, and since the wave is influenced by frictional heat generation and the like during the propagation in the region, the energy loss is relatively significant, that is, there is absorption attenuation. Furthermore, when the seismic data is used for offset imaging, the obtained offset profile often has the problems of abnormal amplitude, frequency and phase and the like, namely, the amplitude, the frequency and the wavelet form of the offset trace set and the offset profile of the seismic data can be changed, and further the problems of low resolution, wrong position of a same-direction axis and the like can be caused in the subsequent use. Thus, after seismic data is acquired, one may further determine a corresponding depth domain figure of merit (i.e., the ratio of total energy to absorbed energy in the seismic data) from such seismic data to characterize the energy attenuation due to absorption. Further, specific prestack depth migration imaging can be subsequently performed according to the quality factor so as to eliminate the influence on the seismic data caused by absorption attenuation.

In an embodiment, the acquiring of the seismic record of the target area may include the following steps: exciting seismic waves in the target area, and recording the acquired seismic wave data; and the acquired seismic wave data is subjected to data processing by conventional processing means to obtain the seismic record.

S12: and acquiring a time domain migration gather and a seismic reflection interface according to the seismic record.

In an embodiment, the obtaining of the seismic reflection interface according to the seismic record may include the following steps:

s1: establishing a depth domain velocity model by using the seismic records;

s2: determining a depth migration profile according to the seismic record and the depth domain velocity model;

s3: and acquiring a seismic reflection interface according to the depth migration profile.

In one embodiment, the acquiring a seismic reflection interface according to the depth migration profile may include: and according to the depth migration profile, carrying out similarity scanning through local write stacking to obtain the seismic reflection interface.

In one embodiment, after the seismic reflection interface is obtained, the interface dip of the seismic reflection interface may be obtained by plane wave decomposition according to the depth migration profile.

In an embodiment, the above acquiring a time domain migration gather according to the seismic record may include the following steps:

s1: establishing a depth domain velocity model by using the seismic records;

s2: determining a depth domain migration gather according to the seismic record and the depth domain velocity model;

s3: establishing a depth relation according to the depth domain speed model;

s4: and according to the time-depth relation, performing deep-time conversion on the depth domain migration gather to obtain the time domain migration gather.

In an embodiment, the deep time conversion of the depth domain offset gather according to the time-depth relationship may specifically be understood as: and respectively converting the data of the depth domain offset gather into corresponding time domains according to the conversion ratio determined by the time-depth relation, thereby obtaining the data of the corresponding time domain offset gather.

In an embodiment, the performing the deep time conversion on the depth domain offset gather according to the time-depth relationship may include: respectively carrying out deep time conversion on the data in the depth domain offset track set according to the following formula:

in the above equation, z may be specifically expressed as the depth of any sample point in the depth domain offset gather,

specifically, the time may be represented as an arithmetic average of the velocity values in the depth domain velocity model of the sampling point with the depth z, and t may be specifically represented as a time corresponding to the sampling point with the depth z after the conversion.

S13: and carrying out autocorrelation processing on the time domain migration gather at the seismic reflection interface to obtain an autocorrelation function of the time domain migration gather.

In one embodiment, the time domain migration trace gather is obtained according to the acquired seismic record, so that noise interference exists in the time domain migration trace gather; in addition, the amplitude spectrum directly extracted based on the time domain offset gather does not completely conform to the morphological characteristics of the rake wavelet. Therefore, the time domain offset gather is directly subjected to Fourier transform, so that an amplitude spectrum is extracted, and then the peak frequency is obtained based on the amplitude spectrum to determine the depth domain quality factor, which is lower in precision and poorer in resolution. In view of the above, in the present embodiment, the autocorrelation process may be performed on the time domain migration gather to eliminate the interference of noise and other impurities in the seismic record, so as to obtain the autocorrelation function of the time domain migration gather, so that the amplitude spectrum with higher accuracy and higher approximation degree to the rake wavelet may be extracted from the autocorrelation function of the time domain migration gather in the following.

In one embodiment, when the autocorrelation processing is performed on the time domain migrated gather at the seismic reflection interface to obtain the autocorrelation function of the time domain migrated gather, the autocorrelation processing may be performed on the time domain migrated gather according to the following formula:

in the above formula, c (u) may be specifically represented as an autocorrelation function of the time domain offset gather, u may be specifically represented as a delay time of autocorrelation, f (x) may be specifically represented as an amplitude value at a sampling point x in the time domain offset gather, x may be specifically represented as a sampling point in the time domain offset gather, and window may be specifically represented as a time window length of autocorrelation processing.

In one embodiment, a specific value of the time window length of the autocorrelation process may be set to a wavelet length. Accordingly, the delay time value of the autocorrelation may be set to be equal to or slightly greater than the number of sampling points in one wavelet length. It should be understood that the above-mentioned time window length of the autocorrelation process and the delay time of the autocorrelation are only for better explanation of the embodiments of the present application. In specific implementation, other suitable values may be set as the time window length of the autocorrelation process and the delay time of the autocorrelation process according to specific situations and accuracy requirements. The present application is not limited thereto.

S14: and acquiring an amplitude spectrum of an autocorrelation function of the time domain offset gather.

In one embodiment, after obtaining the autocorrelation function of the time domain offset gather, the autocorrelation function of the time domain offset gather may be used to obtain an amplitude spectrum with higher purity and closer to the spectral shape of the rake wavelet than the autocorrelation function of the time domain offset gather.

In an embodiment, the obtaining an amplitude spectrum of the autocorrelation function of the time domain offset gather may include the following steps:

s1: determining the position of the amplitude peak of the autocorrelation function of the time domain offset gather;

s2: and acquiring signal data with a preset length at the position of an amplitude peak value in the autocorrelation function of the time domain offset gather, and determining an amplitude spectrum of the autocorrelation function of the time domain offset gather according to the signal data with the preset length, wherein the preset length is a wavelet length.

In the present embodiment, it is generally considered that the amplitude spectrum directly acquired from the time domain offset gather has a certain similarity with the spectral characteristics of the amplitude spectrum of the rake wavelet, and therefore, the conventional method extracts the peak frequency using the amplitude spectrum based on the time domain offset gather to determine the depth domain quality factor. However, there is still a certain difference between the amplitude spectrum directly obtained from the time domain offset gather and the rake self-wave, so the accuracy of determining the depth domain quality factor based on the amplitude spectrum directly obtained from the time domain offset gather is relatively poor. It is further contemplated that the spectral shape of the signal within a wavelet length range around the peak of the amplitude based autocorrelation function will generally be closer to the rake wavelet after the autocorrelation process has been performed on the time domain offset gather. Therefore, in this embodiment, the autocorrelation function of the time domain offset gather is used instead of directly using the time domain offset gather to obtain the corresponding amplitude spectrum.

In this embodiment, it should be further noted that the preset length may be specifically set to be a wavelet length, so that not only the spectrum form of the extracted amplitude spectrum is closer to the rake wavelet, but also noise interference caused by cycle skip of autocorrelation calculation can be avoided, and thus the accuracy of the obtained amplitude spectrum is improved.

In the present embodiment, the amplitude spectrum obtained in the above manner is closer to the spectrum form of the rake wavelet, and more approximately satisfies the following expression of the rake wavelet:

in the above formula, pi may be specifically expressed as a circumferential ratio, f may be specifically expressed as a current frequency, and f_p0And in particular may be expressed as the dominant frequency of the wavelet excitation initial state.

S15: and determining an effective quality factor according to the amplitude spectrum of the autocorrelation function of the time domain migration gather.

In one embodiment, after obtaining the amplitude spectrum of the autocorrelation function of the time domain offset gather, in a specific implementation, the peak frequency may be extracted and obtained by using the amplitude spectrum of the autocorrelation function of the time domain offset gather; and calculating an effective quality factor by using the extracted peak frequency.

In one embodiment, in order to ensure that a peak frequency with high accuracy is obtained, when implemented, the specific peak frequency can be obtained by establishing a fitting parabola of the amplitude spectrum.

In an embodiment, the determining the effective quality factor according to the amplitude spectrum of the autocorrelation function of the time domain offset gather may include the following steps:

s1: establishing a fitted parabola of an amplitude spectrum of an autocorrelation function for the time domain offset gather;

s2: solving a first derivative of the fitted parabola;

s3: determining a frequency value corresponding to a point at which the first derivative of the fitted parabola is zero as a peak frequency;

s4: calculating the effective quality factor according to the peak frequency.

In one embodiment, the above-mentioned building a fitted parabola about the amplitude spectrum of the autocorrelation function of the time domain offset gather may be implemented by the following steps:

s1: smoothing and removing abnormal values of the amplitude spectrum of the autocorrelation function of the time domain migration gather to obtain a processed amplitude spectrum;

s2: extracting a first sampling point, a second sampling point and a third sampling point from the processed amplitude spectrum, wherein the first sampling point is a sampling point near an amplitude peak in the amplitude spectrum, and the second sampling point and the third sampling point are sampling points on two sides of the first sampling point respectively;

s3: and performing data fitting by using the data of the first sampling point, the second sampling point and the third sampling point to establish a fitting parabola of the amplitude spectrum of the autocorrelation function of the time domain offset gather.

In one embodiment, the frequency of the first sampling point is f_pAmplitude of A_p. The frequency of the second sampling point on the left side is f_p-1Amplitude of A_p-1(ii) a The frequency of the third Caiyan point on the right side is f_p+1Amplitude of A_p+1. Correspondingly, data fitting is carried out by utilizing the data of the first sampling point, the second sampling point and the third sampling point, and the interval [ f ] is established_p-1,f_p+1]The fitting parabola of the amplitude spectrum of the autocorrelation function of the inner time domain offset gather can be specifically expressed as:

A(f)＝A_p-1·l_p-1(f)+A_p·l_p(f)+A_p+1·l_p+1(f)

wherein the content of the first and second substances,

in the above formula, A (f) can be specifically expressed as a time domain offset gatherFor characterizing the interval f_p-1,f_p+1]Frequency versus amplitude.

Thus, the determination section [ f ] can be calculated by fitting a parabola to the amplitude spectrum of the autocorrelation function of the time domain offset gather_p-1,f_p+1]The amplitude corresponding to any frequency.

In one embodiment, the first derivative of the fitting parabola is solved, and in practical implementation, the peak frequency may be determined by solving a point where the first derivative of the first derivative (i.e. a (f)) of the fitting parabola with respect to the frequency f is zero, and determining a frequency value corresponding to the point where the first derivative is zero.

Specifically, the point at which the first derivative of the fitted parabola with respect to the frequency f is zero can be solved according to the following formula:

in the above formula, a (f) may be specifically expressed as a fitted parabolic function of the amplitude spectrum of the autocorrelation function of the time domain offset gather, and f may be specifically expressed as the frequency of the sampling point.

In one embodiment, the effective quality factor is calculated according to the peak frequency, and in practical implementation, the effective quality factor may be calculated according to the following formula:

in the above formula, Q_eff(t) may be expressed in particular as an effective quality factor, f_pMay be expressed in particular as said peak frequency, f_p0Specifically, it can be expressed as the dominant frequency of the seismic record, t specifically can be expressed as travel time, and pi specifically can be expressed as the circumference ratio.

S16: and determining a depth domain quality factor according to the effective quality factor.

In an embodiment, the determining the depth domain quality factor according to the effective quality factor may include the following steps:

s1: acquiring logging data of a target area;

s2: determining an initial quality factor according to the logging data;

s3: performing kinematic ray tracing according to the seismic record and the seismic reflection interface to obtain analog values of inversion travel time and effective quality factors;

s4: solving an inversion equation according to the effective quality factor, the inversion travel time and the analog value of the effective quality factor to obtain a correction value of the quality factor;

s5: and correcting the initial quality factor by using the quality factor correction amount to obtain the depth domain quality factor.

In this embodiment, the determining an initial quality factor according to the log data may specifically include: and setting a specific numerical value as an initial quality factor according to the logging data and combining with the seismic records. Of course, it should be noted that the above-listed method of obtaining and using well log data to determine an initial quality factor is merely illustrative. In the implementation, other auxiliary data besides logging data can be selected and obtained according to the specific situation and the precision requirement to set the initial quality factor. The present application is not limited thereto.

In this embodiment, the kinematic ray tracing may be specifically understood as a functional equation obtained according to the fermat principle, the huygens principle and a transmission theorem derived from the two principles, and then solving the functional equation to obtain a parameter for determining a correction amount of the quality factor, such as an inversion travel time and an analog value of an effective quality factor. The Fermat principle can specifically mean that in a continuous medium, a ray propagates along a path with a travel time of a stable value, and is the basis of a ray kinematics theory. The huygens principle may specifically refer to that a seismic wave forms a wave front in a propagation process, and the wave front at the next moment is a spherical wave envelope surface generated by taking each point on the wave front at the previous moment as a new source point, and a new wave front is continuously generated and propagated forward.

In one embodiment, in implementation, the correlation parameters of the partial differential equation of the equation function can be established and solved by the 4 th-order longge-kutta method in the mesh model through the above-mentioned kinematic ray tracing:

in the above formula, x and z may be specifically expressed as spatial positions, t may be specifically expressed as travel time, v may be specifically expressed as velocity, a may be specifically expressed as the angle of the ray to the z-axis,

can be expressed as the partial derivatives of velocity in the x and z directions, Q, respectively^-1Specifically, it can be expressed as the reciprocal of the initial quality factor (i.e. initial quality factor), t^*And may be specifically expressed as a signal attenuation amount.

The relationship between the signal attenuation and the analog value of the effective quality factor can be specifically expressed as the following form:

in the above formula, t can be specifically represented as travel time, Q_eff ^mod(t) may be expressed specifically as an analog value of the effective quality factor.

In the present embodiment, the description is given by the aboveThe kinematic ray tracing obtains a ray path and the travel time (namely travel time) of the ray in each traversed inversion grid on one hand; on the other hand by obtaining t^*(i.e., signal attenuation) to obtain an analog value of the effective quality factor

So that the physical parameters needed for solving the inversion equations subsequently are obtained.

In an embodiment, the solving of the inversion equation according to the effective quality factor, the inversion travel time, and the analog value of the effective quality factor to obtain the correction value of the quality factor may include the following steps:

in the above formula, Q_eff(t) may specifically be expressed as an effective quality factor, and t may specifically be expressed as travel time, Q_eff ^mod(t) may specifically be expressed as an analog value of the effective quality factor, ijk may specifically be expressed as the number of the inverse grid, t_ijkIn particular, the inverse travel time of the inverse grid numbered ijk, Ray in particular may be represented as a Ray path,

specifically, the correction amount may be expressed as a correction amount of the quality factor of the inversion grid numbered ijk.

In an embodiment, in the above step, the initial quality factor is corrected by using the quality factor correction amount to obtain the depth domain quality factor, and in a specific implementation, the initial quality factor may be corrected by using the following formula to obtain a depth domain quality factor with higher precision and better resolution:

in the above formula, Q_newAnd may be specifically expressed as a depth domain quality factor,

which may be expressed in particular as a correction quantity of the quality factor, Q_initAnd may be specifically expressed as an initial quality factor.

Compared with the prior art, the method has the advantages that the time domain migration gather is subjected to autocorrelation processing, the amplitude spectrum is obtained according to the processed autocorrelation function, the interference of noise in data is eliminated, and the more accurate amplitude spectrum is obtained; and determining the effective quality factor with higher precision by using the amplitude spectrum to determine the depth domain quality factor, thereby solving the technical problems of poorer precision and lower resolution of the determination of the depth domain quality factor in the existing method, and achieving the technical effects of obtaining the depth domain quality factor with high resolution and high precision to improve the seismic exploration precision.

In one embodiment, after determining the depth domain quality factor, in order to enable high-precision, high-resolution seismic exploration of a target region, the method may be further implemented by:

s1: performing prestack depth migration imaging according to the depth domain quality factor;

s2: and performing seismic exploration on the target area according to the result of the prestack depth migration imaging.

In the present embodiment, the above-mentioned exemplary use of performing prestack depth migration imaging according to the depth domain quality factor is only an illustrative example. In specific implementation, the determined depth domain quality factor can be applied to specific construction of seismic exploration in other aspects. The present application is not limited thereto.

From the above description, it can be seen that, in the method for determining a depth domain quality factor provided in the embodiment of the present application, the autocorrelation processing is performed on the time domain offset gather first, and the amplitude spectrum is obtained according to the processed autocorrelation function, so that the interference of noise in data is eliminated, and a more accurate amplitude spectrum is obtained; the effective quality factor with higher precision is determined by utilizing the amplitude spectrum to determine the depth domain quality factor, so that the technical problems of poor precision and lower resolution of the determination of the depth domain quality factor in the existing method are solved, and the technical effects of obtaining the depth domain quality factor with high resolution and high precision and improving the seismic exploration precision are achieved; and a fitting parabola of the amplitude spectrum of the autocorrelation function of the time domain offset gather is established, and the fitting parabola is utilized to extract the peak frequency so as to determine the effective quality factor, so that the accuracy of the picked peak frequency is improved, and the precision and the resolution of the determined depth domain quality factor are improved.

Based on the same inventive concept, an embodiment of the present invention further provides a device for determining a depth domain quality factor, as described in the following embodiments. Because the principle of solving the problem of the device for determining the quality factor in the depth domain is similar to the method for determining the quality factor in the depth domain, the implementation of the device for determining the quality factor in the depth domain can refer to the implementation of the method for determining the quality factor in the depth domain, and repeated details are omitted. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Referring to fig. 2, a block diagram of an apparatus for determining a depth domain quality factor according to an embodiment of the present application is shown, where the apparatus may specifically include: this structure will be specifically described below.

The first obtaining module 21 may be specifically configured to obtain a seismic record of a target area;

the second obtaining module 22 may be specifically configured to obtain a time domain migration gather and a seismic reflection interface according to the seismic record;

the autocorrelation processing module 23 may be specifically configured to perform autocorrelation processing on the time domain migration gather at the seismic reflection interface to obtain an autocorrelation function of the time domain migration gather;

a third obtaining module 24, specifically configured to obtain an amplitude spectrum of an autocorrelation function of the time domain offset gather;

a first determining module 25, specifically configured to determine an effective quality factor according to an amplitude spectrum of an autocorrelation function of the time domain offset gather;

the second determining module 26 may specifically be configured to determine a depth domain quality factor according to the effective quality factor.

In one embodiment, in order to obtain the seismic reflection interface according to the seismic record, the second obtaining module 22 may include the following structural units:

the first establishing unit can be specifically used for establishing a depth domain velocity model by using the seismic records;

the first determining unit can be specifically used for determining a depth migration profile according to the seismic record and the depth domain velocity model;

the first acquisition unit may be specifically configured to acquire the seismic reflection interface according to the depth migration profile.

In an embodiment, in order to acquire a time domain migration gather according to the seismic record, the second acquisition module 22 may further include the following structural units:

the first determining unit can be specifically used for determining a depth domain migration gather according to the seismic record and the depth domain velocity model;

the second establishing unit is specifically used for establishing a depth relation according to the depth domain speed model;

and the conversion unit may be specifically configured to perform deep-time conversion on the depth domain offset gather according to the time-depth relationship, so as to obtain the time domain offset gather.

In one embodiment, in order to perform autocorrelation processing on the time domain migrated gather at the seismic reflection interface to obtain an autocorrelation function of the time domain migrated gather, the autocorrelation processing module 23 may perform autocorrelation processing on the time domain migrated gather according to the following formula:

In one embodiment, in order to obtain an amplitude spectrum of the autocorrelation function of the time domain offset gather, the third obtaining module 24 may include the following structural units:

a second determining unit, which may be specifically configured to determine a position of an amplitude peak of the autocorrelation function of the time-domain offset gather;

the second obtaining unit may be specifically configured to obtain signal data of a preset length at a position of an amplitude peak in an autocorrelation function of the time domain offset gather, and determine an amplitude spectrum of the autocorrelation function of the time domain offset gather according to the signal data of the preset length, where the preset length is a wavelet length.

In one embodiment, in order to determine the effective quality factor according to the amplitude spectrum of the autocorrelation function of the time domain offset gather, the first determining module 25 may include the following structural units:

a third establishing unit, which can be specifically used for establishing a fitting parabola of the amplitude spectrum of the autocorrelation function of the time domain offset gather;

the first solving unit can be specifically used for solving a first derivative of the fitting parabola;

a third determining unit, configured to determine, as a peak frequency, a frequency value corresponding to a point where a first derivative of the fitted parabola is zero;

the calculation unit may be specifically configured to calculate the effective quality factor according to the peak frequency.

In one embodiment, in order to calculate the effective quality factor according to the peak frequency, the calculating unit may calculate the effective quality factor according to the following formula:

In one embodiment, in order to determine the depth domain quality factor according to the effective quality factor, the second determining module 26 may include the following structural units:

the third acquisition unit can be specifically used for acquiring logging data of a target area;

a fourth determining unit, which may be specifically configured to determine an initial quality factor according to the logging data;

the fourth obtaining unit is specifically configured to perform kinematic ray tracing according to the seismic record and the seismic reflection interface to obtain an analog value of an effective quality factor during inversion;

the second solving unit is specifically configured to perform inversion equation solving according to the effective quality factor, the inversion travel time, and the analog value of the effective quality factor to obtain a correction value of the quality factor;

the correction unit may be specifically configured to correct the initial quality factor by using the quality factor correction amount to obtain the depth domain quality factor.

In an embodiment, in order to solve the inversion equation according to the effective quality factor, the inversion travel time, and the analog value of the effective quality factor to obtain the correction value of the quality factor, when the second solving unit is implemented, the following inversion equations may be solved according to the effective quality factor, the inversion travel time, and the analog value of the effective quality factor to obtain the correction value of the quality factor:

in the above formula, Q_eff(t) may specifically be expressed as an effective quality factor, and t may specifically be expressed as travel time, Q_eff ^mod(t) may specifically be expressed as an analog value of the effective quality factor, ijk may specifically be expressed as the number of the inverse grid, t_ijkWhich may be expressed in particular as the inversion travel time of the inversion grid numbered ijk, Ray is the Ray path,

the correction amount of the quality factor of the inversion grid numbered ijk.

In one embodiment, in order to correct the initial quality factor by using the quality factor correction amount to obtain the depth domain quality factor, the correcting unit may obtain the depth domain quality factor by using the following formula:

In one embodiment, in order to enable more accurate and high-resolution seismic exploration for a target area, the device for determining a quality factor of a depth domain may further include a construction module, which is specifically configured to perform prestack depth migration imaging according to the quality factor of the depth domain; and then carrying out seismic exploration on the target area according to the result of the prestack depth migration imaging.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

It should be noted that, the systems, devices, modules or units described in the above embodiments may be implemented by a computer chip or an entity, or implemented by a product with certain functions. For convenience of description, in the present specification, the above devices are described as being divided into various units by functions, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.

Moreover, in the subject specification, adjectives such as first and second may only be used to distinguish one element or action from another element or action without necessarily requiring or implying any actual such relationship or order. References to an element or component or step (etc.) should not be construed as limited to only one of the element, component, or step, but rather to one or more of the element, component, or step, etc., where the context permits.

From the above description, it can be seen that, in the apparatus for determining a depth domain quality factor provided in the embodiment of the present application, the autocorrelation processing module performs autocorrelation processing on the time domain offset gather first, and the third obtaining module obtains the amplitude spectrum according to the processed autocorrelation function, so that interference of noise in data is eliminated, and a more accurate amplitude spectrum is obtained; the effective quality factor with higher precision is determined by the first determining module and the second determining module according to the amplitude spectrum to determine the quality factor of the depth domain, so that the technical problems of poor precision and low resolution of the quality factor of the depth domain determined by the conventional method are solved, and the technical effects of obtaining the quality factor of the depth domain with high resolution and high precision and improving the seismic exploration precision are achieved; and a fitting parabola of the amplitude spectrum of the autocorrelation function of the time domain offset gather is established through the autocorrelation processing module, and the peak frequency is extracted by utilizing the fitting parabola through the third acquisition module so as to determine the effective quality factor, so that the accuracy of the picked peak frequency is improved, and the precision and the resolution of the determined depth domain quality factor are improved.

The embodiment of the present application further provides an electronic device, which may specifically refer to a schematic structural diagram of the electronic device shown in fig. 3 and based on the method for determining the depth domain quality factor provided in the embodiment of the present application, where the electronic device may specifically include an input device 31, a processor 32, and a memory 33. The input device 31 may be specifically used for inputting seismic records of a target area. The processor 32 may be specifically configured to obtain a time domain migration gather and a seismic reflection interface from the seismic record; performing autocorrelation processing on the time domain migration gather at the seismic reflection interface to obtain an autocorrelation function of the time domain migration gather; obtaining an amplitude spectrum of an autocorrelation function of the time domain migration gather; determining an effective quality factor according to the amplitude spectrum of the autocorrelation function of the time domain migration gather; and determining a depth domain quality factor according to the effective quality factor. The memory 33 may be used in particular to store seismic records input via the input device 31, intermediate data generated during processing by the processor 32, and program execution instructions.

In this embodiment, the input device may be one of the main apparatuses for information exchange between a user and a computer system. The input device may include a keyboard, a mouse, a camera, a scanner, a light pen, a handwriting input board, a voice input device, etc.; the input device is used to input raw data and a program for processing the data into the computer. The input device can also acquire and receive data transmitted by other modules, units and devices. The processor may be implemented in any suitable way. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth. The memory may in particular be a memory device used in modern information technology for storing information. The memory may include multiple levels, and in a digital system, the memory may be any memory as long as it can store binary data; in an integrated circuit, a circuit without a physical form and with a storage function is also called a memory, such as a RAM, a FIFO and the like; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card and the like.

In this embodiment, the functions and effects specifically realized by the electronic device can be explained by comparing with other embodiments, and are not described herein again.

Also provided in an embodiment of the present application is a computer storage medium for a depth quality factor-based determination method, the computer storage medium storing computer program instructions that, when executed, implement: acquiring a seismic record of a target area; acquiring a time domain migration gather and a seismic reflection interface according to the seismic record; performing autocorrelation processing on the time domain migration gather at the seismic reflection interface to obtain an autocorrelation function of the time domain migration gather; obtaining an amplitude spectrum of an autocorrelation function of the time domain migration gather; determining an effective quality factor according to the amplitude spectrum of the autocorrelation function of the time domain migration gather; and determining a depth domain quality factor according to the effective quality factor.

In the present embodiment, the storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard disk (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.

In this embodiment, the functions and effects specifically realized by the program instructions stored in the computer storage medium can be explained by comparing with other embodiments, and are not described herein again.

In a specific implementation scenario, the method and the device for determining the depth domain quality factor according to the embodiment of the present application are applied to process seismic records acquired in a certain target area, so as to determine a corresponding depth domain quality factor, and further perform seismic exploration with higher precision on the target area. The specific implementation process can be executed by referring to the following contents.

In the present embodiment, it should be noted that, if the depth domain quality factor is not determined and imaging is performed directly using seismic data, the obtained seismic section may have amplitude, frequency, and phase anomalies. Reference may be made to FIG. 4, which is a schematic illustration of a seismic section obtained without offset imaging using depth domain quality factors in one example of a scenario.

(1) And (3) exciting seismic waves in the target area and recording the seismic waves, and processing the seismic data acquired in the field according to a conventional seismic data processing flow to obtain a conventionally processed time-domain shot gather seismic record (namely acquiring the seismic record of the target area).

(2) And (3) establishing a depth domain velocity model by using the seismic records obtained in the step (1).

(3) And (3) establishing an initial quality factor model (namely determining an initial quality factor according to the logging data) by using the seismic records or other auxiliary data (such as logging data) obtained in the step (1).

(4) And performing conventional prestack depth migration processing by using the time domain shot gather seismic record and the depth domain velocity model to obtain a depth migration gather and a depth migration profile.

(5) And automatically picking up the interface dip angle of the seismic reflection interface and the seismic interface according to the depth migration profile. The seismic reflection interface pickup can be specifically completed by performing similarity scanning through local slant stacking, and the interface dip angle can be specifically completed through methods such as plane wave decomposition.

(6) And according to the time-depth relation established by the depth domain speed model and the time-depth relation, performing depth-time conversion proportion on the depth domain offset gather to obtain the time domain offset gather. Wherein the formula for the deep time transition can be expressed in the form:

(7) And (4) at each seismic reflection interface obtained in the step (5), solving peak frequency by using the time domain signal (namely the time domain migration gather) obtained in the step (6), and further solving an effective quality factor.

In the present embodiment, it should be emphasized that the conventional method usually directly performs fourier transform on the time domain signal obtained in step (6) and then solves the amplitude spectrum. This is susceptible to noise and wavelet shape. In the present embodiment, the time domain signal obtained in step (6) is first subjected to autocorrelation processing, so that the influence caused by noise can be eliminated to a certain extent, and the autocorrelation signal can better conform to the form of a rake wavelet. The amplitude spectrum of the autocorrelation function is solved. In particular, a parabolic fitting method (i.e., fitting a parabola to the amplitude spectrum of the autocorrelation function of the time domain offset gather) can be used to accurately find the peak of the amplitude spectrum.

Based on the above idea, the specific implementation can be performed according to the following steps:

in the first step, a short time window is used, and the autocorrelation function of the time domain signal in the time window is solved. The specific solving formula is as follows:

In the second step, the amplitude spectrum of the seismic data after the autocorrelation processing is considered to be approximated by the amplitude spectrum of the rake wavelet. Therefore, after the time domain signal is subjected to autocorrelation, the shape of the signal within a wavelet length (i.e. a preset length) near the amplitude peak of the obtained autocorrelation function (i.e. the autocorrelation function of the time domain offset gather) is closer to the Rake wavelet. In order to make the signal closer to the Rake wavelet and to avoid noise caused by cycle hopping of the autocorrelation calculation, a wavelet length signal can be taken only in the vicinity of the amplitude peak of the autocorrelation function, and the amplitude spectrum can be calculated. Specifically, fig. 5 is a schematic diagram of an amplitude spectrum based on an autocorrelation function obtained by applying the method and apparatus for determining a depth domain quality factor provided by the embodiment of the present application in a scene example, and a comparison diagram of an amplitude spectrum obtained based on a conventional method and a rake wavelet. In the figure, the horizontal axis represents frequency, the vertical axis represents amplitude, the Real raw data in the figure represents an amplitude spectrum curve obtained based on the prior art, the Autocorrelation of raw data represents an amplitude spectrum curve based on an Autocorrelation function, the Synthetic Rickerwavelet represents an amplitude spectrum curve of a Rake wavelet, and the right-side curve is an Autocorrelation function curve of a signal. In the figure, a dotted line indicates an amplitude spectrum based on an autocorrelation function, a curve below the dotted line, which is closer to the shape of the dotted line, is a rake wavelet, and a curve above the dotted line, which has a large fluctuation, is an amplitude spectrum obtained by a conventional method. It can be known from the figure that the amplitude spectrum obtained based on the existing method not only has poor regularity, but also has larger morphological difference with the Rake wavelet, and the smoothness of the amplitude spectrum based on the autocorrelation function is obviously improved and basically coincides with the amplitude spectrum of the Rake wavelet.

The amplitude spectrum of the rake wavelet may be specifically represented as:

Thirdly, analyzing the distribution of the amplitude spectrum obtained in the second step, and performing certain operations such as smoothing and removing abnormal values on the amplitude spectrum; then, a peak point (i.e., a first sampling point) in the amplitude spectrum and one point (i.e., a second sampling point and a third sampling point) on the left and right sides of the peak point are found, and parabolic fitting is performed to perform fine sampling on the amplitude spectrum so as to establish a fitting function a (f) of a parabola (i.e., a fitting parabola of the amplitude spectrum of the autocorrelation function of the time domain offset gather).

In the present embodiment, when embodied, the following processing may be performed: obtaining a frequency of the assumed peak point as f_p', amplitude of A_p(ii) a And the frequency of the point to the left of its peak point is f_p-1Amplitude of A_p-1(ii) a The frequency of the right-hand point is f_p+1Amplitude of A_p+1(ii) a In the interval [ f_p-1,f_p+1]The amplitude of any one of the frequencies can be obtained by a parabolic fit as follows:

A(f)＝A_p-1·l_p-1(f)+A_p·l_p(f)+A_p+1·l_p+1(f) (4)

and fourthly, solving the first derivative of the function A (f) to the frequency f, finding out a zero point of the first derivative (namely solving the first derivative of the fitting parabola), and determining a frequency value corresponding to a point where the first derivative of the fitting parabola is zero as the peak frequency.

In this embodiment, the following formula can be used for implementation:

in the above formula, a (f) may be specifically expressed as a fitted parabolic function of the amplitude spectrum of the autocorrelation function of the time domain offset gather, and f may be specifically expressed as the frequency of the sampling point. Wherein, the frequency corresponding to the determined zero point is the peak frequency, which can be expressed as f_p。

And fifthly, solving the effective quality factor.

In this embodiment, in practice, the effective quality factor can be calculated according to the following formula:

(8) And performing kinematic ray tracing according to the velocity model, the initial quality factor model, the seismic reflection interface and the interface dip angle obtained in the previous step.

In the present embodiment, the kinematic ray tracing is mainly based on the equation of a function obtained by the fermat principle, the huygens principle, and the transmission theorem derived from the two, and the equation of the function is solved to determine the inversion parameters. The Fermat principle refers to that rays propagate along a path with stable travel time in a continuous medium, and is the basis of ray kinematics theory. The huygens principle means that a wave front is formed by seismic waves in a propagation process, and the wave front at the next moment is a spherical wave envelope surface generated by taking each point on the wave front at the previous moment as a new source point, and a new wave front is continuously generated and is propagated forwards.

In this embodiment, in specific implementation, the method for solving the partial differential equation in the mesh model by using the 4 th-order longge-kutta method may be implemented by using the kinematic ray tracing:

can be expressed as the partial derivatives of velocity in the x and z directions, Q, respectively^-1Which can be expressed specifically as the initial quality factor (i.e., initial quality factor)

Number, t^*And may be specifically expressed as a signal attenuation amount.

The relationship between the signal attenuation and the analog value of the effective quality factor can be specifically expressed as the following form

Formula (II):

By the ray tracing, on one hand, a ray path and the travel time of the ray in each traversed inversion grid (i.e. the inversion travel time) are obtained, and on the other hand, t is obtained^*I.e. obtaining an analog value of the effective quality factor

All the physical quantities (i.e. inversion parameters) which are then used to solve the Q-inversion equations are thus obtained.

(9) And (4) listing an inversion equation and solving the inversion equation to determine the correction value of the quality factor (namely, solving the inversion equation according to the effective quality factor, the inversion travel time and the analog value of the effective quality factor to obtain the correction value of the quality factor).

In this embodiment, the above inversion equation can be expressed in the following form:

in the above formula, Q_eff(t) may specifically be expressed as an effective quality factor, and t may specifically be expressed as travel time, Q_eff ^mod(t) may specifically be expressed as an analog value of the effective quality factor, ijk may specifically be expressed as a number (or index) of the inversion grid, t_ijkIn particular, the inverse travel time of the inverse grid numbered ijk, Ray in particular may be represented as a Ray path,

(10) And iteratively solving and updating the initial quality factor model to obtain a depth domain quality factor (namely, correcting the initial quality factor by using the quality factor correction value to obtain the depth domain quality factor).

In the present embodiment, the expression corresponding to the specific updating method (or correction method) is specifically expressed as follows:

in the above formula, Q_newIn particular a depth domain quality factor (also referred to as Q-model),

Specifically, fig. 6 is a schematic diagram of a depth domain quality factor (or Q model) obtained by applying the method and apparatus for determining a depth domain quality factor provided by the embodiment of the present application in an exemplary scenario. As can be seen from the figure, the Q model obtained by the tomography inversion of the invention can be accurately positioned to the position of the abnormal body, and the high-resolution result is inverted.

(11) And performing seismic exploration on the target area according to the determined depth domain quality factor.

In this embodiment, in a specific implementation, pre-stack depth migration imaging (obtaining a common imaging point gather of a stack profile of pre-stack depth migration and pre-stack depth migration) may be performed according to the depth domain quality factor; and then carrying out seismic exploration on the target area according to the result of the prestack depth migration imaging.

Specifically, a schematic diagram of a comparison between a pre-stack depth migration overlay section obtained by applying the method and apparatus for determining a depth domain quality factor provided by the embodiment of the present application and a pre-stack depth migration overlay section obtained based on the existing method shown in fig. 7, and a schematic diagram of a pre-stack depth migration co-imaging point gather obtained by applying the method and apparatus for determining a depth domain quality factor provided by the embodiment of the present application and a pre-stack depth migration co-imaging point gather obtained based on the existing method shown in fig. 8 can be referred to. As can be seen from the figure, when the Q model obtained in the embodiment of the present application is used to perform the Q prestack depth migration, the migration profile and the migration gather are compared with the conventional migration profile and migration gather, the obtained result energy is more uniform, the resolution is higher, and the amplitude missing condition below the abnormal region is greatly improved.

According to the scene example, the method and the device for determining the depth domain quality factor provided by the embodiment of the application are verified, the time domain offset gather is subjected to autocorrelation processing, and the amplitude spectrum is obtained according to the processed autocorrelation function, so that the interference of noise in data is eliminated, and a more accurate amplitude spectrum is obtained; and then, the effective quality factor with higher precision is determined by utilizing the amplitude spectrum to determine the depth domain quality factor, so that the technical problems of poor precision and lower resolution in determining the depth domain quality factor in the existing method are solved, and the technical effects of obtaining the depth domain quality factor with high resolution and high precision and improving the seismic exploration precision are achieved.

Although various specific embodiments are mentioned in the disclosure of the present application, the present application is not limited to the cases described in the industry standards or the examples, and the like, and some industry standards or the embodiments slightly modified based on the implementation described in the custom manner or the examples can also achieve the same, equivalent or similar, or the expected implementation effects after the modifications. Embodiments employing such modified or transformed data acquisition, processing, output, determination, etc., may still fall within the scope of alternative embodiments of the present application.

Although the present application provides method steps as described in an embodiment or flowchart, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an apparatus or client product in practice executes, it may execute sequentially or in parallel (e.g., in a parallel processor or multithreaded processing environment, or even in a distributed data processing environment) according to the embodiments or methods shown in the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded.

The devices or modules and the like explained in the above embodiments may be specifically implemented by a computer chip or an entity, or implemented by a product with certain functions. For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the present application, the functions of each module may be implemented in one or more pieces of software and/or hardware, or a module that implements the same function may be implemented by a combination of a plurality of sub-modules, and the like. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed.

Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, or the like, and includes several instructions for enabling a computer device (which may be a personal computer, a mobile terminal, a server, or a network device) to execute the method according to the embodiments or some parts of the embodiments of the present application.

The embodiments in the present specification are described in a progressive manner, and the same or similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

While the present application has been described by way of examples, those of ordinary skill in the art will appreciate that there are numerous variations and permutations of the present application that do not depart from the spirit of the present application and that the appended embodiments are intended to include such variations and permutations without departing from the present application.

Claims

1. A method for determining a depth domain quality factor, comprising:

acquiring a seismic record of a target area;

determining a depth domain quality factor according to the effective quality factor;

wherein obtaining a time domain migration gather from the seismic record comprises: establishing a depth domain velocity model by using the seismic records; determining a depth domain migration gather according to the seismic record and the depth domain velocity model; establishing a depth relation according to the depth domain speed model; and according to the time-depth relation, performing deep-time conversion on the depth domain migration gather to obtain the time domain migration gather.

2. The method of claim 1, wherein obtaining a seismic reflection interface from the seismic record comprises:

establishing a depth domain velocity model by using the seismic records;

3. The method of claim 1, wherein auto-correlating the time domain migrated gathers at the seismic reflection interface to obtain an auto-correlation function for the time domain migrated gathers, comprises:

4. The method of claim 1, wherein obtaining an amplitude spectrum of an autocorrelation function of the time domain offset gather comprises:

5. The method of claim 1, wherein determining an effective quality factor from an amplitude spectrum of an autocorrelation function of the time domain offset gather comprises:

solving a first derivative of the fitted parabola;

calculating the effective quality factor according to the peak frequency.

6. The method of claim 5, wherein calculating the effective quality factor from the peak frequency comprises:

the effective quality factor is calculated according to the following formula:

in the above formula, Q_eff(t) is the effective quality factor, f_pIs the peak frequency, f_p0The main frequency of the seismic record, t is travel time, and pi is the circumferential frequency.

7. The method of claim 1, wherein determining a depth domain quality factor based on the effective quality factor comprises:

acquiring logging data of a target area;

determining an initial quality factor according to the logging data;

8. The method of claim 7, wherein solving an inversion equation according to the effective quality factor, the inversion travel time, and the analog value of the effective quality factor to obtain a correction amount of the quality factor comprises:

in the above formula, Q_eff(t) is the effective quality factor, t is the travel time,Q_eff ^mod(t) is the analog value of the effective quality factor, ijk is the number of the inverse grid, t_ijkThe inverse travel time of the inverse grid numbered ijk, Ray is the Ray path,

the correction amount of the quality factor of the inversion grid numbered ijk.

9. An apparatus for determining a depth domain quality factor, comprising:

a second determining module, configured to determine a depth domain quality factor according to the effective quality factor;

the second obtaining module is specifically configured to obtain a time domain migration gather according to the seismic record, and includes: establishing a depth domain velocity model by using the seismic records; determining a depth domain migration gather according to the seismic record and the depth domain velocity model; establishing a depth relation according to the depth domain speed model; and according to the time-depth relation, performing deep-time conversion on the depth domain migration gather to obtain the time domain migration gather.

10. The apparatus of claim 9, wherein the second obtaining module comprises:

11. The apparatus of claim 10, wherein the second obtaining module further comprises: