Surface wave suppression method and device based on three-dimensional conical filtering and mean weighting

Abstract

The invention 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 processed first data; determining noise data according to the original seismic data and the first data; performing preset surface wave prediction processing on the noise data to obtain processed second data; and determining de-noised data according to the original seismic data and the second data. Through the combined use of the two methods, the advantages of the two methods can be complemented, effective signals are well protected while the surface waves are effectively suppressed, and fidelity and amplitude-preserving denoising are well realized.

Description

Technical Field

[0001] The present application relates to the technical field of seismic signal processing, and in particular to a surface roll suppression method, device, storage medium, and electronic device based on three-dimensional cone filtering and mean weighting. Background Art

[0002] The purpose of the background technology description provided here is to give an overall background of the present application. The statements in this section merely provide background related to the present application and do not necessarily constitute prior art.

[0003] At present, the commonly used surface roll interference suppression methods mainly include: FK apparent velocity filtering, wavelet transform inverse threshold elimination method, wavelet domain blind hill climbing fitting subtraction method, mean weighted elimination method and energy attenuation method, etc.

[0004] FK apparent velocity filtering primarily utilizes Fourier transforms to directly remove noise signals in the frequency-wavenumber domain. This process then performs an inverse Fourier transform on the noise-removed result to suppress coherent noise. This method, when used for denoising, can produce aliasing effects due to the absence of certain frequency components in the data, severely damaging the valid signal and creating a noticeable aliasing effect.

[0005] The wavelet transform inverse threshold elimination method mainly uses wavelet transform instead of Fourier transform to cut out the noise signal within a certain fan-shaped window in the wavelet transform domain. Although the transform itself has many advantages, the denoising method achieved by cutting out still has the same defects as the FK apparent velocity filter.

[0006] The wavelet-domain blind hill-climbing fitting and subtraction method primarily uses a blind hill-climbing search method on wavelet scales containing surface waves. It extracts surface wave components based on the relationship between energy and time differences. The surface wave signal is then reconstructed using wavelets. The extracted surface waves are then subtracted from the original recording to achieve denoising. This method is extremely time-consuming to extract surface waves and is not commonly used for pre-stack denoising of actual seismic data.

[0007] The mean-weighted elimination method primarily utilizes the frequency and spatial characteristics of noise, fitting coherent noise to eliminate ground roll. However, this method also severely damages the effective signal. In particular, when applied to coherent noise suppression in distant arrays, the time-distance curve of the coherent noise becomes hyperbolic, similar to the characteristics of the effective reflected wave. This can lead to inadequate denoising and severe signal damage.

[0008] The energy attenuation method primarily uses the large difference between effective wave energy and surface roll energy within the surface roll frequency band for denoising. This method calculates the average energy across multiple channels, frequency divisions, and time windows within the surface roll frequency band. It then calculates the ratio of the energy at a given point to the average energy. If this ratio exceeds a given threshold parameter, the signal at that point is considered to be primarily surface roll and is attenuated. Otherwise, the signal is retained. However, when surface roll is present in every recording channel at a given moment, this method is ineffective and may result in severe denoising or loss of 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 solve the above-mentioned defects. Summary of the Invention

[0010] In response to the above problems, the present application proposes a surface wave suppression method, device, storage medium, and electronic device based on three-dimensional cone filtering and mean weighting. For the processing of massive pre-stack seismic data, when performing surface wave suppression, it is necessary to consider not only the effectiveness of the method but also its operational efficiency. The method disclosed in the present application achieves the complementary advantages of the two methods, overcomes the defects of a single method, and realizes fidelity-preserving and amplitude-preserving denoising; at the same time, it takes advantage of the simple parameter selection and fast operation speed of the two methods, and improves processing efficiency.

[0011] In a first aspect of the present application, a surface roll suppression method based on three-dimensional cone filtering and mean weighting is provided, the method comprising:

[0012] Performing three-dimensional cone filtering on the original seismic data to obtain processed first data;

[0013] determining noise data based on the original seismic data and the first data;

[0014] performing a preset surface roll prediction process on the noise data to obtain processed second data;

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

[0016] Furthermore, the original seismic data includes:

[0017] The cross-array domain contains the original seismic data of surface waves.

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

[0019] The first data is subtracted from the original seismic data to obtain noise data.

[0020] Furthermore, the preset surface roll prediction process includes:

[0021] Mean-weighted surface roll prediction processing.

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

[0023] The second data is subtracted from the original seismic data to obtain denoised data.

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

[0025] A first processing module is used to perform three-dimensional cone filtering on the original seismic data to obtain processed first data;

[0026] a noise data determination module, configured to determine noise data based on the original seismic data and the first data;

[0027] a second processing module, configured to perform a preset surface roll prediction process on the noise data to obtain processed second data;

[0028] A denoising module is used to determine 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 denoised data.

[0031] According to a third aspect of the present application, a computer-readable storage medium is provided. The computer program stored in the computer-readable storage medium can be executed by one or more processors to implement the steps of the method described above.

[0032] The fourth aspect of the present application provides an electronic device comprising a memory and one or more processors, wherein a computer program is stored in the memory, and the memory and the one or more processors are communicatively connected to each other. When the computer program is executed by the one or more processors, the steps of the method described above are implemented.

[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 roll suppression method with simple parameter selection, fast running speed and good denoising effect. It overcomes the problems of incomplete denoising or serious damage to the effective signal caused by the difficulty in signal-noise separation when only three-dimensional cone filtering (3DFKK) and mean weighting method are used to suppress surface roll. By combining the two methods, the advantages of the two methods can be complementary. While effectively suppressing surface roll, the effective signal is well protected, and fidelity-preserving and amplitude-preserving denoising are better achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in the relevant field, other drawings can be obtained based on the provided drawings without any creative work.

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

[0037] Figure 1 A flow chart of a surface roll suppression method based on three-dimensional cone filtering and mean weighting provided in an embodiment of the present application;

[0038] Figure 2 A schematic diagram of a three-dimensional cone filter for suppressing surface rolls provided in an embodiment of the present application;

[0039] Figure 3 A schematic diagram of a mean-weighted surface roll suppression effect provided in an embodiment of the present application;

[0040] Figure 4 A schematic diagram of a surface roll suppression effect based on three-dimensional cone filtering and mean-weighted filtering provided in an embodiment of the present application;

[0041] Figure 5 A flowchart of another surface roll suppression method based on three-dimensional cone filtering and mean weighting provided in an embodiment of the present application;

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

[0043] Figure 7 A connection block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] The following will describe the implementation methods of this application in detail with reference to the accompanying drawings and examples, so that the application can fully understand how technical means are used to solve technical problems and achieve corresponding technical effects, and implement them accordingly. The embodiments of this application and the various features therein can be combined with each other without conflict, and the resulting technical solutions are all within the scope of protection of this application.

[0045] It should be clear that the embodiments described below are only some of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of this application.

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

[0047] FK apparent velocity filtering primarily utilizes Fourier transforms to directly remove noise signals in the frequency-wavenumber domain. This process then performs an inverse Fourier transform on the noise-removed result to suppress coherent noise. This method, when used for denoising, can produce aliasing effects due to the absence of certain frequency components in the data, severely damaging the valid signal and creating a noticeable aliasing effect.

[0048] The wavelet transform inverse threshold elimination method mainly uses wavelet transform instead of Fourier transform to cut out the noise signal within a certain fan-shaped window in the wavelet transform domain. Although the transform itself has many advantages, the denoising method achieved by cutting out still has the same defects as the FK apparent velocity filter.

[0049] The wavelet-domain blind hill-climbing fitting and subtraction method primarily uses a blind hill-climbing search method on wavelet scales containing surface waves. It extracts surface wave components based on the relationship between energy and time differences. The surface wave signal is then reconstructed using wavelets. The extracted surface waves are then subtracted from the original recording to achieve denoising. This method is extremely time-consuming to extract surface waves and is not commonly used for pre-stack denoising of actual seismic data.

[0050] The mean-weighted elimination method primarily utilizes the frequency and spatial characteristics of noise, fitting coherent noise to eliminate ground roll. However, this method also severely damages the effective signal. In particular, when applied to coherent noise suppression in distant arrays, the time-distance curve of the coherent noise becomes hyperbolic, similar to the characteristics of the effective reflected wave. This can lead to inadequate denoising and severe signal damage.

[0051] The energy attenuation method primarily uses the large difference between effective wave energy and surface roll energy within the surface roll frequency band for denoising. This method calculates the average energy across multiple channels, frequency divisions, and time windows within the surface roll frequency band. It then calculates the ratio of the energy at a given point to the average energy. If this ratio exceeds a given threshold parameter, the signal at that point is considered to be primarily surface roll and is attenuated. Otherwise, the signal is retained. However, when surface roll is present in every recording channel at a given moment, this method is ineffective and may result in severe denoising or loss of effective signal.

[0052] In view of this, the present application proposes a surface roll suppression method, device, storage medium, and electronic device based on three-dimensional cone filtering and mean weighting. For the processing of massive pre-stack seismic data, when performing surface roll suppression, it is necessary to consider not only the effectiveness of the method but also its operational efficiency. The method disclosed in the present application achieves the complementary advantages of the two methods, overcomes the defects of a single method, and realizes fidelity-preserving and amplitude-preserving denoising; at the same time, it takes advantage of the simple parameter selection and fast operation speed of the two methods, and improves processing efficiency.

[0053] Example 1

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

[0055] Currently, the most commonly used three-dimensional pyramidal filtering method for denoising is 3DFKK filtering in the cross-arrangement domain. This method fully considers the fact that linear interference in seismic data is widely distributed throughout the entire three-dimensional space. It applies a three-dimensional Fourier transform and adjusts the dip parameter for denoising, that is, by adjusting the inter-trace time difference parameter of the surface roll signal. This can completely remove noise. However, because the denoising process directly removes a signal component in the frequency (wavenumber) domain, it can lead to aliasing, which manifests as false events in the denoised seismic record. If the surface roll noise to be removed is high in frequency and velocity, this method will severely damage the valid signal while denoising, and the aliasing effect is obvious. If the effective signal is protected, a large amount of surface roll 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 distant coherent noise is hyperbolic, similar to the characteristics of effective reflections, achieving complete noise suppression in this case will also damage a large amount of effective signals, resulting in aliasing.

[0057] In order to avoid the phenomenon of incomplete denoising or severe effective signal damage 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 large tilt parameter during cross-arranged pyramidal filtering to ensure that the desired surface roll is mostly within the noise domain, achieving a rough separation between surface roll and valid signal. At this point, a small amount of valid signal remains within a single shot of noise. Surface roll is then predicted in the 3DFKK noise domain using a mean-weighted approach. The frequency and velocity of the noise are properly controlled during surface roll fitting, while an appropriate valid signal protection window is selected to minimize the presence of valid signal in the predicted surface roll. Finally, the predicted surface roll model is subtracted from the original data to obtain the denoised data. This method combines the advantages of both methods to achieve better signal-to-noise separation. The subtraction method effectively suppresses surface roll while effectively protecting the valid signal, achieving both fidelity- and amplitude-preserving denoising.

[0059] As an example, Figure 1 A flow chart of a surface roll suppression method based on three-dimensional cone filtering and mean weighting provided in an embodiment of the present application is shown in FIG. 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 processed first data;

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

[0062] Step 130: Perform a preset surface roll prediction process on the noise data to obtain processed second data;

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

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

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

[0066] The first data is subtracted from the original seismic data to obtain noise data.

[0067] In some embodiments, the preset surface roll prediction process in step 130 may specifically include:

[0068] Mean-weighted surface roll prediction processing.

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

[0070] The second data is subtracted from the original seismic data to obtain denoised data.

[0071] The surface roll suppression method based on three-dimensional cone filtering and mean weighting provided in this embodiment leverages the complementary advantages of the two methods, overcoming the drawbacks of a single method and achieving fidelity-preserving and amplitude-preserving denoising. It also leverages the advantages of both methods, namely, simple parameter selection and fast computational speed, and improves processing efficiency. Specifically, the method comprises the following steps: Step 110, performing three-dimensional cone filtering on the original seismic data to obtain processed first data; Step 120, determining noise data based on the original seismic data and the first data; Step 130, performing a preset surface roll prediction process on the noise data to obtain processed second data; and Step 140, determining denoised data based on the original seismic data and the second data. This method 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 weighting to suppress surface roll. By combining the two methods, the advantages of the two methods complement each other, effectively suppressing surface rolls while effectively protecting the effective signal, achieving fidelity-preserving and amplitude-preserving denoising.

[0072] Example 2

[0073] This embodiment further illustrates the surface roll suppression method based on three-dimensional cone filtering and mean weighting disclosed in this application on the basis of the first embodiment.

[0074] Currently, the most commonly used three-dimensional pyramidal filtering method for denoising is 3DFKK filtering in the cross-arrangement domain. This method fully considers the fact that linear interference in seismic data is widely distributed throughout the entire three-dimensional space. It applies a three-dimensional Fourier transform and adjusts the dip parameter for denoising, that is, by adjusting the inter-trace time difference parameter of the surface roll signal. This can completely remove noise. However, because the denoising process directly removes a signal component in the frequency (wavenumber) domain, it can lead to aliasing, which manifests as false events in the denoised seismic record. If the surface roll noise to be removed is high in frequency and velocity, this method will severely damage the valid signal while denoising, and the aliasing effect is obvious. If the effective signal is protected, a large amount of surface roll 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 distant coherent noise is hyperbolic, similar to the characteristics of effective reflections, achieving complete noise suppression in this case will also damage a large amount of effective signals, resulting in aliasing.

[0076] In order to avoid the phenomenon of incomplete denoising or severe effective signal damage 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 large tilt parameter during cross-arranged pyramidal filtering to ensure that the desired surface roll is mostly within the noise domain, achieving a rough separation between surface roll and valid signal. At this point, a small amount of valid signal remains within a single shot of noise. Surface roll is then predicted in the 3DFKK noise domain using a mean-weighted approach. The frequency and velocity of the noise are properly controlled during surface roll fitting, while an appropriate valid signal protection window is selected to minimize the presence of valid signal in the predicted surface roll. Finally, the predicted surface roll model is subtracted from the original data to obtain the denoised data. This method combines the advantages of both methods to achieve better signal-to-noise separation. The subtraction method effectively suppresses surface roll while effectively protecting the valid signal, achieving both fidelity- and amplitude-preserving denoising.

[0078] As an example, Figure 1 A flow chart of a surface roll suppression method based on three-dimensional cone filtering and mean weighting provided in an embodiment of the present application is shown in FIG. 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 processed first data.

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

[0081] The cross-array domain contains the original seismic data of surface waves.

[0082] As an example, the input seismic data containing surface waves in a cross-arranged domain may be used as the original seismic data.

[0083] As an example, three-dimensional cone filtering is performed on the original seismic data input in step 110 to obtain processed first 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] The first data is subtracted from the original seismic data to obtain noise data.

[0087] As an example, the processed first data obtained in step 110 is subtracted from the input data in step 110 to obtain 3DFKK noise data.

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

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

[0090] Mean-weighted surface roll prediction processing.

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

[0092] Step 140: Determine 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] The second data is subtracted from the original seismic data to obtain denoised data.

[0095] As an example, the processed second data obtained in step 130 is subtracted from the original seismic data input in step 110 to obtain denoised data.

[0096] The surface roll suppression method based on three-dimensional cone filtering and mean weighting provided in this embodiment leverages the complementary advantages of the two methods, overcoming the drawbacks of a single method and achieving fidelity-preserving and amplitude-preserving denoising. It also leverages the advantages of both methods, namely, simple parameter selection and fast computational speed, and improves processing efficiency. Specifically, the method comprises the following steps: Step 110, performing three-dimensional cone filtering on the original seismic data to obtain processed first data; Step 120, determining noise data based on the original seismic data and the first data; Step 130, performing a preset surface roll prediction process on the noise data to obtain processed second data; and Step 140, determining denoised data based on the original seismic data and the second data. This method 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 weighting to suppress surface roll. By combining the two methods, the advantages of the two methods complement each other, effectively suppressing surface rolls while effectively protecting the effective signal, achieving 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 has good protection for valid signals, there is no obvious valid information in the noise record, and it achieves good fidelity and amplitude-preserving denoising.

[0099] Currently, the most commonly used three-dimensional pyramidal filtering method for denoising is 3DFKK filtering in the cross-arrangement domain. This method fully considers the fact that linear interference in seismic data is widely distributed throughout the entire three-dimensional space. It applies a three-dimensional Fourier transform and adjusts the dip parameter for denoising, that is, by adjusting the inter-trace time difference parameter of the surface roll signal. This can completely remove noise. However, because the denoising process directly removes a signal component in the frequency (wavenumber) domain, it can lead to aliasing, which manifests as false events in the denoised seismic record. If the surface roll noise to be removed is high in frequency and velocity, this method will severely damage the valid signal while denoising, and the aliasing effect is obvious. If the effective signal is protected, a large amount of surface roll 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 distant coherent noise is hyperbolic, similar to the characteristics of effective reflections, achieving complete noise suppression in this case will also damage a large amount of effective signals, resulting in aliasing.

[0101] In order to avoid the phenomenon of incomplete denoising or severe effective signal damage 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 large tilt parameter during cross-arranged pyramidal filtering to ensure that the desired surface roll is mostly within the noise domain, achieving a rough separation between surface roll and valid signal. At this point, a small amount of valid signal remains within a single shot of noise. Surface roll is then predicted in the 3DFKK noise domain using a mean-weighted approach. The frequency and velocity of the noise are properly controlled during surface roll fitting, while an appropriate valid signal protection window is selected to minimize the presence of valid signal in the predicted surface roll. Finally, the predicted surface roll model is subtracted from the original data to obtain the denoised data. This method combines the advantages of both methods to achieve better signal-to-noise separation. The subtraction method effectively suppresses surface roll while effectively protecting the valid signal, achieving both fidelity- and amplitude-preserving denoising.

[0103] In this embodiment, the surface roll 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 flow chart of a surface roll suppression method based on three-dimensional cone filtering and mean weighting provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method disclosed in this application includes the following steps when performing verification:

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

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

[0107] The cross-array domain contains the original seismic data of surface waves.

[0108] As an example, the input seismic data containing surface waves in a cross-arranged domain may be used as the original seismic data.

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

[0110] like Figure 2 As shown in Figure 3, when a single cross-arrangement domain 3D cone filtering method is used to suppress surface rolls, a large amount of surface roll 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] The first data is subtracted from the original seismic data to obtain noise data.

[0114] As an example, the processed first data obtained in step 110 is subtracted from the input data in step 110 to obtain 3DFKK noise data.

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

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

[0117] Mean-weighted surface roll prediction processing.

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

[0119] like Figure 3As shown in the figure, when the single mean weighted method is used to suppress the surface roll, due to the hyperbolic time-distance curve of the surface roll in the distant data, the effective signal is severely damaged when complete denoising is achieved, and the noise contains more effective reflection signals.

[0120] Step 140: Determine 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] The second data is subtracted from the original seismic data to obtain denoised data.

[0123] As an example, the processed second data obtained in step 130 is subtracted from the original seismic data input in step 110 to obtain denoised data.

[0124] like Figure 4 As shown in the figure, the proposed method effectively suppresses surface rolls while protecting the effective signal well. There is no obvious effective information in the noise record, and the method achieves good fidelity and amplitude preservation denoising.

[0125] Further, in order to facilitate understanding of the technical solution of this application, you can also refer to Figure 5 .

[0126] Example 4

[0127] This embodiment provides a surface roll 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 the structure of a surface roll suppression device based on three-dimensional cone filtering and mean weighting provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the device 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 processed first data;

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

[0130] A second processing module 603 is configured to perform a preset surface roll prediction process on the noise data to obtain processed second data;

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

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

[0133] In some embodiments, the denoising module 604 is configured 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-array domain contains the original seismic data of surface waves.

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

[0137] Mean-weighted surface roll prediction processing.

[0138] It is understood by those skilled in the art that Figure 6 The structure shown in the figure does not constitute a limitation on the device of the embodiment of the present application, and may include more or fewer modules / units than shown in the figure, or combine certain modules / units, or arrange modules / units differently.

[0139] Those skilled in the art will appreciate that the modules or steps of the present application described above can be implemented using a general-purpose computing device, and can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than herein, or can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation.

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

[0141] The apparatus provided in this embodiment includes: a first processing module 601 for performing three-dimensional cone filtering on raw seismic data to obtain processed first data; a noise data determination module 602 for determining noise data based on the raw seismic data and the first data; a second processing module 603 for performing preset surface roll prediction processing on the noise data to obtain processed second data; and a denoising module 604 for determining denoised data based on the raw 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 weighting to suppress surface roll. By combining the two methods, the advantages of the two methods are complemented, effectively suppressing surface roll while protecting the effective signal, thereby achieving both fidelity-preserving 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 that, when executed by a processor, can implement some or all of the steps of the method in the aforementioned method embodiment:

[0144] Performing three-dimensional cone filtering on the original seismic data to obtain processed first data;

[0145] determining noise data based on the original seismic data and the first data;

[0146] performing a preset surface roll prediction process on the noise data to obtain processed second data;

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

[0148] Computer-readable storage media may also include computer programs, data files, data structures, etc., individually or in combination. The computer-readable storage medium or computer program may be specifically designed and understood by those skilled in the art of computer software, or the computer-readable storage medium may be well-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 CDROMs and DVDs; magneto-optical media, such as optical disks; 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, app stores, etc. Examples of computer programs include machine code (e.g., code generated by a compiler) and files containing higher-level code that can be executed by a computer using an interpreter. The described hardware devices can be configured to function as one or more software modules to perform the operations and methods described above, and vice versa. In addition, computer-readable storage media can be distributed across networked computer systems, allowing program code or computer programs to be stored and executed in a decentralized manner.

[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, implements all or part of the steps of the method in the aforementioned method embodiment, which will not be repeated in this embodiment.

[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 embodied on a computer-readable medium, the computer program including program code for performing any method in the embodiments of the present disclosure. In such an embodiment, 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 the present application, such as Figure 7 As shown, the electronic device 700 may include: one or more processors 701 , a memory 702 , a multimedia component 703 , an input / output (I / O) interface 704 , and a communication component 705 .

[0154] The one or more processors 701 are used to execute all or part of the steps in the aforementioned method embodiment. The memory 702 is used to store various types of data, such as instructions of any application or method in the electronic device, and application-related data.

[0155] The memory 702 can be implemented by any type of volatile or non-volatile memory 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 memory, flash memory, magnetic disk or optical disk.

[0156] The 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 may be configured to execute all or part of the steps in the aforementioned method embodiment:

[0157] Performing three-dimensional cone filtering on the original seismic data to obtain processed first data;

[0158] determining noise data based on the original seismic data and the first data;

[0159] performing a preset surface roll prediction process on the noise data to obtain processed second data;

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

[0161] The multimedia component 703 may include a screen and an audio component. The screen may be a touch screen, and the audio component is used to output and / or input 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 a memory or transmitted via a communication component. The audio component also includes at least one speaker for outputting audio signals.

[0162] The I / O interface 704 provides an interface between one or more processors 701 and other interface modules, such as a keyboard, a mouse, buttons, etc. These buttons may 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 through a network port, a serial port, etc.; wireless communication includes: Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, 5G, or one or a combination thereof.

[0164] In summary, the present application provides a surface roll suppression method, device, computer-readable storage medium, and electronic device based on three-dimensional cone filtering and mean weighting. The method leverages the advantages of the two methods, overcoming the drawbacks of a single method and achieving fidelity-preserving and amplitude-preserving denoising. It also leverages the advantages of both methods, namely, simple parameter selection and fast computational speed, and improves processing efficiency. Specifically, the method comprises the following steps: Step 110, performing three-dimensional cone filtering on the original seismic data to obtain processed first data; Step 120, determining noise data based on the original seismic data and the first data; Step 130, performing a preset surface roll prediction process on the noise data to obtain processed second data; and Step 140, determining denoised data based on the original seismic data and the second data. This method 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 weighting to suppress surface rolls. By combining the two methods, the advantages of the two methods can be complemented, effectively suppressing surface rolls while effectively protecting the effective signal, achieving fidelity-preserving and amplitude-preserving denoising.

[0165] It should also be understood that the methods or devices disclosed in the embodiments provided in this application may also be implemented in other ways. The method or device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the methods and devices according to the various embodiments of this application. In this regard, each box in the flowchart or block diagram may represent a module, a computer program segment, or a portion of a computer program, which contains one or more computer programs for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes may occur in an order different from that marked in the drawings, and may actually be executed substantially in parallel, or they may sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and combinations of boxes in the block diagram and / or flowchart, may be implemented using a dedicated hardware-based system that performs the specified functions or actions, or may be implemented using a combination of dedicated hardware and computer programs.

[0166] In this application, the terms "comprises," "comprising," 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 that are inherent to such process, method, article, or apparatus. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, device or equipment including the elements; if there is a description of "first", "second", etc., it is only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features; in the description of this application, unless otherwise specified, the terms "multiple" and "many" mean at least two; if there is a description of a server, it should be noted that the server can be an independent physical server or terminal, or a server cluster composed of multiple physical servers, or a cloud server that can provide basic cloud computing services such as cloud servers, cloud databases, cloud storage and CDN; if there is a description of a smart terminal or mobile device in this application, it should be noted that the smart terminal or mobile device can be a mobile phone, tablet computer, smart watch, netbook, wearable electronic device, personal digital assistant (PDA), augmented reality technology device (AR), virtual reality device (VR), smart TV, smart speaker, personal computer (PC), etc. Computer, referred to as PC), etc., but not limited to this. This application does not specifically limit the specific form of the smart terminal or mobile device.

[0167] Finally, it should be noted that, in the description of this specification, reference to the terms "one embodiment," "some embodiments," "example," "an example," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples.

[0168] Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and the contents described are only embodiments adopted to facilitate understanding of the present application and are not intended to limit the present application. Any person skilled in the art of the present application may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present application, but the scope of protection of the present application shall still be based on the scope defined by the appended claims.

Claims

1. A surface roll suppression method based on three-dimensional cone filtering and mean weighting, characterized in that: The method comprises: Performing three-dimensional cone filtering on the original seismic data to obtain processed first data; determining noise data based on the original seismic data and the first data; Performing a preset surface roll prediction process on the noise data to obtain processed second data; Denoised data is determined based on the original seismic data and the second data.

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

3. The surface roll suppression method based on three-dimensional cone filtering and mean weighting according to claim 1 is characterized in that: The determining of noise data according to the original seismic data and the first data comprises: The first data is subtracted from the original seismic data to obtain noise data.

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

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

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

7. The surface roll suppression device based on three-dimensional cone filtering and mean weighting according to claim 6 is 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 roll suppression device based on three-dimensional cone filtering and mean weighting according to claim 6 is characterized in that: The denoising module is used to subtract the second data from the original seismic data to obtain 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 roll 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: The invention comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the surface roll suppression method based on three-dimensional cone filtering and mean weighting as described in any one of claims 1 to 5 is implemented.

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