Seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing
By adopting block-similar gradient preprocessing method in the full waveform inversion technology, the seismic data is gradient constrained, which solves the problem of noise and abnormal information interference, and improves the inversion accuracy and convergence efficiency.
Patent Information
- Application Number
- CN202311650142.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-04
- Publication Date
- 2025-06-06
AI Technical Summary
In the full waveform inversion technology, the noise and low quality of seismic data cause the gradient to contain more noise or abnormal information, making it difficult to improve modeling accuracy and convergence efficiency.
Using a block similarity gradient preprocessing method, by calculating the similarity factor of the gradient between the two neighboring blocks, the full waveform inversion gradient is constrained, the processed gradient results are obtained, and the full waveform inversion speed is updated.
Effectively eliminate noise interference and abnormal information, enhance effective structural information in the gradient, improve the full waveform inversion accuracy and convergence efficiency, and make the inversion result more in line with the geological background.
Smart Images
Figure CN120103427A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of seismic velocity modeling and imaging in oil and gas exploration and development, and more specifically, relates to a seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing. Background Art
[0002] Full waveform inversion technology is one of the most accurate inversion methods currently. Its key problem is reliable gradient calculation. However, seismic data often contain more noise and lower quality, which results in more noise or abnormal information in the gradient calculation of full waveform inversion. Traditional full waveform inversion methods often solve this problem by smoothing the gradient, but this method has limited processing effect and is difficult to obtain satisfactory results. It also reduces the convergence efficiency of full waveform inversion or directly obtains wrong inversion results. Summary of the invention
[0003] The purpose of the present invention is to propose a seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing, to optimize the gradient morphology, and to improve the accuracy and convergence efficiency of full waveform inversion velocity modeling.
[0004] To achieve the above objectives, in a first aspect, the present invention proposes a seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing, comprising:
[0005] Obtain full waveform inversion gradient of seismic data;
[0006] Performing similarity-based preprocessing on the full waveform inversion gradient to obtain a similarity factor of the gradient between two neighborhood blocks;
[0007] Performing constraint processing on the full waveform inversion gradient based on the similarity factor to obtain a processed gradient result;
[0008] Perform full waveform inversion velocity update based on the gradient result;
[0009] Determine whether the target functional meets the set conditions. If not, loop all the above steps. If yes, output the final inversion result.
[0010] Optionally, the calculation formula for the full waveform inversion gradient of the seismic data is:
[0011]
[0012] Where g represents the full waveform inversion gradient, N s ,N r They represent the traversal of the earthquake source and the detection point, v represents the velocity of the seismic wave, T represents the propagation time of the seismic wave, and u represents the seismic wave field. Indicates the accompanying earthquake source.
[0013] Optionally, the similarity factor of the gradient between two neighborhood blocks is calculated by the following formula:
[0014]
[0015] Among them, c(p 1 ;p 2 ) represents the similarity factor of the gradient between adjacent domain blocks, O is the Euclidean metric of two neighboring blocks, R is the search area block, and p 1 ,p 2 is the position of the midpoint of the two area blocks, and f is the filtering parameter.
[0016] Optionally, the constraint processing of the full waveform inversion gradient based on the similarity factor is calculated by the following formula:
[0017]
[0018] in, is the gradient result after processing.
[0019] Optionally, the calculation formula for the full waveform inversion velocity update is:
[0020]
[0021] Among them, k represents the number of iterations and α represents the iteration step size.
[0022] Optionally, the determining whether the target functional meets a set condition includes:
[0023] Determine whether the target functional reaches the iteration threshold or the maximum number of iterations.
[0024] In a second aspect, the present invention provides an electronic device, the electronic device comprising:
[0025] at least one processor; and,
[0026] a memory communicatively connected to the at least one processor; wherein,
[0027] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing as described in any one of the first aspects.
[0028] In a third aspect, the present invention proposes a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute any of the seismic waveform inversion velocity modeling methods based on block similarity gradient preprocessing described in the first aspect.
[0029] In a fourth aspect, the present invention provides a seismic waveform inversion velocity modeling device based on block similarity gradient preprocessing, comprising:
[0030] Inversion gradient calculation module, used to obtain the full waveform inversion gradient of seismic data;
[0031] A similarity factor calculation module is used to perform similarity-based preprocessing on the full waveform inversion gradient to obtain a similarity factor of the gradient between two neighborhood blocks;
[0032] A constraint processing module, used for performing constraint processing on the full waveform inversion gradient based on the similarity factor to obtain a processed gradient result;
[0033] An inversion module, used for performing full waveform inversion velocity update based on the gradient result;
[0034] The inversion result input module is used to determine whether the target functional meets the set conditions. If not, all the above steps are looped. If yes, the final inversion result is output.
[0035] Optionally, the determining whether the target functional meets a set condition includes:
[0036] Determine whether the target functional reaches the iteration threshold or the maximum number of iterations.
[0037] The beneficial effects of the present invention are:
[0038] The present invention constrains the full waveform inversion gradient based on the block similarity method, which can better eliminate noise interference and abnormal information in the full waveform inversion gradient, enhance the effective structural information in the gradient, and improve the accuracy of the full waveform inversion gradient, thereby improving the full waveform inversion accuracy and convergence efficiency, and making the full waveform inversion result more consistent with the geological background.
[0039] The system of the present invention has other characteristics and advantages, which will be apparent from the drawings incorporated herein and the following detailed description, or will be described in detail in the drawings incorporated herein and the following detailed description, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, in which like reference numerals generally represent like components.
[0041] Figure 1 A step diagram of a seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing according to the present invention is shown.
[0042] Figure 2 A schematic diagram of an actual velocity model in an embodiment is shown.
[0043] Figure 3 A schematic diagram of an initial velocity model in an embodiment is shown.
[0044] Figure 4 A schematic diagram of conventional full waveform inversion gradient in one embodiment is shown.
[0045] Figure 5 A schematic diagram of conventional full waveform inversion results in one embodiment is shown.
[0046] Figure 6 A schematic diagram of the result of full waveform inversion gradient processing using the seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing of the present invention in one embodiment is shown.
[0047] Figure 7 A schematic diagram of a full waveform inversion result obtained by using the seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing of the present invention in one embodiment is shown. DETAILED DESCRIPTION
[0048] The present invention will be described in more detail below with reference to the accompanying drawings. Although preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.
[0049] Example 1
[0050] like Figure 1 As shown, this embodiment provides a seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing, comprising:
[0051] S1: Obtain the full waveform inversion gradient of seismic data;
[0052] In this step, the calculation formula for the full waveform inversion gradient of seismic data is:
[0053]
[0054] Where g represents the full waveform inversion gradient, N s ,N r They represent the traversal of the earthquake source and the detection point, v represents the velocity of the seismic wave, T represents the propagation time of the seismic wave, and u represents the seismic wave field. Indicates the accompanying earthquake source.
[0055] S2: Perform similarity-based preprocessing on the full waveform inversion gradient to obtain the similarity factor of the gradient between two neighborhood blocks;
[0056] In this step, the similarity factor of the gradient between two neighborhood blocks is calculated by the following formula:
[0057]
[0058] Among them, c(p 1 ;p 2 ) represents the similarity factor of the gradient between adjacent domain blocks, O is the Euclidean metric of two neighboring blocks, R is the search area block, and p 1 ,p 2 is the position of the midpoint of the two area blocks, and f is the filtering parameter.
[0059] S3: Constrain the full waveform inversion gradient based on the similarity factor to obtain the processed gradient result;
[0060] In this step, the full waveform inversion gradient is constrained and calculated using the following formula:
[0061]
[0062] in, is the gradient result after processing.
[0063] S4: Full waveform inversion velocity update based on gradient results;
[0064] In this step, the calculation formula for full waveform inversion velocity update is:
[0065]
[0066] Among them, k represents the number of iterations and α represents the iteration step size.
[0067] S5: Determine whether the target functional meets the set conditions. If not, loop steps S1-S5. If yes, output the final inversion result.
[0068] In this step, judging whether the target functional meets the set conditions includes:
[0069] Determine whether the target functional reaches the iteration threshold or the maximum number of iterations.
[0070] Example 2
[0071] The present embodiment provides a seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing, which can solve the problems of noise interference and abnormal information in conventional full waveform inversion gradients, improve the accuracy of full waveform inversion, and make the inversion results more consistent with the geological background, thereby improving the accuracy and convergence efficiency of full waveform inversion velocity modeling.
[0072] The seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing in this embodiment specifically includes the following steps:
[0073] Step 1: Obtain the full waveform inversion gradient.
[0074]
[0075] Among them, N s ,N r They represent the traversal of the earthquake source and the detection point, v represents the velocity of the seismic wave, T represents the propagation time of the seismic wave, and u represents the seismic wave field. Indicates the accompanying earthquake source.
[0076] Step 2: Preprocess the full waveform inversion gradient based on block similarity. First, calculate the similarity factor of the gradient between two neighborhood blocks.
[0077]
[0078] Among them, O is the Euclidean metric of two neighborhood blocks, R is the search area block, and p 1 ,p 2 is the position of the midpoint of the two area blocks, and f is the filtering parameter.
[0079] Step 3: Constrain the full waveform inversion gradient.
[0080]
[0081] That is the processed gradient result. Through similarity processing, the effective structural information in the gradient is retained and the noise and abnormal interference are removed.
[0082] Step 4: Full waveform inversion speed update,
[0083]
[0084] Among them, k represents the number of iterations and α represents the iteration step size.
[0085] Step 5: Determine whether the target functional E(v) reaches the iteration threshold σ, that is, E(v)≤σ, or whether the maximum number of iterations K is reached. If not, loop from step 1 to step 5. If satisfied, output v. k+1 This is the final inversion result.
[0086] In this embodiment, the Figure 2 The actual velocity model shown is used for method testing. Figure 3 is the initial model for full waveform inversion, Figure 4 is the gradient directly obtained by conventional full waveform inversion, Figure 5 The conventional full waveform inversion result is then processed using the seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing in this embodiment. The result is as follows: Figure 6 As shown, it can be seen that the noise interference in the gradient is significantly eliminated, and the structure is more consistent with the original actual model. The final full waveform inversion result is as follows Figure 7 As shown, the inversion results are more consistent with the actual model, the structure is more accurate, and there are no obvious abnormalities.
[0087] The model experiment shows that the seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing of the present invention can effectively improve the accuracy of full waveform inversion, proving the effectiveness of the method.
[0088] Example 3
[0089] This embodiment provides a seismic waveform inversion velocity modeling device based on block similarity gradient preprocessing, comprising:
[0090] Inversion gradient calculation module, used to obtain the full waveform inversion gradient of seismic data;
[0091] A similarity factor calculation module is used to perform similarity-based preprocessing on the full waveform inversion gradient to obtain the similarity factor of the gradient between two neighborhood blocks;
[0092] A constraint processing module, used to perform constraint processing on the full waveform inversion gradient based on a similarity factor to obtain a processed gradient result;
[0093] Inversion module, used for full waveform inversion velocity update based on gradient results;
[0094] The inversion result input module is used to determine whether the target functional meets the set conditions. If not, all the above steps are looped. If yes, the final inversion result is output.
[0095] Among them, judging whether the target functional meets the set conditions includes:
[0096] Determine whether the target functional reaches the iteration threshold or the maximum number of iterations.
[0097] In this embodiment, the calculation formula of the full waveform inversion gradient of seismic data is:
[0098]
[0099] Where g represents the full waveform inversion gradient, N s ,N r They represent the traversal of the earthquake source and the detection point, v represents the velocity of the seismic wave, T represents the propagation time of the seismic wave, and u represents the seismic wave field. Indicates the accompanying earthquake source.
[0100] In this embodiment, the similarity factor of the gradient between two neighborhood blocks is calculated by the following formula:
[0101]
[0102] Among them, c(p 1 ;p 2 ) represents the similarity factor of the gradient between adjacent domain blocks, O is the Euclidean metric of two neighboring blocks, R is the search area block, and p 1 ,p 2 is the position of the midpoint of the two area blocks, and f is the filtering parameter.
[0103] In this embodiment, the constraint processing of the full waveform inversion gradient is calculated by the following formula:
[0104]
[0105] in, is the gradient result after processing.
[0106] In this embodiment, the calculation formula for full waveform inversion velocity update is:
[0107]
[0108] Among them, k represents the number of iterations and α represents the iteration step size.
[0109] Example 4
[0110] This embodiment provides an electronic device, the electronic device comprising:
[0111] at least one processor; and,
[0112] a memory communicatively connected to the at least one processor; wherein,
[0113] The memory stores instructions that can be executed 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 seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing described in the above embodiment.
[0114] The electronic device according to an embodiment of the present disclosure includes a memory and a processor, and the memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may, for example, include a read-only memory (ROM), a hard disk, a flash memory, etc.
[0115] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.
[0116] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.
[0117] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.
[0118] Example 5
[0119] This embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute any of the seismic waveform inversion velocity modeling methods based on block similarity gradient preprocessing described in the first aspect.
[0120] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of each embodiment of the present disclosure are executed.
[0121] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).
[0122] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.
Claims
1. A seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing, It is characterized in that include: Obtain full waveform inversion gradient of seismic data; Performing similarity-based preprocessing on the full waveform inversion gradient to obtain a similarity factor of the gradient between two neighborhood blocks; Performing constraint processing on the full waveform inversion gradient based on the similarity factor to obtain a processed gradient result; Perform full waveform inversion velocity update based on the gradient result; Determine whether the target functional meets the set conditions. If not, loop all the above steps. If yes, output the final inversion result.
2. The seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing according to claim 1, It is characterized in that The calculation formula of the full waveform inversion gradient of the seismic data is: Where g represents the full waveform inversion gradient, N s ,N r They represent the traversal of the earthquake source and the detection point, v represents the velocity of the seismic wave, T represents the propagation time of the seismic wave, and u represents the seismic wave field. Indicates the accompanying earthquake source.
3. The seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing according to claim 2, It is characterized in that The similarity factor of the gradient between two neighborhood blocks is calculated by the following formula: Among them, c(p 1 ;p 2 ) represents the similarity factor of the gradient between adjacent domain blocks, O is the Euclidean metric of two neighboring blocks, R is the search area block, and p 1 ,p 2 is the position of the midpoint of the two area blocks, and f is the filtering parameter.
4. The seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing according to claim 3, It is characterized in that The constraint processing of the full waveform inversion gradient based on the similarity factor is calculated by the following formula: in, is the gradient result after processing.
5. The seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing according to claim 4, It is characterized in that The calculation formula for the full waveform inversion velocity update is: Among them, k represents the number of iterations and α represents the iteration step size.
6. The seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing according to claim 1, It is characterized in that The determining whether the target functional meets the set conditions includes: Determine whether the target functional reaches the iteration threshold or the maximum number of iterations.
7. An electronic device, It is characterized in that The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing as described in any one of claims 1-6.
8. A non-transitory computer-readable storage medium, It is characterized in that The non-transitory computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the seismic waveform inversion velocity modeling method based on block similarity gradient preprocessing as described in any one of claims 1-6.
9. A seismic waveform inversion velocity modeling device based on block similarity gradient preprocessing, It is characterized in that include: Inversion gradient calculation module, used to obtain the full waveform inversion gradient of seismic data; A similarity factor calculation module is used to perform similarity-based preprocessing on the full waveform inversion gradient to obtain a similarity factor of the gradient between two neighborhood blocks; A constraint processing module, used for performing constraint processing on the full waveform inversion gradient based on the similarity factor to obtain a processed gradient result; An inversion module, used for performing full waveform inversion velocity update based on the gradient result; The inversion result input module is used to determine whether the target functional meets the set conditions. If not, all the above steps are looped. If yes, the final inversion result is output.
10. The seismic waveform inversion velocity modeling device based on block similarity gradient preprocessing according to claim 9, It is characterized in that The determining whether the target functional meets the set conditions includes: Determine whether the target functional reaches the iteration threshold or the maximum number of iterations.