Imaging domain least squares migration method, device, electronic equipment and medium

Abstract

The application discloses an imaging domain least square migration method, device, electronic equipment and medium, wherein the migration method comprises the following steps: acquiring reverse time migration results and a global space-varying point spread function, and performing regional blocking on the reverse time migration results and the global space-varying point spread function to form a plurality of block data; calculating a spatial domain least square migration result of each block data; and according to a regional blocking mode, reducing the spatial domain least square migration result of each block data to a complete model scale to obtain a global space-varying least square migration result. The application performs regional blocking on the reverse time migration results and the global space-varying point spread function, solves the problem that the space-invariant point spread function cannot effectively consider the space-varying characteristics of the point spread function in the current imaging domain least square reverse time migration, and provides an imaging result with better amplitude fidelity and higher quality, thereby promoting the practicality of the imaging domain least square reverse time migration.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic wave imaging, and more particularly to an imaging domain least square migration method and device, an electronic equipment and a medium. BACKGROUND

[0002] The least square migration converts the classical seismic migration into a linear optimization problem by establishing an error functional of data matching, and thus corrects the conventional migration result by using Hessian information, which is the highest precision seismic high resolution imaging technology at present. In practical application, the method needs a large amount of numerical simulation calculation to realize the migration iteration. At the same time, the seismic wavelet cannot be accurately extracted, the initial model precision is limited, and the like, which will significantly reduce the imaging resolution and quality, so the popularization and application of the data domain least square migration still has certain challenges.

[0003] The imaging domain least square migration can directly obtain the least square migration result without iteration by constructing a reasonable approximation of the Hessian operator. Since the scale of the Hessian is too large, it is not advisable to directly obtain the Hessian inverse. The point spread function is an element in the Hessian matrix, which physically describes the Hessian effect of a single scattering point on the underground space, but the point spread function of a single point cannot accurately depict the complete Hessian information, so if the least square migration result generated in this way can only be accurately inverted in the neighborhood of the scattering point, the space variation characteristics of the velocity model and the illumination condition cannot be effectively considered. Therefore, it is required to adopt a reasonable sparse sampling strategy in the inversion to ensure that the wave field of adjacent scattering points does not interfere, and to increase the distribution density of the underground scattering points as much as possible. However, the current imaging domain least square migration algorithm is still based on the global space invariant point spread function.

[0004] The least square migration mainly includes two routes of data domain implementation and imaging domain implementation. The data domain least square migration realizes the best fitting of the simulated data and the observed data by iterative solution, but its calculation cost is huge, and the convergence speed is too slow, so there are still certain difficulties in the practical data application. The imaging domain least square migration can effectively solve this problem, but the current research is mainly for the global space invariant point spread function implementation, and cannot effectively consider the space variation characteristics of the velocity model and the illumination condition, so it is difficult to obtain high imaging quality.

[0005] Therefore, a new imaging domain least square migration method is expected, which fully utilizes the global space variation point spread function, so as to suppress the imaging noise and improve the imaging quality.

[0006] The information disclosed in the Background section of the present invention is only intended to enhance the understanding of the general background of the present invention and should not be taken as an acknowledgement or any form of suggestion that this information forms prior art that is already known to those skilled in the art. SUMMARY

[0007] The present invention aims to provide an imaging domain least square migration method, device, electronic equipment and medium, which solves the problem that the space-variant characteristics of the point spread function cannot be effectively considered by using a space-invariant point spread function in the current imaging domain least square reverse time migration, resulting in low imaging quality of the inversion result.

[0008] In a first aspect, the embodiments of the present disclosure provide an imaging domain least square migration method, comprising:

[0009] Obtaining a reverse time migration result and a global space-variant point spread function, and performing regional block division on the reverse time migration result and the global space-variant point spread function to form a plurality of block data;

[0010] Calculating a spatial domain least square migration result of each block data;

[0011] According to a regional block division mode, reducing the spatial domain least square migration result of each block data to a complete model scale to obtain a global space-variant least square migration result.

[0012] As a specific implementation mode of the embodiments of the present disclosure, the method for calculating the spatial domain least square migration result comprises:

[0013] For each block data, using three-dimensional fast Fourier transform to convert the reverse time migration result to the wave number domain, convert the point spread function to the wave number domain, perform wave number domain deconvolution and regularization, and calculate the wave number domain least square migration result;

[0014] Using three-dimensional fast Fourier inverse transform to convert the wave number domain least square migration result to the spatial domain to obtain the spatial domain least square migration result.

[0015] As a specific implementation mode of the embodiments of the present disclosure, the wave number domain least square migration result is calculated by using the following relationship,

[0016]

[0017] wherein, is the wave number domain least square migration result, is the wave number domain reverse time migration result, is the wave number domain point spread function, is is the conjugate function of, and is a regularization factor, and is 5% of the maximum value.

[0018] As a specific implementation manner of the embodiments of the present disclosure, the initial position and sampling interval of the region partition are determined according to the global space-variant point spread function, so as to form a plurality of block data.

[0019] In a second aspect, the embodiments of the present disclosure further provide an electronic device, comprising:

[0020] at least one processor; and

[0021] a memory connected with the at least one processor in communication; wherein

[0022] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the imaging domain least square migration method according to any one of the first aspect.

[0023] In a third aspect, the embodiments of the present disclosure further provide a non-transitory computer readable storage medium, which stores computer instructions for causing a computer to perform the imaging domain least square migration method according to any one of the first aspect.

[0024] In a fourth aspect, the embodiments of the present disclosure further provide an imaging domain least square migration device, comprising: an acquisition module, configured to acquire a reverse time migration result and a global space-variant point spread function;

[0025] a data processing module, configured to perform region partition on the reverse time migration result and the global space-variant point spread function, to form a plurality of block data;

[0026] a calculation module, configured to calculate a spatial domain least square migration result of each block data;

[0027] a reduction module, configured to reduce the spatial domain least square migration result of each block data to a complete model scale according to a region partition manner, to obtain a global space-variant least square migration result.

[0028] As a specific implementation manner of the embodiments of the present disclosure, the calculation module converts the reverse time migration result to a wave number domain and converts the point spread function to the wave number domain by using a three-dimensional fast Fourier transform, performs wave number domain deconvolution and regularization, and calculates a wave number domain least square migration result for each block data;

[0029] the wave number domain least square migration result is converted to a spatial domain by using a three-dimensional fast Fourier inverse transform, to obtain a spatial domain least square migration result.

[0030] As a specific implementation manner of the embodiment of the present disclosure, the computing module calculates the wave number domain least square migration result by using the following relationship,

[0031]

[0032] wherein, is the wave number domain least square migration result, is the wave number domain reverse time migration result, is the wave number domain point spread function, is the conjugate function of is a regularization factor, is 5% of the maximum value.

[0033] As a specific implementation manner of the embodiment of the present disclosure, the data processing module determines the initial position and sampling interval of the region block according to the global space-variant point spread function, to form a plurality of block data.

[0034] The present application has the following beneficial effects:

[0035] The present application divides the reverse time migration result and the global space-variant point spread function into region blocks, solves the problem that the space-invariant point spread function used in the current imaging domain least square reverse time migration cannot effectively consider the space-variant characteristics of the point spread function, leading to low imaging quality of the inversion result, and provides an imaging result with better amplitude fidelity and higher quality, thereby promoting the practicality of the imaging domain least square reverse time migration.

[0036] The present application uses the similarity of the local velocity model and the illumination condition, and realizes high-dimensional space deconvolution in a region block manner, instead of the traditional global space-invariant deconvolution, fully utilizes the point spread function information of different regions, improves the imaging precision, and provides an effective tool for actual three-dimensional high-resolution seismic imaging.

[0037] The method and device of the present application have other characteristics and advantages, which will be apparent or will be described in detail in the accompanying drawings and subsequent specific embodiments incorporated herein, which together serve to explain the specific principles of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0038] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout the several views, and wherein exemplary embodiments of the present application are shown.

[0039] Figure 1 A flow chart of steps of an imaging domain least square migration method according to an embodiment of the present application is shown.

[0040] Figure 2 An inverse time migration model of the Overthrust model according to an embodiment of the present application is shown.

[0041] Figure 3 A global space-variable point spread function of the Overthrust model according to an embodiment of the present application is shown.

[0042] Figure 4 The least square migration result of the global space-variable deconvolution of the data is shown. Figure 2 , Figure 3 The least square migration result of the global space-variable deconvolution of the data is shown.

[0043] Figure 5 The least square migration result of the global space-variable deconvolution of the data is shown. Figure 2 , Figure 3 The least square migration result of the global space-variable deconvolution of the data is shown.

[0044] Figure 6 The least square migration result of the global space-variable deconvolution of the data is shown. Figure 2 , Figure 3 The least square migration result of the global space-variable deconvolution of the data is shown.

[0045] Figure 7 The structural diagram of the imaging domain least square migration device according to an embodiment of the present application is shown.

[0046] Explanation of reference numerals:

[0047] 201, acquisition module; 202, data processing module; 203, calculation module; 204, reduction module. DETAILED DESCRIPTION

[0048] The preferred embodiments of the present application will be described in more detail below. Although the preferred embodiments of the present application are described below, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0049] An imaging domain least square migration method according to an embodiment of the present application is provided, which is applied to seismic wave imaging, and the method comprises:

[0050] The inverse time migration result and the global space-variable point spread function are acquired, and the inverse time migration result and the global space-variable point spread function are regionally blocked to form a plurality of block data;

[0051] The spatial domain least square migration result of each block data is calculated;

[0052] The spatial domain least square migration result of each block data is reduced to the complete model scale according to the regional blocking mode, and the global space-variable least square migration result is obtained.

[0053] In one example, the method of computing the spatial domain least-squares migration result comprises:

[0054] For each block of data, the reverse-time migration result is converted to the wave number domain using a three-dimensional fast Fourier transform, the point spread function is converted to the wave number domain, wave number domain deconvolution and regularization are performed, and the wave number domain least-squares migration result is calculated;

[0055] The wave number domain least-squares migration result is converted to the spatial domain using a three-dimensional fast Fourier inverse transform to obtain the spatial domain least-squares migration result.

[0056] In one example, the wave number domain least-squares migration result is calculated using the following relationship:

[0057]

[0058] wherein, is the wave number domain least-squares migration result, is the wave number domain reverse-time migration result, is the wave number domain point spread function, is the conjugate function of the wave number domain point spread function, and a is a regularization factor, and is 5% of the maximum value.

[0059] The embodiments of the present disclosure also provide an electronic device, which comprises:

[0060] at least one processor; and

[0061] a memory in communication connection with the at least one processor; wherein

[0062] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the imaging domain least-squares migration method described above.

[0063] The embodiments of the present disclosure also provide a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the imaging domain least-squares migration method described above.

[0064] The embodiments of the present disclosure also provide a reservoir physical property parameter direct inversion device, which comprises:

[0065] an acquisition module, the acquisition module being configured to acquire a reverse-time migration result and a global space-variant point spread function;

[0066] a data processing module, the data processing module being configured to divide the reverse-time migration result and the global space-variant point spread function into regions to form a plurality of block data;

[0067] a calculation module, configured to calculate a spatial domain least square migration result of each block data;

[0068] a reduction module, configured to reduce the spatial domain least square migration result of each block data to a full model scale according to a regional block manner, to obtain a global space-variable least square migration result.

[0069] In one example, the calculation module converts the reverse time migration result to a wave number domain, converts a point spread function to the wave number domain, performs wave number domain deconvolution and regularization, and calculates a wave number domain least square migration result for each block data by using a three-dimensional fast Fourier transform;

[0070] the wave number domain least square migration result is converted to a spatial domain by using a three-dimensional fast Fourier inverse transform, to obtain the spatial domain least square migration result.

[0071] In one example, the calculation module calculates the wave number domain least square migration result by using the following relationship,

[0072]

[0073] wherein, is the wave number domain least square migration result, is the wave number domain reverse time migration result, is the wave number domain point spread function, is is a conjugate function of, and is a regularization factor, is 5% of the maximum value.

[0074] In one example, the data processing module determines an initial position and a sampling interval of the regional block according to the global space-variable point spread function, to form a plurality of block data.

[0075] The present application divides the reverse time migration result and the global space-variable point spread function into regional blocks, solves the problem that the space-invariant point spread function used in the current imaging domain least square reverse time migration cannot effectively consider the space-variable characteristics of the point spread function, leading to low imaging quality of the inversion result, and provides an imaging result with better amplitude fidelity and higher quality, thereby promoting the practicality of the imaging domain least square reverse time migration.

[0076] The present application uses the similarity of local velocity models and illumination conditions, adopts a regional block manner to realize high-dimensional space deconvolution, replaces the traditional global space-invariant deconvolution, fully utilizes the point spread function information of different regions, improves the imaging accuracy, and provides an effective tool for actual three-dimensional high-resolution seismic imaging.

[0077] For the convenience of understanding the scheme and effects of the embodiments of the present application, four specific application examples are given below. Those skilled in the art should understand that the examples are only for the convenience of understanding the present application, and any specific details thereof are not intended to limit the present application in any way.

[0078] Example 1

[0079] Figure 1 A flow chart showing the steps of the imaging domain least square migration method according to an embodiment of the present application is shown.

[0080] As shown in Figure 1 , the imaging domain least square migration method comprises: step 101, obtaining the reverse time migration result and the global space-variant point spread function; step 102, regionally blocking the reverse time migration result and the global space-variant point spread function to form a plurality of block data; step 103, calculating the spatial domain least square migration result of each block data; step 104, reducing the spatial domain least square migration result of each block data to the complete model scale according to the regional blocking mode to obtain the global space-variant least square migration result.

[0081] Figure 2 A reverse time migration model of the Overthrust model according to an embodiment of the present application is shown.

[0082] Figure 3 A global space-variant point spread function of the Overthrust model according to an embodiment of the present application is shown.

[0083] Figure 4 For the Figure 2 , Figure 3 data, the least square migration result of global space-variant deconvolution.

[0084] Figure 5 For the Figure 2 , Figure 3 data, the least square migration result of space-invariant deconvolution by selecting the single point point spread function with the coordinate X-Y-Z position of 11-6-1.

[0085] Figure 6 For the Figure 2 , Figure 3 data, the least square migration result of space-invariant deconvolution by selecting the single point point spread function with the coordinate X-Y-Z position of 31-6-6.

[0086] The present embodiment is further described below with the Overthrust model as an example in combination with the drawings.

[0087] (1) Obtain the reverse time migration result I rtm As shown in Figure 2 , and the global space-variant point spread function mpsf As shown in Figure 3 ;

[0088] (2) According to the global space-varying point spread function m psf , determine the sampling interval r and the initial position b of the regional block;

[0089] (3) In each regional block, use three-dimensional fast Fourier transform FFT to convert the reverse-time migration result I rtm to the wave number domain Convert the regional block point spread function m psf to the wave number domain

[0090] (4) According to the regional block reverse-time migration result and the point spread function , calculate the least square imaging result in the block

[0091] (5) Use three-dimensional inverse fast Fourier transform IFFT to convert the imaging result to the spatial domain I lsm ;

[0092] (6) According to the regional block method, recombine the spatial domain least square migration result in each block to obtain the global space-varying least square migration result (as shown in Figure 4 ).

[0093] Figure 5 The least square migration result obtained by using the space-invariant deconvolution with the point spread function at the model coordinates X-Y-Z position of 11-6-1, as shown in the position of A in Figure 3 , is Figure 6 The least square migration result obtained by using the space-invariant deconvolution with the point spread function at the model coordinates X-Y-Z position of 31-6-6, as shown in the position of B in Figure 3 , is Figure 5 It is not difficult to see that the results of Figure 6 are much worse than the global space-varying least square migration result of Figure 4 , the overall inversion accuracy is low, the overall energy is unbalanced, and the amplitude decays obviously with depth. In addition, the results of Figure 5 and Figure 6 show that the results obtained by using different position point spread functions for inversion are also different, the point spread function at the shallow part focuses the energy better, the interlayer noise produced by the spatial aliasing is less, and the imaging quality is higher, which further shows that the space-varying property of the point spread function is very important to the inversion result.

[0094] Example 2

[0095] The electronic device according to an embodiment of the disclosure includes a memory and a processor.

[0096] at least one processor; and

[0097] a memory connected with the at least one processor in communication; wherein

[0098] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the imaging domain least square offset method.

[0099] The electronic device according to an embodiment of the disclosure includes a memory and a processor.

[0100] The memory is configured to store non-transitory computer readable instructions. Specifically, the memory can include one or more computer program products that can include various forms of computer readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM), cache memory, and / or the like. The non-volatile memory may, for example, include read only memory (ROM), hard disk, flash memory, and / or the like.

[0101] The processor can be a central processing unit (CPU) or other form of processing unit having data processing and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions. In an embodiment of the disclosure, the processor is configured to execute the computer readable instructions stored in the memory.

[0102] Those skilled in the art will understand that, in order to solve the technical problem of how to obtain a good user experience effect, the embodiment can also include well-known structures such as a communication bus, an interface, and the like, which should also be included in the protection scope of the disclosure.

[0103] The embodiment of the disclosure also provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the imaging domain least square offset method.

[0104] Example 3

[0105] The embodiment of the disclosure provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the imaging domain least square offset method.

[0106] The computer readable storage medium according to the embodiments of the present disclosure has non-transitory computer readable instructions stored thereon. When the non-transitory computer readable instructions are run by a processor, all or part of the steps of the method of the embodiments of the present disclosure described above are performed.

[0107] The computer readable storage medium described above includes, but is not limited to, an optical storage medium (for example, a CD-ROM and a DVD), a magneto-optical storage medium (for example, an MO), a magnetic storage medium (for example, a magnetic tape or a mobile hard disk), a medium with a built-in rewritable non-volatile memory (for example, a memory card), and a medium with a built-in ROM (for example, a ROM cartridge).

[0108] Example 4

[0109] Figure 7 A block diagram of an imaging domain least square migration device according to an embodiment of the present application is shown.

[0110] As shown in Figure 7 The imaging domain least square migration device includes:

[0111] An acquisition module 101 is configured to acquire a reverse time migration result and a global space-variant point spread function;

[0112] A data processing module 102 is configured to perform regional blocking on the reverse time migration result and the global space-variant point spread function to form a plurality of block data;

[0113] A calculation module 103 is configured to calculate a spatial domain least square migration result of each block data;

[0114] A reduction module 104 is configured to reduce the spatial domain least square migration result of each block data to a complete model scale according to a regional blocking manner to obtain a global space-variant least square migration result.

[0115] In one example, the calculation module 103 converts the reverse time migration result to a wave number domain and converts the point spread function to the wave number domain by using a three-dimensional fast Fourier transform for each block data, performs wave number domain deconvolution and regularization, and calculates a wave number domain least square migration result.

[0116] The wave number domain least square migration result is converted to a spatial domain by using a three-dimensional fast Fourier inverse transform to obtain a spatial domain least square migration result.

[0117] In one example, the calculation module 103 calculates the wave number domain least square migration result by using the following relationship:

[0118]

[0119] wherein, is the wave-number domain least-square migration result, is the wave-number domain reverse-time migration result, is the wave-number domain point spread function, is the is the conjugate function of α is a regularization factor, is the 5% of the maximum value.

[0120] In one example, the data processing module 102 determines initial positions and sampling intervals of the regional patches according to the global space-variant point spread function to form a plurality of patch data.

[0121] It is to be understood that the above description of the embodiments of the present application is merely intended to illustrate the beneficial effects of the embodiments of the present application and is not intended to limit the embodiments of the present application to any of the examples given.

[0122] The embodiments of the present application have been described above, the above description is exemplary and is not exhaustive, and is also not limited to the disclosed embodiments. Many modifications and changes are apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. An imaging domain least-squares migration method applied to seismic wave imaging, characterized in that, The method comprises the following steps: obtaining reverse-time migration results and a global spatially variable point spread function, and performing regional blocking on the reverse-time migration results and the global spatially variable point spread function to form a plurality of block data; calculating spatial domain least square migration results of each block data; reducing the spatial domain least square migration results of each block data to a complete model scale according to a regional blocking mode to obtain global spatially variable least square migration results.

2. The imaging domain least squares migration method of claim 1, wherein, The method for calculating spatial domain least square migration results comprises the following steps: for each block data, converting the reverse-time migration results to a wave number domain and the point spread function to a wave number domain by using three-dimensional fast Fourier transform, performing wave number domain deconvolution and regularization, and calculating wave number domain least square migration results; converting the wave number domain least square migration results to a spatial domain by using three-dimensional fast Fourier inverse transform to obtain spatial domain least square migration results.

3. The imaging domain least squares migration method of claim 2, wherein, The wave number domain least square migration results are calculated by using the following relationship, where, is the wave-number domain least-square migration result, is the wave-number domain reverse-time migration result, is the wave-number domain point spread function, is the conjugate function of, and a is a regularization factor, is the 5% of the maximum value.

4. The imaging domain least squares migration method of claim 1, wherein, determining initial positions and sampling intervals of regional blocking according to the global spatially variable point spread function to form a plurality of block data.

5. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the imaging domain least square migration method of any one of claims 1-4.

6. A non-transitory computer-readable storage medium, comprising: The non-transitory computer readable storage medium stores computer instructions for causing a computer to perform the imaging domain least square migration method of any one of claims 1-4.

7. An imaging domain least squares migration apparatus characterized by, The method comprises the following steps: an acquisition module for acquiring reverse-time migration results and a global spatially variable point spread function; a data processing module for performing regional blocking on the reverse-time migration results and the global spatially variable point spread function to form a plurality of block data; a calculation module for calculating spatial domain least square migration results of each block data; a reduction module for reducing the spatial domain least square migration results of each block data to a complete model scale according to a regional blocking mode to obtain global spatially variable least square migration results.

8. The imaging domain least squares migration apparatus of claim 7, wherein, The calculation module converts the reverse-time migration results to a wave number domain and the point spread function to a wave number domain by using three-dimensional fast Fourier transform for each block data, performs wave number domain deconvolution and regularization, and calculates wave number domain least square migration results; converting the wave number domain least square migration results to a spatial domain by using three-dimensional fast Fourier inverse transform to obtain spatial domain least square migration results.

9. The imaging domain least squares migration apparatus of claim 8, wherein, The calculation module calculates the wave number domain least square migration results by using the following relationship, where, is the wave-number domain least-squares migration result, is the wave-number domain reverse-time migration result, is the wave-number domain point spread function, is the conjugate function of, and a is a regularization factor, 5%.

10. The imaging domain least squares migration apparatus of claim 8, wherein, The data processing module determines initial positions and sampling intervals of regional blocking according to the global spatially variable point spread function to form a plurality of block data.

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