A surface wave suppression method and device based on three-dimensional cone filtering and mean weighting

Abstract

The application relates to the field of seismic signal processing, and discloses a surface wave suppression method and device based on three-dimensional conical filtering and mean value weighting. The method comprises the following steps: performing three-dimensional conical filtering processing on original seismic data to obtain first processed data; determining noise data according to the original seismic data and the first processed data; performing preset surface wave prediction processing on the noise data to obtain second processed data; and determining denoised data according to the original seismic data and the second processed data. Through combined use of the two methods, the advantages of the two methods are complementary, surface waves can be effectively suppressed, effective signals are well protected, and fidelity and amplitude-preserved denoising is realized.

Description

Technical Field

[0001] This application relates to the field of seismic signal processing technology, and in particular to a method, apparatus, storage medium, and electronic device for surface wave suppression based on three-dimensional cone filtering and mean weighting. Background Technology

[0002] The purpose of the background description provided herein is to give an overall background to this application. The statements in this section are merely to provide background information relevant to this application and do not necessarily constitute prior art.

[0003] Currently, commonly used surface wave interference suppression methods mainly include: FK apparent velocity filtering, wavelet transform inverse thresholding elimination method, wavelet domain blind climbing fitting subtraction method, mean weighted elimination method, and energy attenuation method, etc.

[0004] FK apparent velocity filtering primarily utilizes Fourier transform to directly remove noise signals in the frequency-wavenumber domain, and then performs inverse Fourier transform on the noise-removed result to achieve coherent noise suppression. However, this method suffers from spurious frequency effects due to the absence of certain frequency components in the data, resulting in significant damage to the effective signal and noticeable mixing effects.

[0005] The wavelet transform inverse thresholding method mainly uses wavelet transform instead of Fourier transform to remove noise signals within a certain sector window in the wavelet transform domain. Although the transform itself has many advantages, the idea of ​​removing noise to achieve denoising still has the same defects as FK apparent velocity filtering.

[0006] The wavelet domain blind hill-climbing fitting subtraction method mainly employs a blind hill-climbing search at the wavelet scale containing surface waves. It extracts surface wave components through energy and time-difference relationships, then reconstructs the surface wave signal using wavelets. The extracted surface waves are then subtracted from the original record to achieve denoising. This method is extremely time-consuming in extracting surface waves and is not commonly used for pre-stack denoising of actual seismic data.

[0007] The mean-weighted noise reduction method mainly utilizes the frequency and spatial characteristics of noise to eliminate surface waves by fitting coherent noise. However, surface wave removal also results in significant damage to the effective signal. Especially when applied to coherent noise suppression in far-space arrays, the time-distance curve of the coherent noise becomes hyperbolic, similar to the characteristics of the effective reflected wave, leading to severe problems of insufficient noise reduction or significant damage to the effective signal.

[0008] Energy attenuation methods primarily rely on the significant difference between the effective wave energy and the surface wave energy within the surface wave band for denoising. This method involves multi-channel, frequency-divided, and time-windowed statistical averaging of the energy within the surface wave band, and calculating the ratio of the sample energy to the average energy at a given moment. If this ratio exceeds a given threshold parameter, the signal at that sample point is considered predominantly surface wave and attenuated; otherwise, it is retained. However, this method is ineffective when surface waves are present in every channel of a given moment, exhibiting significant denoising deficiencies or damage to the effective signal.

[0009] Therefore, there is an urgent need for a new surface wave suppression method that can be applied to various seismic data to address the shortcomings mentioned above. Summary of the Invention

[0010] To address the aforementioned issues, this application proposes a surface wave suppression method, apparatus, storage medium, and electronic device based on three-dimensional cone filtering and mean weighting. For processing massive amounts of pre-stack seismic data, surface wave suppression requires consideration not only of the method's effectiveness but also its operational efficiency. The method disclosed in this application complements the advantages of both methods, overcoming the shortcomings of a single method and achieving fidelity-preserving and amplitude-preserving denoising. Simultaneously, it leverages the advantages of both methods—simple parameter selection and fast computation speed—and improves processing efficiency.

[0011] The first aspect of this application provides a surface wave suppression method based on three-dimensional cone filtering and mean weighting, the method comprising:

[0012] The original seismic data is subjected to three-dimensional cone filtering to obtain the first processed data.

[0013] Noise data is determined based on the original seismic data and the first data;

[0014] The noise data is subjected to a preset surface wave prediction process to obtain the processed second data;

[0015] The denoised data is determined based on the original seismic data and the second data.

[0016] Furthermore, the raw seismic data includes:

[0017] The cross-shaped arrangement domain contains raw seismic data of surface waves.

[0018] Further, determining the noise data based on the original seismic data and the first data includes:

[0019] Subtract the first data from the original seismic data to obtain the noise data.

[0020] Furthermore, the preset surface wave prediction processing includes:

[0021] Mean-weighted surface wave prediction processing.

[0022] Further, determining the denoised data based on the original seismic data and the second data includes:

[0023] Subtract the second data from the original seismic data to obtain the denoised data.

[0024] A second aspect of this application provides a surface wave suppression device based on three-dimensional cone filtering and mean weighting, the device comprising:

[0025] The first processing module is used to perform three-dimensional cone filtering on the raw seismic data to obtain the processed first data.

[0026] A noise data determination module is used to determine noise data based on the original seismic data and the first data;

[0027] The second processing module is used to perform preset surface wave prediction processing on the noise data to obtain the processed second data.

[0028] A denoising module is used to determine the denoised data based on the original seismic data and the second data.

[0029] Furthermore, the noise data determination module is used to subtract the first data from the original seismic data to obtain noise data.

[0030] Furthermore, the denoising module is used to subtract the second data from the original seismic data to obtain the denoised data.

[0031] A third aspect of this application provides a computer-readable storage medium storing a computer program that can be executed by one or more processors to implement the steps of the method described above.

[0032] A fourth aspect of this application provides an electronic device including a memory and one or more processors, wherein a computer program is stored on the memory, and the memory and the one or more processors are communicatively connected to each other, wherein when the computer program is executed by the one or more processors, it implements the steps of the method described above.

[0033] Compared with the prior art, the advantages or beneficial effects of the technical solution of this application include:

[0034] This application proposes a surface wave suppression method that features simple parameter selection, fast operation, and good denoising effect. It overcomes the problem of incomplete denoising or severe damage to the effective signal caused by the difficulty in signal-to-noise separation when using three-dimensional cone filtering (3DFKK) and mean weighting methods to suppress surface waves alone. By combining the two methods, the advantages of the two methods can be complemented, effectively suppressing surface waves while protecting the effective signal, thus achieving good fidelity and amplitude preservation in denoising. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application 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 embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0036] It should also be noted that, for ease of description, only the parts relevant to this disclosure are shown in the accompanying drawings. The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions in this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0037] Figure 1 A flowchart illustrating a surface wave suppression method based on three-dimensional cone filtering and mean weighting, provided for an embodiment of this application;

[0038] Figure 2 A schematic diagram illustrating the effect of three-dimensional conical filtering in suppressing surface waves, provided in an embodiment of this application;

[0039] Figure 3 A schematic diagram illustrating the effect of mean-weighted surface wave suppression provided in an embodiment of this application;

[0040] Figure 4 A schematic diagram illustrating the suppression effect of three-dimensional cone filtering and mean-weighted surface waves provided in an embodiment of this application;

[0041] Figure 5 A flowchart illustrating another surface wave suppression method based on three-dimensional cone filtering and mean weighting provided in this application embodiment;

[0042] Figure 6 A schematic diagram of a surface wave suppression device based on three-dimensional cone filtering and mean weighting provided in an embodiment of this application;

[0043] Figure 7 This is a connection block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0044] The following detailed description of the embodiments of this application, in conjunction with the accompanying drawings, will provide a thorough understanding of how this application uses technical means to solve technical problems and achieve corresponding technical effects, enabling its implementation. The embodiments of this application and the various features within them can be combined with each other without conflict, and all resulting technical solutions are within the protection scope of this application.

[0045] It should be clearly stated that the embodiments described below are merely some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0046] As can be seen from the background technology, the commonly used surface wave interference suppression methods mainly include: FK apparent velocity filtering, wavelet transform inverse thresholding elimination method, wavelet domain blind climbing fitting subtraction method, mean weighted elimination method, and energy attenuation method, etc.

[0047] FK apparent velocity filtering primarily utilizes Fourier transform to directly remove noise signals in the frequency-wavenumber domain, and then performs inverse Fourier transform on the noise-removed result to achieve coherent noise suppression. However, this method suffers from spurious frequency effects due to the absence of certain frequency components in the data, resulting in significant damage to the effective signal and noticeable mixing effects.

[0048] The wavelet transform inverse thresholding method mainly uses wavelet transform instead of Fourier transform to remove noise signals within a certain sector window in the wavelet transform domain. Although the transform itself has many advantages, the idea of ​​removing noise to achieve denoising still has the same defects as FK apparent velocity filtering.

[0049] The wavelet domain blind hill-climbing fitting subtraction method mainly employs a blind hill-climbing search at the wavelet scale containing surface waves. It extracts surface wave components through energy and time-difference relationships, then reconstructs the surface wave signal using wavelets. The extracted surface waves are then subtracted from the original record to achieve denoising. This method is extremely time-consuming in extracting surface waves and is not commonly used for pre-stack denoising of actual seismic data.

[0050] The mean-weighted noise reduction method mainly utilizes the frequency and spatial characteristics of noise to eliminate surface waves by fitting coherent noise. However, surface wave removal also results in significant damage to the effective signal. Especially when applied to coherent noise suppression in far-space arrays, the time-distance curve of the coherent noise becomes hyperbolic, similar to the characteristics of the effective reflected wave, leading to severe problems of insufficient noise reduction or significant damage to the effective signal.

[0051] Energy attenuation methods primarily rely on the significant difference between the effective wave energy and the surface wave energy within the surface wave band for denoising. This method involves multi-channel, frequency-divided, and time-windowed statistical averaging of the energy within the surface wave band, and calculating the ratio of the sample energy to the average energy at a given moment. If this ratio exceeds a given threshold parameter, the signal at that sample point is considered predominantly surface wave and attenuated; otherwise, it is retained. However, this method is ineffective when surface waves are present in every channel of a given moment, exhibiting significant denoising deficiencies or damage to the effective signal.

[0052] In view of this, this application proposes a surface wave suppression method, apparatus, storage medium, and electronic device based on three-dimensional cone filtering and mean weighting. For processing massive amounts of pre-stack seismic data, surface wave suppression requires consideration not only of the method's effectiveness but also its operational efficiency. The method disclosed in this application achieves the complementary advantages of both methods, overcoming the shortcomings of a single method and achieving fidelity-preserving and amplitude-preserving denoising; it also leverages the advantages of both methods—simple parameter selection and fast computation speed—and improves processing efficiency.

[0053] Example 1

[0054] This embodiment provides a surface wave suppression method based on three-dimensional cone filtering and mean weighting.

[0055] Currently, the commonly used three-dimensional cone filtering method uses 3DFKK filtering in the cross-shaped arrangement domain for denoising. This method fully considers the characteristic that linear interference in seismic data is widely distributed throughout the entire three-dimensional space. It applies three-dimensional Fourier transform and denoises by adjusting the dip angle parameter, that is, by adjusting the inter-trace time difference parameter of the surface wave signal, which can achieve complete noise removal. However, since the denoising process directly removes a signal component in the frequency-wavenumber domain, it will lead to spurious frequency effects, which manifest as false phase axes in the denoised seismic record. If the surface wave noise to be removed has a high frequency and high velocity, this method will severely damage the effective signal and have obvious mixing effects while denoising; if the effective signal is to be protected, a lot of surface wave interference will remain.

[0056] The mean-weighted linear noise suppression method can effectively suppress coherent noise by strictly specifying the frequency and spatial characteristics of the noise. However, because the time-distance curve of far-spaced coherent noise becomes hyperbolic, similar to the characteristics of effective reflection, achieving complete noise suppression will also damage a significant amount of the effective signal, resulting in a mixing phenomenon.

[0057] To avoid incomplete denoising or severe damage to the effective signal caused by using only three-dimensional cone filtering or mean-weighted linear coherent noise suppression methods, this embodiment proposes a mean-weighted linear interference suppression method based on three-dimensional cone filtering.

[0058] This method selects a relatively large tilt angle parameter during the cross-shaped conical filtering to ensure that most of the surface wave to be suppressed is within the noise domain, thus initially achieving a rough separation between the surface wave and the effective signal. At this point, there is a small amount of effective signal in the noise-prone single shot. Then, the surface wave is predicted using a mean-weighted method in the 3DFKK noise domain. During surface wave fitting, the frequency and velocity of the noise are reasonably controlled, and an appropriate effective signal protection window is selected to achieve the goal of minimizing the presence of effective signal in the predicted surface wave. Finally, the predicted surface wave model is subtracted from the original data to obtain the denoised data. The advantage of this method is that it combines the strengths of two methods to better achieve signal-to-noise separation. By using a subtraction method, it effectively suppresses the surface wave while better protecting the effective signal, achieving fidelity-preserving and amplitude-preserving denoising.

[0059] As an example, Figure 1 A flowchart illustrating a surface wave suppression method based on three-dimensional cone filtering and mean weighting, provided for embodiments of this application, is shown below. Figure 1 As shown, the method disclosed in this embodiment includes the following steps:

[0060] Step 110: Perform three-dimensional cone filtering on the original seismic data to obtain the first processed data;

[0061] Step 120: Determine noise data based on the original seismic data and the first data;

[0062] Step 130: Perform preset surface wave prediction processing on the noise data to obtain the processed second data;

[0063] Step 140: Determine the denoised data based on the original seismic data and the second data.

[0064] In some embodiments, the raw seismic data in step 110 may be raw seismic data containing surface waves in a cross-shaped arrangement domain.

[0065] In some embodiments, the step 120 of determining noise data based on the original seismic data and the first data may specifically include the following implementation:

[0066] Subtract the first data from the original seismic data to obtain the noise data.

[0067] In some embodiments, the preset surface wave prediction processing described in step 130 may specifically include:

[0068] Mean-weighted surface wave prediction processing.

[0069] In some embodiments, the step 140 of determining the denoised data based on the original seismic data and the second data can specifically include the following implementation:

[0070] Subtract the second data from the original seismic data to obtain the denoised data.

[0071] This embodiment provides a surface wave suppression method based on three-dimensional cone filtering and mean weighting, which complements the advantages of both methods, overcomes the shortcomings of a single method, and achieves fidelity-preserving and amplitude-preserving denoising. It also leverages the advantages of both methods—simple parameter selection and fast computation speed—and improves processing efficiency. Specifically, it includes the following steps: Step 110: Perform three-dimensional cone filtering on the original seismic data to obtain processed first data; Step 120: Determine noise data based on the original seismic data and the first data; Step 130: Perform preset surface wave prediction processing on the noise data to obtain processed second data; Step 140: Determine denoised data based on the original seismic data and the second data. This overcomes the problem of incomplete denoising or severe damage to the effective signal caused by the difficulty of signal-to-noise separation when using only three-dimensional cone filtering and mean weighting to suppress surface waves. By combining the two methods, their advantages are complementary, effectively suppressing surface waves while protecting the effective signal, thus achieving good fidelity-preserving and amplitude-preserving denoising.

[0072] Example 2

[0073] This embodiment further illustrates the surface wave suppression method based on three-dimensional cone filtering and mean weighting disclosed in this application, based on Embodiment 1.

[0074] Currently, the commonly used three-dimensional cone filtering method uses 3DFKK filtering in the cross-shaped arrangement domain for denoising. This method fully considers the characteristic that linear interference in seismic data is widely distributed throughout the entire three-dimensional space. It applies three-dimensional Fourier transform and denoises by adjusting the dip angle parameter, that is, by adjusting the inter-trace time difference parameter of the surface wave signal, which can achieve complete noise removal. However, since the denoising process directly removes a signal component in the frequency-wavenumber domain, it will lead to spurious frequency effects, which manifest as false phase axes in the denoised seismic record. If the surface wave noise to be removed has a high frequency and high velocity, this method will severely damage the effective signal and have obvious mixing effects while denoising; if the effective signal is to be protected, a lot of surface wave interference will remain.

[0075] The mean-weighted linear noise suppression method can effectively suppress coherent noise by strictly specifying the frequency and spatial characteristics of the noise. However, because the time-distance curve of far-spaced coherent noise becomes hyperbolic, similar to the characteristics of effective reflection, achieving complete noise suppression will also damage a significant amount of the effective signal, resulting in a mixing phenomenon.

[0076] To avoid incomplete denoising or severe damage to the effective signal caused by using only three-dimensional cone filtering or mean-weighted linear coherent noise suppression methods, this embodiment proposes a mean-weighted linear interference suppression method based on three-dimensional cone filtering.

[0077] This method selects a relatively large tilt angle parameter during the cross-shaped conical filtering to ensure that most of the surface wave to be suppressed is within the noise domain, thus initially achieving a rough separation between the surface wave and the effective signal. At this point, there is a small amount of effective signal in the noise-prone single shot. Then, the surface wave is predicted using a mean-weighted method in the 3DFKK noise domain. During surface wave fitting, the frequency and velocity of the noise are reasonably controlled, and an appropriate effective signal protection window is selected to achieve the goal of minimizing the presence of effective signal in the predicted surface wave. Finally, the predicted surface wave model is subtracted from the original data to obtain the denoised data. The advantage of this method is that it combines the strengths of two methods to better achieve signal-to-noise separation. By using a subtraction method, it effectively suppresses the surface wave while better protecting the effective signal, achieving fidelity-preserving and amplitude-preserving denoising.

[0078] As an example, Figure 1 A flowchart illustrating a surface wave suppression method based on three-dimensional cone filtering and mean weighting, provided for embodiments of this application, is shown below. Figure 1 As shown, the method disclosed in this embodiment includes the following steps:

[0079] Step 110: Perform three-dimensional cone filtering on the original seismic data to obtain the first processed data.

[0080] In some embodiments, the raw seismic data includes:

[0081] The cross-shaped arrangement domain contains raw seismic data of surface waves.

[0082] As an example, the input cross-shaped arrangement of seismic data containing surface waves can be used as the raw seismic data.

[0083] As an example, the raw seismic data input in step 110 is subjected to three-dimensional cone filtering to obtain the first processed data.

[0084] Step 120: Determine noise data based on the original seismic data and the first data.

[0085] In some embodiments, determining noise data based on the original seismic data and the first data includes:

[0086] Subtract the first data from the original seismic data to obtain the noise data.

[0087] As an example, subtracting the processed first data obtained in step 110 from the input data in step 110 yields 3DFKK noise data.

[0088] Step 130: Perform preset surface wave prediction processing on the noise data to obtain the processed second data.

[0089] In some embodiments, the preset surface wave prediction process includes:

[0090] Mean-weighted surface wave prediction processing.

[0091] As an example, the preset surface wave prediction processing can select mean-weighted surface wave prediction processing, and perform mean-weighted surface wave prediction processing on the 3DFKK noise data obtained in step 120 to obtain the processed second data.

[0092] Step 140: Determine the denoised data based on the original seismic data and the second data.

[0093] In some embodiments, determining the denoised data based on the original seismic data and the second data includes:

[0094] Subtract the second data from the original seismic data to obtain the denoised data.

[0095] As an example, subtracting the processed second data obtained in step 130 from the original seismic data input in step 110 yields the denoised data.

[0096] This embodiment provides a surface wave suppression method based on three-dimensional cone filtering and mean weighting, which complements the advantages of both methods, overcomes the shortcomings of a single method, and achieves fidelity-preserving and amplitude-preserving denoising. It also leverages the advantages of both methods—simple parameter selection and fast computation speed—and improves processing efficiency. Specifically, it includes the following steps: Step 110: Perform three-dimensional cone filtering on the original seismic data to obtain processed first data; Step 120: Determine noise data based on the original seismic data and the first data; Step 130: Perform preset surface wave prediction processing on the noise data to obtain processed second data; Step 140: Determine denoised data based on the original seismic data and the second data. This overcomes the problem of incomplete denoising or severe damage to the effective signal caused by the difficulty of signal-to-noise separation when using only three-dimensional cone filtering and mean weighting to suppress surface waves. By combining the two methods, their advantages are complementary, effectively suppressing surface waves while protecting the effective signal, thus achieving good fidelity-preserving and amplitude-preserving denoising.

[0097] Example 3

[0098] This embodiment is a specific example. In this embodiment, it is verified that the method disclosed in this application protects the effective signal well, there is no obvious effective information in the noise record, and it achieves good fidelity and amplitude preservation denoising.

[0099] Currently, the commonly used three-dimensional cone filtering method uses 3DFKK filtering in the cross-shaped arrangement domain for denoising. This method fully considers the characteristic that linear interference in seismic data is widely distributed throughout the entire three-dimensional space. It applies three-dimensional Fourier transform and denoises by adjusting the dip angle parameter, that is, by adjusting the inter-trace time difference parameter of the surface wave signal, which can achieve complete noise removal. However, since the denoising process directly removes a signal component in the frequency-wavenumber domain, it will lead to spurious frequency effects, which manifest as false phase axes in the denoised seismic record. If the surface wave noise to be removed has a high frequency and high velocity, this method will severely damage the effective signal and have obvious mixing effects while denoising; if the effective signal is to be protected, a lot of surface wave interference will remain.

[0100] The mean-weighted linear noise suppression method can effectively suppress coherent noise by strictly specifying the frequency and spatial characteristics of the noise. However, because the time-distance curve of far-spaced coherent noise becomes hyperbolic, similar to the characteristics of effective reflection, achieving complete noise suppression will also damage a significant amount of the effective signal, resulting in a mixing phenomenon.

[0101] To avoid incomplete denoising or severe damage to the effective signal caused by using only three-dimensional cone filtering or mean-weighted linear coherent noise suppression methods, this embodiment proposes a mean-weighted linear interference suppression method based on three-dimensional cone filtering.

[0102] This method selects a relatively large tilt angle parameter during the cross-shaped conical filtering to ensure that most of the surface wave to be suppressed is within the noise domain, thus initially achieving a rough separation between the surface wave and the effective signal. At this point, there is a small amount of effective signal in the noise-prone single shot. Then, the surface wave is predicted using a mean-weighted method in the 3DFKK noise domain. During surface wave fitting, the frequency and velocity of the noise are reasonably controlled, and an appropriate effective signal protection window is selected to achieve the goal of minimizing the presence of effective signal in the predicted surface wave. Finally, the predicted surface wave model is subtracted from the original data to obtain the denoised data. The advantage of this method is that it combines the strengths of two methods to better achieve signal-to-noise separation. By using a subtraction method, it effectively suppresses the surface wave while better protecting the effective signal, achieving fidelity-preserving and amplitude-preserving denoising.

[0103] In this embodiment, the surface wave suppression method based on three-dimensional cone filtering and mean weighting disclosed in this application is applied to the three-dimensional seismic data processing of a certain work area.

[0104] As an example, Figure 1A flowchart illustrating a surface wave suppression method based on three-dimensional cone filtering and mean weighting, provided for embodiments of this application, is shown below. Figure 1 As shown, the verification process using the method disclosed in this application includes the following steps:

[0105] Step 110: Perform three-dimensional cone filtering on the original seismic data to obtain the first processed data.

[0106] In some embodiments, the raw seismic data includes:

[0107] The cross-shaped arrangement domain contains raw seismic data of surface waves.

[0108] As an example, the input cross-shaped arrangement of seismic data containing surface waves can be used as the raw seismic data.

[0109] As an example, the raw seismic data input in step 110 is subjected to three-dimensional cone filtering to obtain the first processed data.

[0110] like Figure 2 As shown, when a single cross-shaped domain three-dimensional cone filtering method is used to suppress surface waves, a large amount of surface wave information remains in the denoised single-shot record.

[0111] Step 120: Determine noise data based on the original seismic data and the first data.

[0112] In some embodiments, determining noise data based on the original seismic data and the first data includes:

[0113] Subtract the first data from the original seismic data to obtain the noise data.

[0114] As an example, subtracting the processed first data obtained in step 110 from the input data in step 110 yields 3DFKK noise data.

[0115] Step 130: Perform preset surface wave prediction processing on the noise data to obtain the processed second data.

[0116] In some embodiments, the preset surface wave prediction process includes:

[0117] Mean-weighted surface wave prediction processing.

[0118] As an example, the preset surface wave prediction processing can select mean-weighted surface wave prediction processing, and perform mean-weighted surface wave prediction processing on the 3DFKK noise data obtained in step 120 to obtain the processed second data.

[0119] like Figure 3As shown, when using a single mean-weighted method to suppress surface waves, the effective signal is severely damaged in the far-field data due to the hyperbolic nature of the surface wave time-distance curve, and the noise contains a large amount of effective reflected signal.

[0120] Step 140: Determine the denoised data based on the original seismic data and the second data.

[0121] In some embodiments, determining the denoised data based on the original seismic data and the second data includes:

[0122] Subtract the second data from the original seismic data to obtain the denoised data.

[0123] As an example, subtracting the processed second data obtained in step 130 from the original seismic data input in step 110 yields the denoised data.

[0124] like Figure 4 As shown, the method presented in this paper effectively suppresses surface waves while protecting the effective signal well. There is no obvious effective information in the noise recording, and it achieves good fidelity and amplitude preservation in noise reduction.

[0125] Furthermore, for a better understanding of the technical solution of this application, please refer to... Figure 5 .

[0126] Example 4

[0127] This embodiment provides a surface wave suppression device based on three-dimensional cone filtering and mean weighting. This device embodiment can be used to execute the method embodiment of this application. For details not disclosed in this device embodiment, please refer to the method embodiment of this application. Figure 6 A schematic diagram of a surface wave suppression device based on three-dimensional cone filtering and mean weighting is provided for an embodiment of this application, as shown below. Figure 6 As shown, the apparatus 600 disclosed in this embodiment includes:

[0128] The first processing module 601 is used to perform three-dimensional cone filtering on the original seismic data to obtain the processed first data.

[0129] Noise data determination module 602 is used to determine noise data based on the original seismic data and the first data;

[0130] The second processing module 603 is used to perform preset surface wave prediction processing on the noise data to obtain the processed second data.

[0131] The denoising module 604 is used to determine the denoised data based on the original seismic data and the second data.

[0132] In some embodiments, the noise data determination module 602 is used to subtract the first data from the original seismic data to obtain noise data.

[0133] In some embodiments, the denoising module 604 is used to subtract the second data from the original seismic data to obtain denoised data.

[0134] In some embodiments, the raw seismic data includes:

[0135] The cross-shaped arrangement domain contains raw seismic data of surface waves.

[0136] In some embodiments, the preset surface wave prediction process includes:

[0137] Mean-weighted surface wave prediction processing.

[0138] Those skilled in the field can understand that Figure 6 The structures shown do not constitute a limitation on the apparatus of the embodiments of this application. They may include more or fewer modules / units than shown, or combine certain modules / units, or have different module / unit arrangements.

[0139] Those skilled in the art will understand that the modules or steps described above can be implemented using general-purpose computing devices, either centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device. Furthermore, in some cases, the steps shown or described can be performed in a different order than presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of each module in the device can be referred to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0141] The apparatus provided in this embodiment includes: a first processing module 601, used to perform three-dimensional cone filtering on the original seismic data to obtain processed first data; a noise data determination module 602, used to determine noise data based on the original seismic data and the first data; a second processing module 603, used to perform preset surface wave prediction processing on the noise data to obtain processed second data; and a denoising module 604, used to determine denoised data based on the original seismic data and the second data. This overcomes the problem of incomplete denoising or severe damage to the effective signal caused by the difficulty in signal-to-noise separation when using only three-dimensional cone filtering and mean-weighted methods to suppress surface waves. By combining the two methods, the advantages of both methods can be complemented, effectively suppressing surface waves while protecting the effective signal, thus achieving good fidelity and amplitude-preserving denoising.

[0142] Example 5

[0143] This embodiment provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, can implement some or all of the steps of the method as described in the foregoing method embodiments.

[0144] The original seismic data is subjected to three-dimensional cone filtering to obtain the first processed data.

[0145] Noise data is determined based on the original seismic data and the first data;

[0146] The noise data is subjected to a preset surface wave prediction process to obtain the processed second data;

[0147] The denoised data is determined based on the original seismic data and the second data.

[0148] Computer-readable storage media may individually include computer programs, data files, data structures, etc., or combinations thereof. The computer-readable storage media or computer program may be specifically designed and understood by those skilled in the art of computer software, or the computer-readable storage media may be known and available to those skilled in the art of computer software. Examples of computer-readable storage media include: magnetic media, such as hard disks, floppy disks, and magnetic tapes; optical media, such as CD-ROMs and DVDs; magneto-optical media, such as optical discs; and hardware devices specifically configured to store and execute computer programs, such as read-only memory (ROM), random access memory (RAM), flash memory; or servers, application stores, etc. Examples of computer programs include machine code (e.g., code generated by a compiler) and files containing high-level code that can be executed by a computer using an interpreter. The described hardware devices may be configured to function as one or more software modules to perform the operations and methods described above, and vice versa. Furthermore, computer-readable storage media may be distributed across networked computer systems, allowing for the decentralized storage and execution of program code or computer programs.

[0149] Example 6

[0150] This embodiment provides a computer program product. The computer program product includes a computer program or instructions, which, when executed by a processor, implement all or part of the steps of the method as described in the foregoing method embodiments; these will not be repeated here.

[0151] Furthermore, the computer program product may include one or more computer-executable components configured to perform the embodiments when the program is run; the computer program product may also include a computer program tangibly contained on a readable medium thereof, the computer program containing program code for performing any of the methods in the embodiments of this disclosure. In such embodiments, the computer program may be downloaded and installed from a network via a communication component, and / or installed from a removable medium.

[0152] Example 7

[0153] This embodiment provides an electronic device. Figure 7 A connection block diagram of an electronic device provided in an embodiment of this application, such as... Figure 7 As shown, the electronic device 700 may include: one or more processors 701, memory 702, multimedia components 703, input / output (I / O) interface 704, and communication components 705.

[0154] One or more processors 701 are used to execute all or part of the steps as described in the foregoing method embodiments. Memory 702 is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.

[0155] The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0156] One or more processors 701 may be implemented as an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and are used to perform all or part of the steps as described in the foregoing method embodiments:

[0157] The original seismic data is subjected to three-dimensional cone filtering to obtain the first processed data.

[0158] Noise data is determined based on the original seismic data and the first data;

[0159] The noise data is subjected to a preset surface wave prediction process to obtain the processed second data;

[0160] The denoised data is determined based on the original seismic data and the second data.

[0161] Multimedia component 703 may include a screen, which may be a touchscreen, and an audio component for outputting and / or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory or transmitted via a communication component. The audio component also includes at least one speaker for outputting audio signals.

[0162] I / O interface 704 provides an interface between one or more processors 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons can be virtual buttons or physical buttons.

[0163] The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wired communication includes communication via network port, serial port, etc.; wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, 5G, or one or more combinations thereof.

[0164] In summary, this application provides a surface wave suppression method, apparatus, computer-readable storage medium, and electronic device based on three-dimensional cone filtering and mean weighting. The method complements the advantages of both methods, overcoming the shortcomings of a single method and achieving fidelity-preserving and amplitude-preserving denoising. It also leverages the advantages of both methods—simple parameter selection and fast computation speed—and improves processing efficiency. Specifically, it includes the following steps: Step 110: Perform three-dimensional cone filtering on the original seismic data to obtain processed first data; Step 120: Determine noise data based on the original seismic data and the first data; Step 130: Perform preset surface wave prediction processing on the noise data to obtain processed second data; Step 140: Determine denoised data based on the original seismic data and the second data. This overcomes the problem of incomplete denoising or severe damage to the effective signal caused by difficulties in signal-to-noise separation when using only three-dimensional cone filtering and mean weighting to suppress surface waves. By combining the two methods, their advantages are complementary, effectively suppressing surface waves while protecting the effective signal, thus achieving good fidelity-preserving and amplitude-preserving denoising.

[0165] It should also be understood that the methods or apparatuses disclosed in the embodiments provided in this application can also be implemented in other ways. The method or apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functions, and operations of possible implementations of methods and apparatuses according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, computer program segment, or part of a computer program, which includes one or more computer programs for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings, and may actually be executed substantially in parallel. They may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer programs.

[0166] In this application, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "including one..." does not exclude the presence of other identical elements in the process, method, apparatus, or device that includes the element; the use of terms such as "first" and "second" is for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly indicating the number or sequence of the indicated technical features; in the description of this application, unless otherwise stated, the terms "multiple" or "many" mean at least two; if a server is described, it should be noted that a server can be an independent physical server or terminal, or a server cluster consisting of multiple physical servers, or a cloud server capable of providing basic cloud computing services such as cloud servers, cloud databases, cloud storage, and CDN; if a smart terminal or mobile device is described in this application, it should be noted that a smart terminal or mobile device can be a mobile phone, tablet computer, smartwatch, netbook, wearable electronic device, personal digital assistant (PDA), augmented reality (AR) device, virtual reality (VR) device, smart TV, smart speaker, personal computer (PC). Computer (PC) etc., but not limited to these, this application does not make any special restrictions on the specific form of smart terminals or mobile devices.

[0167] Finally, it should be noted that in the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "a single example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0168] Although embodiments of this application have been shown and described above, it is to be understood that the above embodiments are exemplary and the content is only for the purpose of facilitating understanding of this application, and is not intended to limit this application. Any person skilled in the art to which this application pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed in this application, but the scope of protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. A surface wave suppression method based on three-dimensional cone filtering and mean weighting, characterized in that, The method includes: The original seismic data is subjected to three-dimensional cone filtering to obtain the first processed data. Noise data is determined based on the original seismic data and the first data; The noise data is subjected to a preset surface wave prediction process to obtain the processed second data; The denoised data is determined based on the original seismic data and the second data.

2. The surface wave suppression method based on three-dimensional cone filtering and mean weighting according to claim 1, characterized in that, The raw seismic data includes: The cross-shaped arrangement domain contains raw seismic data of surface waves.

3. The surface wave suppression method based on three-dimensional cone filtering and mean weighting according to claim 1, characterized in that, The step of determining noise data based on the original seismic data and the first data includes: Subtract the first data from the original seismic data to obtain the noise data.

4. The surface wave suppression method based on three-dimensional cone filtering and mean weighting according to claim 1, characterized in that, The preset surface wave prediction processing includes: Mean-weighted surface wave prediction processing.

5. The surface wave suppression method based on three-dimensional cone filtering and mean weighting according to claim 1, characterized in that, The step of determining the denoised data based on the original seismic data and the second data includes: Subtract the second data from the original seismic data to obtain the denoised data.

6. A surface wave suppression device based on three-dimensional cone filtering and mean weighting, characterized in that, include: The first processing module is used to perform three-dimensional cone filtering on the raw seismic data to obtain the processed first data. A noise data determination module is used to determine noise data based on the original seismic data and the first data; The second processing module is used to perform preset surface wave prediction processing on the noise data to obtain the processed second data. A denoising module is used to determine the denoised data based on the original seismic data and the second data.

7. The surface wave suppression device based on three-dimensional cone filtering and mean weighting according to claim 6, characterized in that, The noise data determination module is used to subtract the first data from the original seismic data to obtain noise data.

8. The surface wave suppression device based on three-dimensional cone filtering and mean weighting according to claim 6, characterized in that, The denoising module is used to subtract the second data from the original seismic data to obtain the denoised data.

9. A computer-readable storage medium, characterized in that, The computer program stored in the computer-readable storage medium, when executed by one or more processors, implements the surface wave suppression method based on three-dimensional cone filtering and mean weighting as described in any one of claims 1 to 5.

10. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the surface wave suppression method based on three-dimensional cone filtering and mean weighting as described in any one of claims 1 to 5.

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