Pre-stack gather optimization method based on dynamic image deformation

Abstract

The invention provides a pre-stack gather optimization method based on dynamic image deformation. The pre-stack gather optimization method based on dynamic image deformation comprises the following steps: step 1, inputting seismic common imaging point pre-stack gather data needing to be optimized; step 2, carrying out shallow layer far offset gather distortion removal; step 3, performing energy balance processing on the common imaging point pre-stack gather channel by channel; 4, performing dynamic image deformation processing on the preprocessed seismic gather and reference gather, and obtaining a total time shift amount after multiple iterations; and step 5, applying the time shift amount to a common imaging point pre-stack seismic gather needing to be optimized to obtain a leveled seismic gather. According to the pre-stack gather optimization method based on dynamic image deformation, the seismic gather time shifting amount is calculated based on the global optimum theory, the method is not limited by a time window, the method has the advantages of being high in noise immunity and the like, and the problems of pre-stack gather residual time difference correction and wavelet stretching correction can be rapidly and accurately solved.

Description

Technical Field

[0001] The invention relates to the technical field of geophysical processing, and in particular to a pre-stack gather optimization method based on dynamic image deformation. Background Art

[0002] For the problem of residual time difference or wavelet correction stretching in prestack gathers, the bending of seismic phase axis can be used to automatically perform velocity analysis to make it flat, and then merge the residual velocity field with the original processing velocity field. This method has poor stability and requires a large sliding time window to make it stable. The amount of calculation is large and its vertical resolution is also poor. Moreover, it is unrealistic to repeat the conventional seismic processing for a long time to repeat the residual velocity analysis, which is not allowed in time. Another processing method is the cross-correlation comparison method, which is to stack the middle offset of the best signal-to-noise ratio in the gather to form a model trace, and then cross-correlate each part of the stack and the model trace, and calculate the time displacement by sliding the time window from shallow to deep, and then smooth the longitudinal and lateral time displacement of each offset gather, and finally apply it to the corresponding offset data volume to form a corrected flattened data volume. However, the cross-correlation comparison method is a local algorithm. When the time difference is large or the wavelet distortion is serious, the overall gather correction effect is not good, and its results are difficult to meet the needs of reservoir prediction such as AVO attribute analysis and seismic prestack inversion.

[0003] In the Chinese patent application with application number: CN201610665776.4, a method and system for seismic trace imaging is involved. The method may include: based on the common center point trace gather and the seismic velocity field, obtaining the angle domain common imaging point trace gather and the offset domain common imaging point trace gather through pre-stack migration; based on the angle domain common imaging point trace gather and the offset domain common imaging point trace gather, obtaining the processed angle domain common imaging point trace gather stacking section and the processed offset domain common imaging point trace gather stacking section through stretching and cutting processing; based on the processed angle domain common imaging point trace gather stacking section and the processed offset domain common imaging point trace gather stacking section, obtaining the difference section through coefficient weighting and difference calculation; and obtaining the final common imaging point trace gather based on the weighted combination of the processed angle domain common imaging point trace gather stacking section and the processed offset domain common imaging point trace gather stacking section and the difference section calculation.

[0004] In the Chinese patent application with application number: CN201410144181.5, a multi-scale anisotropic diffusion filtering method based on pre-stack CRP gathers is involved, which includes the following steps: 1) regularizing the input pre-stack CRP gathers; 2) using a two-dimensional Mallat algorithm to perform multi-scale decomposition, and initializing each sub-profile after decomposition; 3) obtaining the diffusion threshold value by the diffusion coefficient method, and entering step 6); performing anisotropic diffusion based on the diffusion tensor method to obtain the diffusion tensor parameters, substituting them into the nonlinear anisotropic equation for anisotropic diffusion filtering, and entering step 4); 4) calculating the SNR, MSE and PSNR of the sub-profile after each iteration, and selecting the best seismic sub-profile; 5) performing step 4) on each sub-profile after anisotropic diffusion filtering, and entering step 6); 6) using a two-dimensional Mallat reconstruction algorithm to losslessly reconstruct the pre-stack CRP gathers of each sub-profile information after iteration, and outputting the best pre-stack CRP gathers after reconstruction. The invention can be widely used in the processing of various oil exploration seismic data.

[0005] In the Chinese patent application with application number: CN201810621833.8, a method and system for automatic leveling of seismic gathers is involved. The method may include: step 1: performing distortion removal and energy balance processing on the seismic gathers to obtain the initial gathers; step 2: obtaining reference gathers according to the initial gathers, copying the reference gathers, and obtaining the reference gathers; step 3: calculating the two-dimensional time shift between the reference gathers and the initial gathers, performing time shift processing on the initial gathers according to the two-dimensional time shift, and obtaining the deformed gathers; step 4: judging whether the two-dimensional time shift is less than the time shift threshold, if so, proceeding to step 5, if not, taking the deformed gathers as the initial gathers, and repeating steps 3-4; step 5: adding the two-dimensional time shifts obtained in each iteration to obtain the total time shift, performing time shift processing on the seismic gathers, and obtaining the leveled gathers. The invention can automatically and quickly complete the leveling of seismic gathers after conventional processing, and provide high-quality seismic gathers for later seismic prestack inversion, reservoir prediction, and fluid identification.

[0006] In the Chinese patent application with application number: CN202111071173.9, a method and device for optimizing seismic pre-stack data based on an improved BEMD algorithm are involved. The inventive method uses an improved BEMD algorithm and an adaptive denoising algorithm to decompose the pre-stack data gathers into characteristic signals of different scales. Then, a threshold-based orthogonal wavelet transform denoising is performed on each component to remove most of the noise. Then, the correlation coefficient between each component and the original data is obtained, and the data is reconstructed based on the correlation coefficient. The effective signal is retained to the greatest extent, the interference of the noise signal is removed, the signal-to-noise ratio of the pre-stack data gathers is improved, and a good data foundation is provided for subsequent earthquake prediction algorithms.

[0007] The above existing technologies are all significantly different from the present invention and fail to solve the technical problem we want to solve. Therefore, we have invented a new pre-stack gather optimization method based on dynamic image deformation. Summary of the invention

[0008] The purpose of the present invention is to provide a pre-stack gather optimization method based on dynamic image deformation, which can quickly and accurately solve the problems of residual moveout correction and wavelet stretching correction of pre-stack gathers.

[0009] The object of the present invention can be achieved by the following technical measures: a pre-stack gather optimization method based on dynamic image deformation, the pre-stack gather optimization method based on dynamic image deformation comprises:

[0010] Step 1: Input the pre-stack gather data of the seismic common imaging points to be optimized;

[0011] Step 2: Perform distortion removal of shallow far-offset gathers;

[0012] Step 3: Perform energy balancing on the pre-stack gathers of the common imaging point one by one;

[0013] Step 4: Perform dynamic image deformation processing on the pre-processed seismic gathers and the reference gathers, and obtain the total time shift after multiple iterations;

[0014] Step 5: Apply the total time shift to the pre-stack seismic gathers of the common imaging points that need to be optimized to obtain the leveled seismic gathers.

[0015] The purpose of the present invention can also be achieved by the following technical measures:

[0016] In step 1, the pre-stack gather data of common imaging points to be optimized in the time domain or depth domain are input.

[0017] In step 2, shallow far-offset gathers are generally distorted. Double-pass reflection travel time-offset data pairs are used to form excision lines to perform distortion-free excision of pre-stack gathers of common imaging points in the time domain or depth domain.

[0018] In step 3, energy equalization processing is performed on each of the pre-stack traces of the common imaging point in the time domain or the depth domain to enhance the weak formation reflection energy. The method uses a sliding time window to calculate the root mean square amplitude.

[0019] In step 4, the reference gather is calculated using the near-offset gather, and dynamic image deformation processing is performed on the preprocessed seismic gather and the reference gather to obtain the first deformation time-shift. If the time-shift does not meet the iteration termination condition at this time, the estimated time-shift is applied to time-shift the seismic gather to obtain the time-shifted seismic gather, and then the next time-shift is estimated until the time-shift is small enough to meet the iteration termination condition and terminate the iteration, and finally the total time-shift is obtained.

[0020] In step 4, the dynamic graphics deformation is achieved by solving an optimization problem. Given two digital sequences f[i] and g[i], the integer time-shift sequence u[0:N-1] is calculated to find an optimal time-shift amount so that the energy sum of the error matrix after the time-shift is minimized.

[0021] In step 4, the objective function of the optimization problem is formula (1):

[0022]

[0023] Where i is the sample number, l is the sample length, and N is the number of samples.

[0024] In step 4, the constraint condition is formula (2), that is, the relative time shift of two corresponding time points is less than or equal to a certain value:

[0025] |u[i]-u[i-1]|≤1 (2).

[0026] In step 4, the dynamic time deformation of the optimization problem is extended to the multi-dimensional case, which is called the dynamic image deformation method. This method estimates the time shift by making time shift constraints in the vertical and horizontal directions. At this time, the constraint formula (2) is expanded to formula (3)

[0027] |u(i,j)-u(i,j-1)|≤1

[0028] |u(i,j)-u(i-1,j)|≤1 (3)

[0029] Here i represents the seismic reflection travel time, and j represents the CDP point number.

[0030] In step 5, the original common imaging point gathers in the time domain or depth domain are subjected to dynamic image deformation and time shift according to the total time shift amount in step 4 to obtain optimized common imaging point pre-stack seismic gathers.

[0031] The pre-stack gather optimization method based on dynamic image deformation in the present invention is a new method for quickly realizing seismic pre-stack gather optimization, which introduces the dynamic image deformation technology in image processing into the pre-stack gather processing of seismic data; the method calculates the time shift of seismic gathers based on the global optimal theory, is not limited by the time window, has the characteristics of strong noise resistance, etc., and can quickly and accurately solve the problems of residual time difference correction and wavelet stretching correction of pre-stack gathers. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of a specific embodiment of the pre-stack gather optimization method based on dynamic image deformation of the present invention;

[0033] Figure 2 A schematic diagram of the application effect of the dynamic image deformation method on the theoretical synthesis record in a specific embodiment of the present invention;

[0034] Figure 3 A schematic diagram of a seismic pre-stack gather that needs to be optimized is provided in a specific embodiment of the present invention;

[0035] Figure 4 It is a schematic diagram of a seismic pre-stack gather optimized by a dynamic image deformation method in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0036] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0037] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations and / or combinations thereof.

[0038] like Figure 1 As shown, Figure 1 The flowchart of the pre-stack gather optimization method based on dynamic image deformation of the present invention. The pre-stack gather optimization method based on dynamic image deformation includes:

[0039] Step S1: inputting the pre-stack gather data of the seismic common imaging point to be optimized;

[0040] Step S2: performing distortion removal of shallow far-offset gathers;

[0041] Step S3: performing energy balancing processing on the pre-stack gathers of the common imaging point one by one;

[0042] Step S4: Calculate the reference gather using the near-offset gather, perform dynamic image deformation processing on the pre-processed seismic gather and the reference gather, and obtain the first deformation time shift. If the time shift does not meet the iteration termination condition at this time, then apply the estimated time shift to time shift the seismic gather to obtain the time-shifted seismic gather, and then estimate the next time shift until the time shift is small enough to meet the iteration termination condition and terminate the iteration, and finally obtain the total time shift.

[0043] Step S5: Apply the time shift to the pre-stack seismic gathers that need to be optimized to obtain the leveled seismic gathers, thereby achieving the purpose of gather optimization.

[0044] This method calculates the time shift of seismic pre-stack gathers based on the global optimal theory. It is not limited by the time window and has the characteristics of strong noise resistance. It can quickly and accurately solve the problems of residual time difference correction and wavelet stretching correction of pre-stack gathers.

[0045] The following are several specific embodiments of the present invention.

[0046] Example 1

[0047] In a specific embodiment 1 of the present invention, the pre-stack gather optimization method based on dynamic image deformation includes the following steps:

[0048] Step 1: Input the pre-stack gather data of the common imaging points that need to be optimized; that is, input the pre-stack gather data of the common imaging points that need to be optimized in the time domain or depth domain.

[0049] Step 2: Perform distortion removal of shallow far-offset gathers; Performing distortion removal of shallow far-offset gathers is beneficial for calculating time shift. Shallow far-offset gathers generally have distortion. Double-pass reflection travel time-offset data pairs are used to form removal lines to perform distortion removal of pre-stack gathers of common imaging points in the time domain or depth domain.

[0050] Step 3: Energy equalization is performed on the pre-stack traces of the common imaging points one by one, which is beneficial for calculating the time shift. Energy equalization is performed on the pre-stack traces of the common imaging points in the time domain or depth domain one by one to improve the weak formation reflection energy. The method uses a sliding time window to calculate the root mean square amplitude.

[0051] Step 4: Calculate the reference gather using the near-offset gather, perform dynamic image deformation on the preprocessed seismic gather and the reference gather, and obtain the first deformation time shift. If the time shift does not meet the iteration termination condition at this time, then apply the estimated time shift to time shift the seismic gather to obtain the time-shifted seismic gather, and then estimate the next time shift until the time shift is small enough to meet the iteration termination condition and terminate the iteration, and finally obtain the total time shift.

[0052] The dynamic image deformation method for calculating the time shift specifically includes:

[0053] Dynamic graphics deformation is achieved by solving an optimization problem to calculate the integer time-shift sequence u[0:N-1] given two digital sequences f[i] and g[i]. Here, the objective function of the optimization problem is formula (1), that is, solving an optimal time shift amount so that the energy sum of the error matrix after time shift is minimized.

[0054]

[0055] Where i is the sample number, l is the sample length, and N is the number of samples.

[0056] The constraint condition is formula (2), that is, the relative time shift of two corresponding time points is less than or equal to a certain value:

[0057] |u[i]-u[i-1]|≤1 (2)

[0059] The dynamic time deformation of the optimization problem is extended to the multi-dimensional case, which is called the dynamic image deformation method. This method estimates the time shift by making time shift constraints in the vertical and horizontal directions. At this time, the constraint condition formula (2) is expanded to formula (3). This method has good noise resistance and is not prone to sudden changes in the time shift.

[0060] |u(i,j)-u(i,j-1)|≤1

[0061] |u(i,j)-u(i-1,j)|≤1 (3)

[0062] Here i represents the seismic reflection travel time, and j represents the CDP point number.

[0063] Step 5: Apply the time shift to the common imaging point seismic pre-stack gathers that need to be optimized to obtain the leveled seismic gathers, which can quickly and accurately solve the pre-stack gather residual time difference correction and wavelet stretching correction problems, thereby achieving the purpose of gather optimization.

[0064] For the original common imaging point gathers in the time domain or depth domain, dynamic image deformation and time shift are performed according to the total time shift amount in step 4 to obtain the optimized common imaging point seismic pre-stack gathers.

[0065] Example 2

[0066] In the specific embodiment 2 of the present invention, Figure 2 The present invention provides an application effect of the dynamic image deformation method on the theoretical synthesis record. Figure 2 a is a theoretical synthetic record, the main frequency of the seismic wavelet is 125Hz, the main period is 8ms, and 50% random noise is added, that is, the signal-to-noise ratio is 2:1. Figure 2 The time shift in b (maximum time shift is two main periods) is generated after deformation Figure 2 c; Estimate the time shift of the seismic profile before and after the image deformation, and obtain the image deformation of the vertical time shift calculated by the dynamic time algorithm ( Figure 2 d) Dynamic time algorithm calculates the image deformation with time shift constraints in vertical and horizontal directions to estimate the time shift amount ( Figure 2 e). It can be seen that for noisy seismic profiles, the time shift estimated by the transverse image deformation method is prone to cause lateral mutations. The longitudinal and transverse dynamic image deformation method provided by the present invention has a better application effect, which is consistent with the known time shift ( Figure 2 b) roughly the same.

[0067] Example 3

[0068] In specific embodiment 3 of the present invention, Figure 3 It is necessary to optimize the seismic pre-stack gathers to solve the problems of uneven event axes and long-channel wavelet stretching on the gathers. Figure 4 The invention relates to a seismic pre-stack gather optimized based on a dynamic image deformation method. The method effectively solves the problems of pre-stack gather event flattening correction and wavelet stretching correction, and maintains the relative energy relationship of the seismic data in the vertical and horizontal directions, which is more conducive to the needs of reservoir prediction such as AVO attribute analysis and seismic pre-stack inversion.

[0069] The present invention is a new method for rapidly optimizing seismic pre-stack gathers, which introduces the dynamic image deformation technology in image processing into the processing of seismic data pre-stack gathers. The present invention calculates the time shift of seismic gathers based on the global optimal theory, is not restricted by the time window, has the characteristics of strong noise resistance, and can rapidly and accurately solve the problems of residual time difference correction and wavelet stretching correction of pre-stack gathers.

[0070] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0071] Except for the technical features described in the specification, all other technical features are known technologies to those skilled in the art.

Claims

1. A pre-stack gather optimization method based on dynamic image deformation, characterized in that: The pre-stack gather optimization method based on dynamic image deformation includes: Step 1: Input the pre-stack gather data of the seismic common imaging points to be optimized; Step 2: Perform distortion removal of shallow far-offset gathers; Step 3: Perform energy balancing on the pre-stack gathers of the common imaging point one by one; Step 4: Perform dynamic image deformation processing on the pre-processed seismic gathers and the reference gathers, and obtain the total time shift after multiple iterations; Step 5: Apply the total time shift to the pre-stack seismic gathers of the common imaging points that need to be optimized to obtain the leveled seismic gathers.

2. The pre-stack gather optimization method based on dynamic image deformation according to claim 1, characterized in that: In step 1, the pre-stack gather data of common imaging points to be optimized in the time domain or depth domain are input.

3. The pre-stack gather optimization method based on dynamic image deformation according to claim 1, characterized in that: In step 2, shallow far-offset gathers are generally distorted. Double-pass reflection travel time-offset data pairs are used to form excision lines to perform distortion-free excision of pre-stack gathers of common imaging points in the time domain or depth domain.

4. The pre-stack gather optimization method based on dynamic image deformation according to claim 1, characterized in that: In step 3, energy equalization processing is performed on each of the pre-stack traces of the common imaging point in the time domain or the depth domain to enhance the weak formation reflection energy by calculating the root mean square amplitude using a sliding time window.

5. The pre-stack gather optimization method based on dynamic image deformation according to claim 1, characterized in that: In step 4, the reference gather is calculated using the near-offset gather, and dynamic image deformation processing is performed on the preprocessed seismic gather and the reference gather to obtain the first deformation time-shift. If the time-shift does not meet the iteration termination condition at this time, the estimated time-shift is applied to time-shift the seismic gather to obtain the time-shifted seismic gather, and then the next time-shift is estimated until the time-shift is small enough to meet the iteration termination condition and terminate the iteration, and finally the total time-shift is obtained.

6. The pre-stack gather optimization method based on dynamic image deformation according to claim 5, characterized in that: In step 4, the dynamic graphics deformation is achieved by solving an optimization problem. Given two digital sequences f[i] and g[i], the integer time-shift sequence u[0:N-1] is calculated to find an optimal time-shift amount so that the energy sum of the error matrix after the time-shift is minimized.

7. The pre-stack gather optimization method based on dynamic image deformation according to claim 6, characterized in that: In step 4, the objective function of the optimization problem is formula (1): Where i is the sample number, l is the sample length, and N is the number of samples.

8. The pre-stack gather optimization method based on dynamic image deformation according to claim 7, characterized in that: In step 4, the constraint condition is formula (2), that is, the relative time shift of two corresponding time points is less than or equal to a certain value: |u[i]-u[i-1]|≤1 (2).

9. The pre-stack gather optimization method based on dynamic image deformation according to claim 8, characterized in that: In step 4, the dynamic time deformation of the optimization problem is extended to the multi-dimensional case, which is called the dynamic image deformation method. This method estimates the time shift by making time shift constraints in the vertical and horizontal directions. At this time, the constraint condition formula (2) is expanded to formula (3): |u(i,j)-u(i,j-1)|≤1 |u(i,j)-u(i-1,j)|≤1 (3) Here i represents the seismic reflection travel time, and j represents the CDP point number.

10. The pre-stack gather optimization method based on dynamic image deformation according to claim 1, characterized in that: In step 5, the original common imaging point gathers in the time domain or depth domain are subjected to dynamic image deformation and time shift according to the total time shift amount in step 4 to obtain optimized common imaging point pre-stack seismic gathers.

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