A method and device for reconstructing seismic data
By employing an iterative method involving sparse transformation and thresholding, the problem of incomplete seismic data acquisition was solved, enabling accurate reconstruction of seismic data and restoration of complete seismic signals.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-10-25
- Publication Date
- 2026-03-06
AI Technical Summary
During the earthquake data acquisition process, it is impossible to obtain complete earthquake data, and existing technologies are insufficient to achieve effective and accurate data reconstruction.
An iterative method using sparse transformation and thresholding is employed to reconstruct seismic data through an iterative process of sampling matrix, sparse transformation, thresholding, and inverse sparse transformation. An improved thresholding function is used to enhance reconstruction accuracy.
Based on a small amount of seismic signals, a complete and accurate recovery of seismic signals was achieved, which is more accurate than traditional methods.
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Figure CN115639596B_ABST
Abstract
Description
Technical Field
[0001] This specification relates to the field of exploration geophysics, and in particular to a method and apparatus for reconstructing seismic data. Background Technology
[0002] During the earthquake data acquisition process, complete earthquake data is often not obtained. Therefore, in order to regularize the acquired irregular earthquake data and facilitate subsequent data processing, effective and accurate data reconstruction methods are very important.
[0003] Therefore, a precise seismic data reconstruction scheme is needed. Summary of the Invention
[0004] The purpose of this invention is to provide an accurate reconstruction scheme for seismic data.
[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:
[0006] Firstly, a method for reconstructing seismic data is provided, comprising: S1, acquiring irregular seismic data x, and sampling the irregular seismic data using a sampling matrix φ to obtain output data y; S2, performing a sparse transformation on the output data using a sparse transformation method to obtain sparse domain data s; S3, performing threshold processing on the sparse domain data s based on a preset threshold t, setting data below the threshold to zero, and leaving data above the threshold unchanged, thereby generating processed sparse domain data; S4, performing an inverse sparse transformation on the threshold-processed sparse domain seismic signal to generate an inversely sparse transformed seismic signal; S5, treating the inversely sparse transformed seismic signal as the updated sparse domain data s, and repeating steps S2 to S4 iteratively until a specified number of iterations i is reached, resulting in reconstructed seismic data.
[0007] In a second aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in the first aspect.
[0008] The above-described at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects:
[0009] Compared with existing technologies, this invention obtains irregular seismic data x in step S1, and samples the irregular seismic data y using a sampling matrix φ; in step S2, it performs a sparse transformation on the output data to obtain sparse domain data s; in step S3, it performs thresholding on the sparse domain data s based on a preset threshold t, setting data below the threshold to zero and leaving data above the threshold unchanged, thus generating processed sparse domain data; in step S4, it performs an inverse sparse transformation on the thresholded sparse domain seismic signal to generate an inverse sparse transformation seismic signal; and in step S5, it treats the inverse sparse transformation seismic signal as the updated sparse domain data s, repeating steps S2 to S4 iteratively until a specified number of iterations i is reached, thus reconstructing the seismic data. This achieves a high degree of accuracy compared to traditional methods, as only a small amount of seismic signal remains, and the original complete seismic signal can be almost completely recovered. Attached Figure Description
[0010] Figure 1 This is a schematic diagram of the flowchart framework for implementing the present invention;
[0011] Figure 2 This invention provides a comparison between the improved threshold function proposed in this invention and the traditional soft and hard threshold functions.
[0012] Figure 3 This includes simulated earthquake data and simulated earthquake data with 25% random missing data.
[0013] Figure 4 The results are shown below after reconstructing simulated seismic data using a hard threshold function, a soft threshold function, and an improved threshold function proposed in this invention, respectively.
[0014] Figure 5 The actual seismic data obtained for the Tarim Oilfield and the actual seismic data after random 25% missing data;
[0015] Figure 6 The results are obtained by reconstructing actual seismic data using a hard threshold function, a soft threshold function, and an improved threshold function proposed in this invention, respectively. Detailed Implementation
[0016] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.
[0017] Traditional seismic data reconstruction methods are based on wave equations or predictive filtering. However, these methods often require missing seismic data rules, subsurface velocity information, or assumptions about linear phase axes. For non-steady-state seismic data, time window processing is also necessary, making the process cumbersome. Therefore, this specification provides a seismic data reconstruction scheme to achieve accurate data reconstruction.
[0018] like Figure 1 As shown, Figure 1 A flowchart illustrating a seismic data reconstruction method provided for the implementation of this specification includes:
[0019] S1, acquire irregular seismic data x, and use sampling matrix φ to sample the irregular seismic data to obtain output data y.
[0020] Random sampling of seismic data is possible. Irregular seismic data x collected in the field is input into the program. The seismic data size is m×n. An m×m diagonal matrix is set as the sampling matrix φ. The sampling matrix φ is used to sample the irregular seismic data, and the output data is y. The percentage of non-zero elements in the diagonal elements of the sampling matrix is the sampling rate. The sampling process can be represented as: y = φx, where φ is the sampling matrix, x is the input data, and y is the observed data.
[0021] S2, the output data is sparsely transformed using a sparse transformation method to obtain sparse domain data s.
[0022] Transformation methods include Fourier transform, Wavelet transform, Curvelet transform, etc. After sparse transformation of seismic data, only a small number of non-zero data exist in the sparse domain, and the rest of the data are zero or close to zero. The sparse transformation process can be expressed as: ψy = s, where ψ is the sparse transformation basis, y is the observation data, and s is the sparse domain seismic data.
[0023] S3, based on a preset threshold t, performs threshold processing on the sparse domain data s, setting data smaller than the threshold to zero and leaving data larger than the threshold unchanged, thus generating the processed sparse domain data.
[0024] By setting an appropriate threshold t, thresholding is performed on the sparse domain data. The portion smaller than the threshold is set to zero, and the portion larger than the threshold is retained. In this process, since the effective seismic data is non-zero in the sparse domain after sparse transformation, and the missing portion is close to zero or zero after sparse transformation, it can be considered that the effective seismic data is retained after thresholding. Commonly used threshold functions include hard thresholding and soft thresholding.
[0025] This method adds an adjustment factor k to the threshold function. Compared with soft and hard thresholds, it can minimize the deviation from the original signal while ensuring the continuity of the reconstructed signal. The improved threshold function is as follows:
[0026]
[0027] Where k is an adjustment factor that controls the curvature of the threshold function. The larger the value of k, the closer it is to the hard threshold function, and the smaller the value of k, the closer it is to the soft threshold function.
[0028] S4, perform inverse sparse transformation on the sparse domain seismic signal after threshold processing to generate the seismic signal after inverse sparse transformation.
[0029] An inverse sparse transform is performed on the sparse domain seismic signal after thresholding. The transformation process is shown in the following formula: ψ -1 s′=y′.
[0030] Where ψ -1 Let be the inverse sparse transform basis, s′ be the sparse domain seismic signal after thresholding, and y′ be the seismic signal after inverse sparse transform.
[0031] S5, treat the seismic signal after inverse sparse transformation as the updated sparse domain data s, and repeat steps S2 to S4 for iteration until a specified number of iterations i is reached. The specific number of iterations i can be set as needed. The seismic signal after inverse sparse transformation obtained at the end of the iteration is the reconstructed seismic data.
[0032] S1. Irregular seismic data x is acquired, and the irregular seismic data is sampled using a sampling matrix φ to obtain output data y. S2. Sparse transformation is applied to the output data to obtain sparse domain data s. S3. Based on a preset threshold t, threshold processing is performed on the sparse domain data s, setting data below the threshold to zero and leaving data above the threshold unchanged, thus generating processed sparse domain data. S4. Inverse sparse transformation is performed on the threshold-processed sparse domain seismic signal to generate an inversely sparse transformed seismic signal. S5. The inversely sparse transformed seismic signal is regarded as the updated sparse domain data s, and steps S2 to S4 are repeated iteratively until a specified number of iterations i is reached. The reconstructed seismic data thus achieves a high degree of accuracy compared to traditional methods, as only a small amount of seismic signal remains and the original complete seismic signal can be almost completely recovered.
[0033] Furthermore, the present invention also provides specific experimental examples.
[0034] The method disclosed in this invention was applied to both simulated and real seismic signals, and satisfactory reconstruction results were obtained. Figure 2This is a schematic diagram comparing the improved threshold function proposed in this invention with the traditional soft and hard threshold functions; Figure 3 This is a schematic diagram of simulated earthquake data and simulated earthquake data with 25% random missing data.
[0035] Figure 4 To apply hard thresholding function, soft thresholding function and the improved thresholding function proposed in this invention to respectively... Figure 3 The diagram shows the result after reconstructing the simulated seismic data; from left to right, these are the hard threshold function, the soft threshold function, and the improved threshold function provided by this invention.
[0036] from Figure 4 As can be seen, the results reconstructed by hard threshold and soft threshold are still insufficient in the deep and top regions, while the seismic data reconstructed by the improved threshold function are more continuous in the shallow, middle and deep regions.
[0037] Figure 5 The actual seismic data obtained for the Tarim Oilfield and the actual seismic data after random 25% omission. Figure 6 The images, from left to right, illustrate the reconstruction results of actual seismic data using hard thresholding, soft thresholding, and the improved thresholding function proposed in this invention. Figure 6 As can be seen, all three threshold functions perform well, but the hard threshold function shows discontinuity at the top of the shot record, and the soft threshold function also has some deviation in the reconstruction results in the middle. Compared with the hard threshold function and the soft threshold function, the improved threshold function proposed in this invention performs better in terms of continuity and integrity after reconstruction.
[0038] In a second aspect, correspondingly, this application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned method for reconstructing seismic data.
[0039] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, for embodiments involving apparatus, devices, and media, since they are fundamentally similar to the method embodiments, the descriptions are relatively simple, and relevant details can be found in the descriptions of the method embodiments; they will not be elaborated upon here.
[0040] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, for embodiments involving apparatus, devices, and media, since they are fundamentally similar to the method embodiments, the descriptions are relatively simple, and relevant details can be found in the descriptions of the method embodiments; they will not be elaborated upon here.
[0041] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions, steps, or modules recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Claims
1. A method for reconstructing seismic data, comprising: S1, obtaining irregular seismic data x, sampling the irregular seismic data using a sampling matrix to obtain output data y; S2, performing sparse transformation on the output data using a sparse transformation method to obtain sparse domain data s; S3, based on a preset threshold t, performing threshold processing on the sparse domain data s, setting data less than the threshold to zero, and leaving data greater than the threshold unchanged to generate processed sparse domain data; specifically comprising: performing threshold processing using a threshold function T(s) as follows: ； wherein k is an adjustment factor; S4, performing inverse sparse transformation on the sparse domain seismic signal after threshold processing to generate an inverse sparse transformed seismic signal; S5, regarding the inverse sparse transformed seismic signal as updated sparse domain data s, repeating steps S2 to S4 for iteration, and after reaching a specified number of iterations i, generating reconstructed seismic data.
2. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, The processor implements the method of claim 1 when executing the program.