Seismic data noise resistance frequency compensation method and device

Abstract

This invention discloses a method and apparatus for noise-resistant frequency compensation of seismic data, relating to the fields of compressed sensing and seismic exploration design. The method includes: performing spectral analysis on the original seismic data to determine the expected frequency broadening range; performing autocorrelation calculation on the original seismic data to obtain wavelet-like waveform data; using the wavelet-like waveform data as simulated wavelet data, inputting iterative solutions to the compressed sensing noise-resistant frequency extension basis function to obtain the full bandwidth reflection coefficient; concatenating the full bandwidth spectrum with the original seismic data spectrum to obtain the extended spectrum; and performing an inverse Fourier transform on the extended spectrum to obtain the noise-resistant frequency extension data. This invention is based on the compressed sensing framework, utilizes the Shearlet denoising basis function to constrain the frequency extension basis function, and constructs a noise-resistant frequency extension basis function based on compressed sensing, achieving good high- and low-frequency compensation effects for seismic data under low signal-to-noise ratio conditions.

Description

Technical Field

[0001] This invention relates to the fields of compressed sensing and seismic exploration design technology, and in particular to a method and apparatus for noise reduction frequency compensation of seismic data. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] The quality of a seismic profile is typically determined by its bandwidth. In seismic exploration, low-frequency information is beneficial for improving the quality of deep-seated imaging. Enhancement of high-frequency information is helpful for identifying thin reservoirs and extracting weak signals, thus contributing to improved seismic profile resolution. Low-frequency energy is easily contaminated by noise during acquisition and is readily lost in processing steps such as deconvolution. The application of compressed sensing theory in seismic data overlay is based on the fact that reflection coefficients are sparse in the time domain. Reconstructing reflection coefficients filtered by wavelet can reasonably compensate for amplitude energy, broaden the bandwidth, and improve data resolution. However, like most traditional methods, the effectiveness of compressed sensing overlay is largely affected by the signal-to-noise ratio (SNR). Satisfactory compensation results can only be obtained under high SNR conditions. Summary of the Invention

[0004] This invention provides a method for noise-resistant frequency compensation of seismic data, which achieves good high- and low-frequency compensation effects in seismic data under low signal-to-noise ratio conditions. The method includes:

[0005] Spectral analysis is performed on the raw seismic data to obtain its frequency distribution, and the expected frequency broadening range is determined based on the frequency distribution of the raw seismic data.

[0006] Autocorrelation calculations are performed on the raw seismic data to obtain wavelet-like waveform data of the raw seismic data;

[0007] The wavelet-like waveform data of the original seismic data is used as simulated wavelet data and input into the compressed sensing noise-resistant topology basis function for iterative solution to obtain the full bandwidth reflection coefficient; the compressed sensing noise-resistant topology basis function is generated by fusing the Shearlet denoising basis function as a regularization constraint term with the topology basis function.

[0008] Based on the full bandwidth reflection coefficient and the expected frequency broadening range, the full bandwidth spectrum is spliced ​​with the original seismic data spectrum to obtain the extended spectrum;

[0009] The spread spectrum is subjected to inverse Fourier transform to obtain noise-resistant spread spectrum data.

[0010] This invention also provides a seismic data noise reduction frequency compensation device to obtain good high and low frequency compensation effects for seismic data under low signal-to-noise ratio conditions. The device includes:

[0011] The spectrum analysis module is used to perform spectrum analysis on the raw seismic data to obtain the frequency distribution of the raw seismic data, and to determine the expected frequency broadening range based on the frequency distribution of the raw seismic data.

[0012] The wavelet estimation module is used to perform autocorrelation calculations on the raw seismic data to obtain wavelet-like waveform data of the raw seismic data;

[0013] The reflection coefficient solution module is used to take the wavelet-like waveform data of the original seismic data as simulated wavelet data, input the compressed sensing noise-resistant topology basis function for iterative solution, and invert to obtain the full bandwidth reflection coefficient; the compressed sensing noise-resistant topology basis function is generated by fusing the Shearlet denoising basis function as a regularization constraint term with the topology basis function.

[0014] The spectrum stitching module is used to stitch the full-bandwidth spectrum with the original seismic data spectrum based on the full-bandwidth reflection coefficient and the expected frequency widening range to obtain the extended spectrum;

[0015] The spectrum spreading module is used to perform an inverse Fourier transform on the spread spectrum to obtain noise-resistant spread spectrum data.

[0016] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described earthquake data noise reduction frequency compensation method.

[0017] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described earthquake data noise reduction frequency compensation method.

[0018] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described seismic data noise reduction frequency compensation method.

[0019] In this embodiment of the invention, spectral analysis is performed on the original seismic data to obtain its frequency distribution, and the expected frequency broadening range is determined based on this distribution. Autocorrelation calculation is performed on the original seismic data to obtain wavelet-like waveform data. This wavelet-like waveform data is then used as simulated wavelet data and input into a compressed sensing noise-resistant topology basis function for iterative solution, resulting in the full-bandwidth reflection coefficient. Based on the full-bandwidth reflection coefficient and the expected frequency broadening range, the full-bandwidth spectrum is concatenated with the original seismic data spectrum to obtain the extended spectrum. An inverse Fourier transform is then performed on the extended spectrum to obtain the noise-resistant topology data. The compressed sensing noise-resistant topology basis function uses a Shearlet denoising basis function as a regularization constraint term, which is fused with the topology basis function. Thus, based on the compressed sensing framework, and using the Shearlet denoising basis function to constrain the topology basis function, a compressed sensing noise-resistant topology basis function is constructed, achieving good high- and low-frequency compensation effects for seismic data under low signal-to-noise ratio conditions. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0021] Figure 1 This is a flowchart of the earthquake data noise reduction frequency compensation method in an embodiment of the present invention;

[0022] Figure 2 This is an example diagram of the original seismic data in an embodiment of the present invention;

[0023] Figure 3 This is an example diagram showing the frequency distribution of the original seismic data in an embodiment of the present invention;

[0024] Figure 4 This is an example diagram of an actual seismic trace and its wavelet in an embodiment of the present invention;

[0025] Figure 5 This is an example diagram of the original seismic data in an embodiment of the present invention;

[0026] Figure 6 This is an example diagram of noisy data in an embodiment of the present invention;

[0027] Figure 7 This is an example diagram of wavelet transform in an embodiment of the present invention;

[0028] Figure 8 This is an example diagram of curve wave transform in an embodiment of the present invention;

[0029] Figure 9 This is an example diagram of the denoising result of shear wave transform in an embodiment of the present invention;

[0030] Figure 10 This is an example diagram of noisy data in an embodiment of the present invention;

[0031] Figure 11 Example graph showing the results of processing using traditional methods;

[0032] Figure 12 This is an example diagram showing the processing results of the seismic data noise reduction frequency compensation method in an embodiment of the present invention;

[0033] Figure 13 This is an example of the spectrum of noisy data in an embodiment of the present invention;

[0034] Figure 14 Example of a spectrum of the result processed by a traditional method;

[0035] Figure 15 This is a spectral example of the processing result of the seismic data noise reduction frequency compensation method in an embodiment of the present invention;

[0036] Figure 16 This is a structural block diagram of the earthquake data noise reduction frequency compensation device in an embodiment of the present invention;

[0037] Figure 17 This is a specific example diagram of the earthquake data noise reduction frequency compensation device in the embodiments of the present invention;

[0038] Figure 18 This is a schematic diagram of the computer device structure according to an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0040] In order to obtain good high and low frequency compensation effect of seismic data under low signal-to-noise ratio conditions, this invention provides a seismic data noise-resistant frequency compensation method. Figure 1 This is a flowchart of the seismic data noise reduction frequency compensation method in an embodiment of the present invention, as shown below. Figure 1 As shown, the method may include:

[0041] Step 101: Perform spectral analysis on the raw seismic data to obtain the frequency distribution of the raw seismic data, and determine the expected frequency broadening range based on the frequency distribution of the raw seismic data;

[0042] Step 102: Perform autocorrelation calculation on the raw seismic data to obtain wavelet-like waveform data of the raw seismic data;

[0043] Step 103: The wavelet waveform data of the original seismic data is used as simulated wavelet data and input into the compressed sensing noise-resistant topology basis function for iterative solution to obtain the full bandwidth reflection coefficient; the compressed sensing noise-resistant topology basis function is generated by fusing the Shearlet denoising basis function as a regularization constraint term with the topology basis function.

[0044] Step 104: Based on the full bandwidth reflection coefficient and the expected frequency broadening range, the full bandwidth spectrum is spliced ​​with the original seismic data spectrum to obtain the extended spectrum;

[0045] Step 105: Perform an inverse Fourier transform on the spread spectrum to obtain noise-resistant spread spectrum data.

[0046] Depend on Figure 1 As shown in the flowchart, the seismic data noise-resistant frequency compensation method provided in this embodiment of the invention can be used for frequency compensation and denoising of seismic data. It is a technical solution based on compressed sensing theory for denoising and constrained frequency extension. Based on the compressed sensing framework, it uses Shearlet denoising basis function to constrain the frequency extension basis function and constructs compressed sensing noise-resistant frequency extension basis function based on compressed sensing, thereby obtaining good high and low frequency compensation effect of seismic data under low signal-to-noise ratio conditions.

[0047] In this embodiment, spectral analysis is first performed on the raw seismic data to obtain its frequency distribution. The expected frequency broadening range is then determined based on this distribution. For example, after obtaining the raw seismic data, the data can be observed first, and spectral analysis performed on it to determine the expected spectral broadening range. See [example example]. Figure 2 and Figure 3 ,in Figure 2 This is an example image of raw seismic data. Figure 3 This is an example diagram showing the spectral distribution of the original seismic data.

[0048] In this embodiment, autocorrelation calculation is performed on the original seismic data to obtain wavelet-like waveform data. This is because the wavelet shape of field data often differs significantly from that of the Ricker wavelet. Performing channel-by-channel autocorrelation calculation on the original seismic data yields a result that closely approximates the wavelet of the field data. This is primarily because the autocorrelation function is a measure of the correlation between a signal and its time-delayed version. For an ideal seismic wavelet, its autocorrelation function should exhibit a peak at zero delay, indicating a perfect match between the wavelet and itself. Seismic data can typically be represented as the convolution of the reflection coefficient of the subsurface medium and the seismic wavelet. When autocorrelation is performed on this convolutional signal, the result will contain characteristics of the wavelet's autocorrelation function. Since the wavelet's autocorrelation function is similar to the wavelet itself (especially for symmetrical or approximately symmetrical wavelets), the autocorrelation result is close to the wavelet. Therefore, in this embodiment, the autocorrelation method is used to estimate the wavelet from the actual data, and the autocorrelation method is employed to extract the data wavelet. For example... Figure 4 This is an example diagram of an actual seismic trace and its wavelet.

[0049] In this embodiment, after obtaining the wavelet-like waveform data of the original seismic data, the wavelet-like waveform data of the original seismic data is used as simulated wavelet data and input into the compressed sensing noise-resistant topology basis function for iterative solution to obtain the full bandwidth reflection coefficient. The compressed sensing noise-resistant topology basis function is generated by fusing the Shearlet denoising basis function as a regularization constraint term with the topology basis function.

[0050] The compressed sensing noise-resistant topology basis function in this embodiment of the invention selects the Shearlet denoising basis function as a regularization constraint term to constrain the solution of the topology basis function, thereby achieving simultaneous denoising and topology. This is because the inventors, through analysis, learned that the effectiveness of the compressed sensing-based seismic data denoising method depends on the choice of dictionary. Similar to data reconstruction, the denoising effect, including the degree of noise removal and the preservation of effective signals, improves with the sufficiency of sparse representation. Therefore, they considered finding a dictionary that can provide a more sufficient sparse representation of the signal. The Shearlet dictionary has a directional, tightly supported structure, and its fine scale can well represent the characteristics of different frequency bands. It can provide a unified transformation between discrete and continuous variations and can best sparse anisotropic images. For example, see... Figure 5 , Figure 6 , Figure 7 , Figure 8 and Figure 9 ,in, Figure 5 Example image of raw seismic data. Figure 6 Example image of noisy data. Figure 7 Here is an example diagram of wavelet transform. Figure 8 Here is an example diagram of the curve wave transform. Figure 9 This is an example diagram showing the denoising results of shear wave transform. Based on this, in this embodiment of the invention, autocorrelation is calculated for each trace of the original seismic data to be processed to obtain its wavelet-like waveform, which serves as the simulated wavelet in the compressed sensing topology basis function. After selecting the simulated wavelet for each trace, the constructed compressed sensing noise-resistant topology basis function is first fused with the shearlet denoising basis function as a regularization constraint, thus obtaining the final noise-resistant optimization solution function.

[0051] In this embodiment, the shearlet denoising basis function is added as a regularization constraint and fused with the topology basis function. During this process, the weights of the two combined functions can be determined. That is, in one embodiment, the seismic data noise-resistant frequency compensation method may further include: determining the weights of the shearlet denoising basis function and the topology basis function respectively; and, based on the weights of the shearlet denoising basis function and the topology basis function, using the shearlet denoising basis function as a regularization constraint term, fusing it with the topology basis function to generate a compressed sensing noise-resistant topology basis function.

[0052] In this embodiment, the wavelet-like waveform data of the original seismic data is used as simulated wavelet data. This data is then input into the compressed sensing noise-resistant topology basis function for iterative solution, and the full-bandwidth reflection coefficients are obtained through inversion. This process may include: using the wavelet-like waveform data of the original seismic data as simulated wavelet data, inputting it into the compressed sensing noise-resistant topology basis function, and using the Fast Convex Projection (POCS) algorithm for iterative solution to obtain the full-bandwidth reflection coefficients. Finally, FPOCS is used to solve for the reflection coefficients of the seismic data after noise compensation, and the spectrum of these reflection coefficients is the full bandwidth.

[0053] After obtaining the full-bandwidth reflection coefficient through inversion, the full-bandwidth spectrum is concatenated with the original seismic data spectrum based on the full-bandwidth reflection coefficient and the expected frequency broadening range to obtain the extended spectrum. In this embodiment, the process may include: truncating the full-bandwidth spectrum based on the full-bandwidth reflection coefficient; and concatenating the full-bandwidth spectrum with the original seismic data spectrum according to frequency compensation weights to obtain the extended spectrum. For example, the full-bandwidth spectrum can be truncated according to the full-bandwidth reflection coefficient and a custom-designed frequency range, and then concatenated with the original seismic data frequency according to a certain frequency compensation weight to obtain the extended spectrum of the compensated data.

[0054] In this embodiment, when stitching the full-bandwidth spectrum with the original seismic data spectrum based on the full-bandwidth reflection coefficient and the expected frequency broadening range, the full-bandwidth spectrum is used for regions with frequencies higher than a first threshold or lower than a second threshold, while the original seismic data spectrum is used for regions with frequencies neither higher than the first threshold nor lower than the second threshold. For example, the full-bandwidth spectrum is used for compensated high and low frequencies, while the original seismic data spectrum is used for uncompensated intermediate regions.

[0055] Finally, the spread spectrum is subjected to an inverse Fourier transform to obtain noise-resistant spread spectrum data.

[0056] As can be seen from the above embodiments, by analyzing the spectrum, signal-to-noise ratio and profile comparison of the compensated data and the original data, analyzing the frequency range selection, determining the compensation weight, and analyzing the relationship between the shear wave dictionary scale and data characteristics and signal-to-noise ratio, the embodiments of the present invention deduce a full-frequency compensation strategy suitable for low signal-to-noise ratio environments with complex data.

[0057] To verify the applicability and effectiveness of the seismic data noise-resistant frequency compensation method in this embodiment of the invention, the following example illustrates the verification of the method. In this example, random noise is first added to the real data, and then the compensation effects of the traditional method and the method of this embodiment are compared comprehensively. The data used in this experiment is the shallow layer of a pre-stack time profile of a basin. The layers are dense, and there are multiple thin reservoir areas. Weak reflections between some strong axes indicate that the data requires a wider frequency band and stronger mid-to-high frequency amplitude energy. First, weak random noise is added to the original data, and then the traditional method and the method of this embodiment of the invention are processed respectively to compare the superiority of the method of this embodiment of the invention.

[0058] Figure 10 This is an example image of the noisy data in this case. Figure 11 This is an example image showing the results of processing using traditional methods. Figure 12 The figures shown are example diagrams illustrating the processing results of the method according to embodiments of the present invention. Comparisons show that both the conventional method and the method of the present invention contribute to improving the resolution of the profile; however, the method of the present invention outperforms the conventional method in enhancing weak reflections and the continuity of the in-phase axis. For processing weakly noisy data, the method of the present invention exhibits better noise resistance than the conventional method, which is verified by the signal-to-noise ratio of the processed data. Simultaneously, the data resolution is significantly improved, and the result of the method of the present invention has a higher signal-to-noise ratio than the conventional method.

[0059] Figure 13 This is an example of the spectrum of the noisy data in this case. Figure 14 Here is an example of a spectrum of the results processed using traditional methods. Figure 15 This is an example spectrum of the processing result of the method according to an embodiment of the present invention. The changes before and after processing, as well as the differences between the conventional method and the method of the present invention, can be clearly observed. Figure 13 It shows a bandwidth of 75 Hz, with weak mid-frequency and high-frequency amplitude energy. Furthermore, the presence of noise amplifies the lowest energy in the noise data spectrum. When comparing... Figure 14 and Figure 15It is evident that both the conventional method and the method of the present invention exhibit a certain degree of frequency compensation. However, the method of the present invention is less susceptible to noise. In the case of weak noise, its impact is essentially negligible. When noise interference is present, the method of the present invention exhibits excellent bandwidth broadening and energy enhancement performance in both the low-frequency and high-frequency domains.

[0060] The seismic data noise-resistant frequency compensation method in this embodiment of the invention can effectively remove noise and broaden the bandwidth, thereby improving data resolution. This scheme focuses on constructing compressed sensing noise-resistant topology functions by using Shearlet denoising basis functions to constrain the topology basis functions, thus achieving good high and low frequency compensation effects for seismic data under low signal-to-noise ratio conditions.

[0061] This invention also provides a seismic data noise reduction frequency compensation device, as described in the following embodiments. Since the principle behind this device's solution is similar to that of the seismic data noise reduction frequency compensation method, its implementation can be found in the implementation of the seismic data noise reduction frequency compensation method; repeated details will not be elaborated further.

[0062] Figure 16 This is a structural block diagram of the seismic data noise reduction frequency compensation device in an embodiment of the present invention, as shown below. Figure 16 As shown, the seismic data noise reduction frequency compensation device may include:

[0063] The spectrum analysis module 1601 is used to perform spectrum analysis on the raw seismic data to obtain the frequency distribution of the raw seismic data and determine the expected frequency broadening range based on the frequency distribution of the raw seismic data.

[0064] The wavelet estimation module 1602 is used to perform autocorrelation calculation on the raw seismic data to obtain wavelet-like waveform data of the raw seismic data.

[0065] The reflection coefficient solving module 1603 is used to take the wavelet waveform data of the original seismic data as simulated wavelet data, input the compressed sensing noise-resistant topology basis function for iterative solution, and invert to obtain the full bandwidth reflection coefficient; the compressed sensing noise-resistant topology basis function is generated by fusing the Shearlet denoising basis function as a regularization constraint term with the topology basis function.

[0066] The spectrum stitching module 1604 is used to stitch the full bandwidth spectrum with the original seismic data spectrum based on the full bandwidth reflection coefficient and the expected frequency widening range to obtain the extended spectrum;

[0067] The spectrum spreading module 1605 is used to perform an inverse Fourier transform on the spread spectrum to obtain noise-resistant spread spectrum data.

[0068] Figure 17This is a specific example diagram of the seismic data noise reduction frequency compensation device in an embodiment of the present invention, such as... Figure 17 As shown, in one embodiment, Figure 16 The earthquake data noise reduction frequency compensation device shown may also include:

[0069] The function construction module 1701 is used to: determine the weights of the Shearlet denoising basis function and the topology basis function respectively; and, based on the weights of the Shearlet denoising basis function and the topology basis function, fuse the Shearlet denoising basis function as a regularization constraint term with the topology basis function to generate the compressed sensing noise-resistant topology basis function.

[0070] In one embodiment, the reflection coefficient solving module 1603 is specifically used for:

[0071] The wavelet waveform data of the original seismic data is used as the simulated wavelet data. It is input into the compressed sensing noise-resistant topology basis function and solved iteratively using the fast convex projection POCS algorithm to obtain the full bandwidth reflection coefficient.

[0072] In one embodiment, the spectrum splicing module 1604 is specifically used for:

[0073] Based on the full bandwidth reflection coefficient, the full bandwidth spectrum is extracted;

[0074] Based on the frequency compensation weight, the full bandwidth spectrum is spliced ​​with the original seismic data spectrum to obtain the extended spectrum.

[0075] In one embodiment, the spectrum splicing module 1604 is specifically used for:

[0076] During stitching, the full bandwidth spectrum is used for regions with frequencies higher than the first threshold or lower than the second threshold, while the original seismic data spectrum is used for regions with frequencies neither higher than the first threshold nor lower than the second threshold.

[0077] Based on the aforementioned inventive concept, such as Figure 18 As shown, the present invention also proposes a computer device 1800, including a memory 1810, a processor 1820, and a computer program 1830 stored in the memory 1810 and executable on the processor 1820. When the processor 1820 executes the computer program 1830, it implements the aforementioned earthquake data noise reduction frequency compensation method.

[0078] Based on the aforementioned inventive concept, the present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned seismic data noise reduction frequency compensation method.

[0079] Based on the aforementioned inventive concept, the present invention proposes a computer program product, which includes a computer program that, when executed by a processor, implements a method for noise reduction frequency compensation of seismic data.

[0080] In summary, in this embodiment of the invention, spectral analysis is performed on the original seismic data to obtain its frequency distribution, and the expected frequency broadening range is determined based on this distribution. Autocorrelation calculation is performed on the original seismic data to obtain wavelet-like waveform data. This wavelet-like waveform data is then used as simulated wavelet data and input into the compressed sensing noise-resistant topology basis function for iterative solution, resulting in the full-bandwidth reflection coefficient. Based on the full-bandwidth reflection coefficient and the expected frequency broadening range, the full-bandwidth spectrum is concatenated with the original seismic data spectrum to obtain the extended spectrum. Finally, the extended spectrum undergoes an inverse Fourier transform to obtain the noise-resistant topology data. The compressed sensing noise-resistant topology basis function uses the Shearlet denoising basis function as a regularization constraint term, which is fused with the topology basis function. Thus, based on the compressed sensing framework, and using the Shearlet denoising basis function to constrain the topology basis function, a compressed sensing noise-resistant topology basis function is constructed, achieving good high- and low-frequency compensation effects for seismic data under low signal-to-noise ratio conditions.

[0081] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0083] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0084] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0085] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for noise reduction frequency compensation of seismic data, characterized in that, include: Spectral analysis is performed on the raw seismic data to obtain its frequency distribution, and the expected frequency broadening range is determined based on the frequency distribution of the raw seismic data. Autocorrelation calculations are performed on the seismic data to obtain wavelet-like waveform data of the original seismic data; The wavelet-like waveform data of the original seismic data is used as simulated wavelet data and input into the compressed sensing noise-resistant topology basis function for iterative solution to obtain the full bandwidth reflection coefficient; the compressed sensing noise-resistant topology basis function is generated by fusing the Shearlet denoising basis function as a regularization constraint term with the topology basis function. Based on the full bandwidth reflection coefficient and the expected frequency broadening range, the full bandwidth spectrum is spliced ​​with the original seismic data spectrum to obtain the extended spectrum; The spread spectrum is subjected to inverse Fourier transform to obtain noise-resistant spread spectrum data.

2. The method as described in claim 1, characterized in that, Also includes: Determine the weights of the Shearlet denoising basis function and the frequency extension basis function respectively; Based on the weights of the Shearlet denoising basis function and the topology basis function, the Shearlet denoising basis function is used as a regularization constraint term and fused with the topology basis function to generate the compressed sensing noise-resistant topology basis function.

3. The method as described in claim 1, characterized in that, The wavelet-like waveform data from the original seismic data is used as simulated wavelet data and input into the compressed sensing noise-resistant topology basis function for iterative solution. The full bandwidth reflection coefficients are then obtained through inversion, including: The wavelet waveform data of the original seismic data is used as the simulated wavelet data. It is input into the compressed sensing noise-resistant topology basis function and solved iteratively using the fast convex projection POCS algorithm to obtain the full bandwidth reflection coefficient.

4. The method as described in claim 1, characterized in that, Based on the full-bandwidth reflection coefficient and the predicted frequency broadening range, the full-bandwidth spectrum is stitched together with the original seismic data spectrum to obtain the extended spectrum, including: Based on the full bandwidth reflection coefficient, the full bandwidth spectrum is extracted; Based on the frequency compensation weight, the full bandwidth spectrum is spliced ​​with the original seismic data spectrum to obtain the extended spectrum.

5. The method as described in claim 1, characterized in that, Based on the full-bandwidth reflection coefficient and the predicted frequency broadening range, the full-bandwidth spectrum is stitched together with the original seismic data spectrum to obtain the extended spectrum, including: During stitching, the full bandwidth spectrum is used for regions with frequencies higher than the first threshold or lower than the second threshold, while the original seismic data spectrum is used for regions with frequencies neither higher than the first threshold nor lower than the second threshold.

6. A seismic data noise reduction frequency compensation device, characterized in that, include: The spectrum analysis module is used to perform spectrum analysis on the raw seismic data to obtain the frequency distribution of the raw seismic data, and to determine the expected frequency broadening range based on the frequency distribution of the raw seismic data. The wavelet estimation module is used to perform autocorrelation calculations on the raw seismic data to obtain wavelet-like waveform data of the raw seismic data; The reflection coefficient solution module is used to take the wavelet-like waveform data of the original seismic data as simulated wavelet data, input the compressed sensing noise-resistant topology basis function for iterative solution, and invert to obtain the full bandwidth reflection coefficient; the compressed sensing noise-resistant topology basis function is generated by fusing the Shearlet denoising basis function as a regularization constraint term with the topology basis function. The spectrum stitching module is used to stitch the full-bandwidth spectrum with the original seismic data spectrum based on the full-bandwidth reflection coefficient and the expected frequency widening range to obtain the extended spectrum; The spectrum spreading module is used to perform an inverse Fourier transform on the spread spectrum to obtain noise-resistant spread spectrum data.

7. The apparatus as claimed in claim 6, characterized in that, Also includes: The function building module is used to determine the weights of the Shearlet denoising basis function and the topology basis function, respectively; Based on the weights of the Shearlet denoising basis function and the topology basis function, the Shearlet denoising basis function is used as a regularization constraint term and fused with the topology basis function to generate the compressed sensing noise-resistant topology basis function.

8. The apparatus as claimed in claim 6, characterized in that, The reflection coefficient calculation module is specifically used for: The wavelet waveform data of the original seismic data is used as the simulated wavelet data. It is input into the compressed sensing noise-resistant topology basis function and solved iteratively using the fast convex projection POCS algorithm to obtain the full bandwidth reflection coefficient.

9. The apparatus as claimed in claim 6, characterized in that, The spectrum splicing module is specifically used for: Based on the full bandwidth reflection coefficient, the full bandwidth spectrum is extracted; Based on the frequency compensation weight, the full bandwidth spectrum is spliced ​​with the original seismic data spectrum to obtain the extended spectrum.

10. The apparatus as claimed in claim 6, characterized in that, The spectrum splicing module is specifically used for: During stitching, the full bandwidth spectrum is used for regions with frequencies higher than the first threshold or lower than the second threshold, while the original seismic data spectrum is used for regions with frequencies neither higher than the first threshold nor lower than the second threshold.

11. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 5.

13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 5.

Images