A seismic noise removal method, device, storage medium and electronic equipment
By employing least-squares adaptive filtering subtraction technology, the problem of surface wave and multiple wave noise removal in marine and onshore seismic exploration has been solved, improving the signal-to-noise ratio and imaging quality of seismic data and optimizing geological interpretation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2022-09-30
- Publication Date
- 2026-07-14
AI Technical Summary
Existing technologies are insufficient to effectively remove surface wave and multiple noise in marine and onshore seismic exploration, leading to inaccurate seismic data processing and migration imaging results, which affects geological interpretation. Furthermore, the multiple models predicted by wave equation-based methods differ from the actual multiples, and adaptive filtering subtraction techniques do not fully utilize the weighted minimum energy criterion.
The least squares adaptive filtering subtraction technique is adopted. The original seismic data and noise data are acquired, converted to the frequency domain and cross-correlation is performed. The filter value is determined by the weighted minimum energy criterion to remove noise.
It achieves flexible and adaptive noise removal, improves the signal-to-noise ratio, optimizes seismic profiles, and achieves better adaptive subtraction results, making it suitable for noise removal under complex conditions.
Smart Images
Figure CN117849876B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of digital signal processing technology for seismic exploration data, and in particular to a method, apparatus, storage medium, and electronic device for seismic noise removal. Background Technology
[0002] Due to the presence of a stable free surface (water surface) in marine seismic exploration, the acquired data often contains abundant low- or high-order multiples. The multiple problem is one of the most prominent issues in marine seismic exploration. In addition, in onshore seismic exploration, surface-related multiples are generally not generated due to the instability of the surface structure. However, when there are strong reflective interfaces such as igneous rocks and coal seams underground, interlayer multiples will be generated, which seriously affects the authenticity and reliability of the imaging.
[0003] Surface waves and multiples are regular interference waves with strong energy and dispersion characteristics in seismic records. When processing seismic data, the suppression effect of surface waves and multiples directly affects the subsequent seismic data processing and migration imaging results, and ultimately affects the geological interpretation of seismic data.
[0004] In existing technologies, when processing seismic data, it is often necessary to fit surface waves or multiples, and then subtract the fitted surface waves or multiples from the original data.
[0005] Currently, the mainstream methods for suppressing multiples in the industry are wave equation-based methods, such as the inverse scattering series method and the feedback method (SRME / IME). The processing methods include: first predicting the multiples, and then subtracting the multiples from the original data.
[0006] Wave equation-based methods predict multiple wave models that differ from the original recorded multiple waves in various aspects and cannot be directly subtracted. Taking SRME as an example, even with theoretical model data, the predicted multiple wave model theoretically differs from the actual multiple wave by a wavelet, and this wavelet is time-varying. In actual data acquisition, due to interference from factors such as imperfect excitation and reception conditions, noise, and irregular spatial sampling, this difference becomes even greater, mainly manifesting in aspects such as time difference, amplitude, phase, and frequency.
[0007] The adaptive filtering subtraction technique is a key step that determines the final effect of wave equation-based multiple suppression techniques. Existing technologies rarely use the core principle of least squares adaptive filtering subtraction and the weighted minimum energy criterion to suppress multiples. Summary of the Invention
[0008] To address the aforementioned problems, this application proposes a method, apparatus, storage medium, and electronic device for seismic noise removal. This application proposes a least-squares adaptive filtering subtraction technique, an important component of surface wave and multiple wave suppression techniques. Utilizing the core principle of least-squares adaptive filtering subtraction, it employs a weighted minimum energy criterion, i.e., a cost function, to ultimately obtain the filter value. By employing the technical solution of this application, noise can be effectively and adaptively subtracted, exhibiting high flexibility, applicability to noise subtraction under complex conditions, and the ability to improve the signal-to-noise ratio, optimize seismic profiles, and achieve better adaptive subtraction results.
[0009] A first aspect of this application provides a method for earthquake noise removal, the method comprising:
[0010] Acquire raw seismic data and noise data, wherein the noise data includes noise data predicted from the raw seismic data;
[0011] The original seismic data and the noise data are respectively converted to the frequency domain to obtain the corresponding frequency domain seismic data and frequency domain noise data;
[0012] Cross-correlation processing is performed on the seismic data in the frequency domain and the noise data in the frequency domain to determine the cross-correlation result;
[0013] Perform an inner product operation on the cross-correlation results to determine the filter value;
[0014] Based on the original seismic data, the noise data, and the filter value, noise in the original seismic data is removed using a preset processing model.
[0015] In some embodiments, the original seismic data and the noise data are converted to the frequency domain by Fourier transform, respectively.
[0016] In some embodiments, the inner product operation is performed on the cross-correlation results using the following formula:
[0017]
[0018] Where m and n are both the number of channels, and P() is the cross-correlation result.
[0019] In some embodiments, the preset processing model includes:
[0020] R(x) = P(x)[s(x) - N(x)]
[0021] Where R() is the result of the subtraction, s() is the original seismic data, N() is the noise data, and P() is the filter value.
[0022] In some embodiments, performing the inner product operation on the cross-correlation result includes:
[0023] The inner product operation is performed on the cross-correlation results under the least squares constraint.
[0024] In some embodiments, determining the filter value includes:
[0025] The filter value is determined by iterative solution using the gradient descent method.
[0026] In some embodiments, the step of performing cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain to determine the cross-correlation result includes:
[0027] Cross-correlation processing is performed on the seismic data in the frequency domain and the noise data in the frequency domain, and the cross-correlation result is determined when the cross-correlation coefficient is maximized.
[0028] A second aspect of this application provides an apparatus comprising:
[0029] An acquisition module is used to acquire raw seismic data and noise data, wherein the noise data includes noise data predicted from the raw seismic data;
[0030] The conversion module is used to convert the original seismic data and the noise data to the frequency domain respectively, so as to obtain the corresponding frequency domain seismic data and frequency domain noise data.
[0031] The cross-correlation module is used to perform cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain, and determine the cross-correlation result;
[0032] The determination module is used to perform an inner product operation on the cross-correlation results to determine the filter value;
[0033] The noise removal module is used to remove noise from the original seismic data using a preset processing model based on the original seismic data, the noise data, and the filter value.
[0034] In some embodiments, the original seismic data and the noise data are converted to the frequency domain by Fourier transform, respectively.
[0035] In some embodiments, the inner product operation is performed on the cross-correlation results using the following formula:
[0036]
[0037] Where m and n are both the number of channels, and P() is the cross-correlation result.
[0038] In some embodiments, the preset processing model includes:
[0039] R(x) = P(x)[s(x) - N(x)]
[0040] Where R() is the result of the subtraction, s() is the original seismic data, N() is the noise data, and P() is the filter value.
[0041] In some embodiments, the determining module performs an inner product operation on the cross-correlation results under least squares constraints.
[0042] In some embodiments, the determining module determines the filter value by iteratively solving the gradient descent method.
[0043] In some embodiments, the cross-correlation module performs cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain, and determines the cross-correlation result when the cross-correlation coefficient is maximized.
[0044] 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 method described above.
[0045] 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, and the computer program, when executed by the one or more processors, implements the method described above.
[0046] Compared with the prior art, the technical solution of this application has the following advantages or beneficial effects:
[0047] It can effectively and adaptively subtract noise, is highly flexible, applicable to noise subtraction under complex conditions, and can improve the signal-to-noise ratio of the signal, optimize seismic profiles, and achieve good adaptive subtraction results. Attached Figure Description
[0048] 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.
[0049] Figure 1 A flowchart of an earthquake noise removal method provided in this application embodiment;
[0050] Figure 2A A schematic diagram of raw gun gathering data provided in an embodiment of this application;
[0051] Figure 2B A schematic diagram of extracted noise data provided in an embodiment of this application;
[0052] Figure 2C This application provides a schematic diagram of effective signal data after subtraction in an embodiment.
[0053] Figure 3A This is a schematic diagram of another original gun collection data provided in an embodiment of this application;
[0054] Figure 3B A schematic diagram of another type of extracted noise data provided in an embodiment of this application;
[0055] Figure 3C This is a schematic diagram of another effective signal data after subtraction provided in an embodiment of this application;
[0056] Figure 4 A schematic diagram of the structure of an apparatus provided in an embodiment of this application;
[0057] Figure 5 This is a connection block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation
[0058] 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.
[0059] As the background technology shows, marine seismic exploration often produces abundant low- or high-order multiples in the acquired data due to the presence of a stable free surface (water surface). The multiple problem is one of the most prominent issues in marine seismic exploration. In addition, in onshore seismic exploration, surface-related multiples are generally not generated due to the instability of the surface structure. However, when there are strong reflective interfaces such as igneous rocks and coal seams underground, interlayer multiples will be generated, which seriously affects the authenticity and reliability of the imaging.
[0060] Surface waves and multiples are regular interference waves with strong energy and dispersion characteristics in seismic records. When processing seismic data, the suppression effect of surface waves and multiples directly affects the subsequent seismic data processing and migration imaging results, and ultimately affects the geological interpretation of seismic data.
[0061] In existing technologies, when processing seismic data, it is often necessary to fit surface waves or multiples, and then subtract the fitted surface waves or multiples from the original data.
[0062] Currently, the mainstream methods for suppressing multiples in the industry are wave equation-based methods, such as the inverse scattering series method and the feedback method (SRME / IME). The processing methods include: first predicting the multiples, and then subtracting the multiples from the original data.
[0063] Wave equation-based methods predict multiple wave models that differ from the original recorded multiple waves in various aspects and cannot be directly subtracted. Taking SRME as an example, even with theoretical model data, the predicted multiple wave model theoretically differs from the actual multiple wave by a wavelet, and this wavelet is time-varying. In actual data acquisition, due to interference from factors such as imperfect excitation and reception conditions, noise, and irregular spatial sampling, this difference becomes even greater, mainly manifesting in aspects such as time difference, amplitude, phase, and frequency.
[0064] The adaptive filtering subtraction technique is a key step that determines the final effect of wave equation-based multiple suppression techniques. Existing technologies rarely use the core principle of least squares adaptive filtering subtraction and the weighted minimum energy criterion to suppress multiples.
[0065] In view of this, this application discloses a method, apparatus, computer-readable storage medium, and electronic device for seismic noise removal. This application discloses a least-squares adaptive filtering subtraction technique, an important component of surface wave and multiple wave suppression techniques. Utilizing the core principle of least-squares adaptive filtering subtraction, it employs a weighted minimum energy criterion, i.e., a cost function, to ultimately obtain the filter value. By employing the technical solution of this application, noise can be effectively and adaptively subtracted, exhibiting high flexibility, applicability to noise subtraction under complex conditions, and the ability to improve the signal-to-noise ratio, optimize seismic profiles, and achieve better adaptive subtraction results.
[0066] Example 1
[0067] To make the objectives, technical solutions, and beneficial effects of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings and specific implementation methods.
[0068] The following sections will explain some of the technical terms used in the embodiments of this application and in the prior art, so that those skilled in the art can understand the technical solutions of this application.
[0069] Least squares technique: Least squares is a mathematical optimization technique that finds the best function fit for a set of data by minimizing the sum of squared errors. The least squares method uses the simplest method to find some absolutely unknown truth values while minimizing the sum of squared errors. Least squares is commonly used for curve fitting, and many other optimization problems can also be expressed in least squares form using minimum energy or maximum entropy. The core principle of the least squares adaptive filtering subtraction technique can be shown in the following equation:
[0070] P(t,x)=D(t,x)-f(t,x)*M(t,x)
[0071] The above equation is the adaptive subtraction module, where t is time, x is the number of channels, P() is the output result, D() is the input data, f() is the filter value, and M() is the desired output result.
[0072] It should also be noted that the main technical ideas and implementation of the technical solution in this application include: using the core principle of least squares adaptive filtering subtraction, adopting the weighted minimum energy criterion, i.e., the cost function, to finally obtain the value of the filter.
[0073] This embodiment provides a method for earthquake noise removal. Figure 1 A flowchart of an earthquake noise removal method provided in this application embodiment is shown below. Figure 1 As shown, the method in this embodiment includes:
[0074] Step 110: Obtain raw seismic data and noise data, wherein the noise data includes noise data predicted from the raw seismic data.
[0075] In some embodiments, the noise data is obtained from the original seismic data using a preset prediction method.
[0076] Those skilled in the art will understand that the method for predicting noise data from the raw seismic data can be selected according to the user's actual needs, and the preset prediction method can also be selected according to the user's actual needs; no specific limitations are made here.
[0077] Step 120: Convert the original seismic data and the noise data to the frequency domain respectively to obtain the corresponding frequency domain seismic data and frequency domain noise data.
[0078] In some embodiments, the original seismic data and the noise data are converted to the frequency domain by Fourier transform, respectively.
[0079] Optionally, Fourier transforms can be performed on the original seismic data and the noise data predicted from the original seismic data to transform the data to the frequency domain.
[0080] Step 130: Perform cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain, and determine the cross-correlation result.
[0081] In some embodiments, the step of performing cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain to determine the cross-correlation result includes:
[0082] Cross-correlation processing is performed on the seismic data in the frequency domain and the noise data in the frequency domain, and the cross-correlation result is determined when the cross-correlation coefficient is maximized.
[0083] Optionally, the original record and the predicted noise signal are cross-correlated. The result with the highest cross-correlation coefficient is considered to best match the geological reality and is used as the final high-resolution processing result of the characteristic dominant spectrum extension. This allows for amplitude matching between them, providing a basis for adaptive subtraction.
[0084] Step 140: Perform an inner product operation on the cross-correlation results to determine the filter value.
[0085] In some embodiments, the inner product operation is performed on the cross-correlation results using the following formula:
[0086]
[0087] Where m and n are both channel numbers, i and j are both variables, and P() is the cross-correlation result.
[0088] In some embodiments, performing the inner product operation on the cross-correlation result includes:
[0089] The inner product operation is performed on the cross-correlation results under the least squares constraint.
[0090] In some embodiments, determining the filter value includes:
[0091] The filter value is determined by iterative solution using the gradient descent method.
[0092] Optionally, under the least squares constraint, by taking the inner product of the cross-correlation results using equation (1), the least squares expression in the minimum energy form is obtained, where equation (1) can be expressed as follows:
[0093]
[0094] Where m and n are both channel numbers, i and j are both variables, and P() is the cross-correlation result.
[0095] The least-squares adaptive filter is solved and implemented quickly using the robust gradient descent method. The preprocessing of the filter operator is studied, specifically as follows:
[0096] P(x) = arg min p(x)
[0097] To minimize p(t), where P(x) is the optimal filter value obtained through iterative solution, this value is used in the subtraction process.
[0098] Step 150: Based on the original seismic data, the noise data, and the filter value, remove the noise from the original seismic data using a preset processing model.
[0099] In some embodiments, the preset processing model includes:
[0100] R(x) = P(x)[s(x) - N(x)]
[0101] Where R() is the result of the subtraction, s() is the original seismic data, N() is the noise data, and P() is the filter value.
[0102] Optionally, the adaptively subtracted seismic data can be output to obtain data after eliminating surface wave dispersion noise.
[0103] This application discloses a least-squares adaptive filtering subtraction technique, an important component of surface wave and multiple wave suppression techniques. Utilizing the core principle of least-squares adaptive filtering subtraction, it employs a weighted minimum energy criterion, i.e., a cost function, to ultimately obtain the filter value. Specifically: It acquires original seismic data and noise data, wherein the noise data includes noise data predicted from the original seismic data; it converts the original seismic data and the noise data to the frequency domain, obtaining corresponding frequency-domain seismic data and frequency-domain noise data; it performs cross-correlation processing on the frequency-domain seismic data and the frequency-domain noise data to determine the cross-correlation result; it performs an inner product operation on the cross-correlation result to determine the filter value; and based on the original seismic data, the noise data, and the filter value, it removes noise from the original seismic data using a preset processing model. By utilizing the technical solution of this application, noise can be effectively and adaptively subtracted, exhibiting high flexibility, applicability to noise subtraction under complex conditions, and the ability to improve the signal-to-noise ratio and optimize seismic profiles. Compared to existing technologies, it achieves better adaptive subtraction results.
[0104] Example 2
[0105] To make the objectives, technical solutions, and beneficial effects of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings and specific implementation methods.
[0106] The following sections will explain some of the technical terms used in the embodiments of this application and in the prior art, so that those skilled in the art can understand the technical solutions of this application.
[0107] Least squares technique: Least squares is a mathematical optimization technique that finds the best function fit for a set of data by minimizing the sum of squared errors. The least squares method uses the simplest method to find some absolutely unknown truth values while minimizing the sum of squared errors. Least squares is commonly used for curve fitting, and many other optimization problems can also be expressed in least squares form using minimum energy or maximum entropy. The core principle of the least squares adaptive filtering subtraction technique can be shown in the following equation:
[0108] P(t,x)=D(t,x)-f(t,x)*M(t,x)
[0109] The above equation is the adaptive subtraction module, where t is time, x is the number of channels, P() is the output result, D() is the input data, f() is the filter value, and M() is the desired output result.
[0110] It should also be noted that the main technical ideas and implementation of the technical solution in this application include: using the core principle of least squares adaptive filtering subtraction, adopting the weighted minimum energy criterion, i.e., the cost function, to finally obtain the value of the filter.
[0111] This embodiment provides a specific example, and through actual data testing, it is verified that the technical solution of this application has a better noise removal effect than the prior art.
[0112] Step 1: Obtain raw seismic data and noise data, wherein the noise data includes noise data predicted from the raw seismic data.
[0113] In some embodiments, the noise data is obtained from the original seismic data using a preset prediction method.
[0114] Those skilled in the art will understand that the method for predicting noise data from the raw seismic data can be selected according to the user's actual needs, and the preset prediction method can also be selected according to the user's actual needs; no specific limitations are made here.
[0115] Step 2: Convert the original seismic data and the noise data to the frequency domain respectively to obtain the corresponding frequency domain seismic data and frequency domain noise data.
[0116] In this embodiment, an adaptive subtraction effect analysis is performed based on actual seismic data of a certain area to verify the effectiveness of the method disclosed in this application.
[0117] In some embodiments, the original seismic data and the noise data are converted to the frequency domain by Fourier transform, respectively.
[0118] Optionally, Fourier transforms can be performed on the original seismic data and the noise data predicted from the original seismic data to transform the data to the frequency domain.
[0119] Step 3: Perform cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain, and determine the cross-correlation result.
[0120] In some embodiments, the step of performing cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain to determine the cross-correlation result includes:
[0121] Cross-correlation processing is performed on the seismic data in the frequency domain and the noise data in the frequency domain, and the cross-correlation result is determined when the cross-correlation coefficient is maximized.
[0122] Optionally, the original record and the predicted noise signal are cross-correlated. The result with the highest cross-correlation coefficient is considered to best match the geological reality and is used as the final high-resolution processing result of the characteristic dominant spectrum extension. This allows for amplitude matching between them, providing a basis for adaptive subtraction.
[0123] Step 4: Perform an inner product operation on the cross-correlation results to determine the filter value.
[0124] In some embodiments, the inner product operation is performed on the cross-correlation results using the following formula:
[0125]
[0126] Where m and n are both channel numbers, i and j are both variables, and P() is the cross-correlation result.
[0127] In some embodiments, performing the inner product operation on the cross-correlation result includes:
[0128] The inner product operation is performed on the cross-correlation results under the least squares constraint.
[0129] In some embodiments, determining the filter value includes:
[0130] The filter value is determined by iterative solution using the gradient descent method.
[0131] Optionally, under the least squares constraint, by taking the inner product of the cross-correlation results using equation (1), the least squares expression in the minimum energy form is obtained, where equation (1) can be expressed as follows:
[0132]
[0133] Where m and n are both channel numbers, i and j are both variables, and P() is the cross-correlation result.
[0134] The least-squares adaptive filter is solved and implemented quickly using the robust gradient descent method. The preprocessing of the filter operator is studied, specifically as follows:
[0135] P(x) = arg min p(x)
[0136] To minimize p(t), where P(x) is the optimal filter value obtained through iterative solution, this value is used in the subtraction process.
[0137] Step 5: Based on the original seismic data, the noise data, and the filter value, remove the noise from the original seismic data using a preset processing model.
[0138] In some embodiments, the preset processing model includes:
[0139] R(x) = P(x)[s(x) - N(x)]
[0140] Where R() is the result of the subtraction, s() is the original seismic data, N() is the noise data, and P() is the filter value.
[0141] Optionally, the adaptively subtracted seismic data can be output to obtain data after eliminating surface wave dispersion noise.
[0142] Tests based on actual earthquake data can be used as a reference. Figures 2A to 2C ,in, Figure 2A This is a schematic diagram of raw shot collection data provided in an embodiment of this application. Figure 2B This is a schematic diagram of extracted noise data provided in an embodiment of this application. Figure 2C This is a schematic diagram of the effective signal data after subtraction provided in an embodiment of this application; it can be seen that... Figure 2A and Figure 2C The cross-sections have a strong similarity.
[0143] Furthermore, actual seismic data from another piedmont work area were used for testing and adaptive subtraction effect analysis. The test results can be referenced. Figures 3A-3C ,in, Figure 3A This is a schematic diagram of another original shot collection data provided in an embodiment of this application. Figure 3B This is another schematic diagram of extracted noise data provided in an embodiment of this application, showing good amplitude preservation. Figure 3CThe diagram illustrates another effective signal data after subtraction provided in this embodiment, showing almost no residual noise. This demonstrates that the adaptive subtraction method disclosed in this application has excellent performance and a high signal-to-noise ratio, achieving better adaptive subtraction results compared to existing technologies.
[0144] This application discloses a least-squares adaptive filtering subtraction technique, an important component of surface wave and multiple wave suppression techniques. Utilizing the core principle of least-squares adaptive filtering subtraction, it employs a weighted minimum energy criterion, i.e., a cost function, to ultimately obtain the filter value. Specifically: It acquires original seismic data and noise data, wherein the noise data includes noise data predicted from the original seismic data; it converts the original seismic data and the noise data to the frequency domain, obtaining corresponding frequency-domain seismic data and frequency-domain noise data; it performs cross-correlation processing on the frequency-domain seismic data and the frequency-domain noise data to determine the cross-correlation result; it performs an inner product operation on the cross-correlation result to determine the filter value; and based on the original seismic data, the noise data, and the filter value, it removes noise from the original seismic data using a preset processing model. By utilizing the technical solution of this application, noise can be effectively and adaptively subtracted, exhibiting high flexibility, applicability to noise subtraction under complex conditions, and the ability to improve the signal-to-noise ratio and optimize seismic profiles. Compared to existing technologies, it achieves better adaptive subtraction results.
[0145] Example 3
[0146] To make the objectives, technical solutions, and beneficial effects of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings and specific implementation methods.
[0147] The following sections will explain some of the technical terms used in the embodiments of this application and in the prior art, so that those skilled in the art can understand the technical solutions of this application.
[0148] Least squares technique: Least squares is a mathematical optimization technique that finds the best function fit for a set of data by minimizing the sum of squared errors. The least squares method uses the simplest method to find some absolutely unknown truth values while minimizing the sum of squared errors. Least squares is commonly used for curve fitting, and many other optimization problems can also be expressed in least squares form using minimum energy or maximum entropy. The core principle of the least squares adaptive filtering subtraction technique can be shown in the following equation:
[0149] P(t,x)=D(t,x)-f(t,x)*M(t,x)
[0150] The above equation is the adaptive subtraction module, where t is time, x is the number of channels, P() is the output result, D() is the input data, f() is the filter value, and M() is the desired output result.
[0151] It should also be noted that the main technical ideas and implementation of the technical solution in this application include: using the core principle of least squares adaptive filtering subtraction, adopting the weighted minimum energy criterion, i.e., the cost function, to finally obtain the value of the filter.
[0152] This embodiment provides an apparatus that can be used to execute the method embodiment of this application. For details not disclosed in this apparatus embodiment, please refer to the method embodiment of this application. Figure 4 This is a schematic diagram of the structure of a device provided in an embodiment of this application, such as... Figure 4 As shown, the device 400 provided in this embodiment includes:
[0153] The acquisition module 401 is used to acquire raw seismic data and noise data, wherein the noise data includes noise data predicted from the raw seismic data;
[0154] The conversion module 402 is used to convert the original seismic data and the noise data to the frequency domain respectively, so as to obtain the corresponding frequency domain seismic data and frequency domain noise data.
[0155] The cross-correlation module 403 is used to perform cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain, and determine the cross-correlation result;
[0156] The determination module 404 is used to perform an inner product operation on the cross-correlation result to determine the filter value;
[0157] The noise removal module 405 is used to remove noise from the original seismic data by means of a preset processing model based on the original seismic data, the noise data, and the filter value.
[0158] In some embodiments, the noise data is obtained from the original seismic data using a preset prediction method.
[0159] Those skilled in the art will understand that the method for predicting noise data from the raw seismic data can be selected according to the user's actual needs, and the preset prediction method can also be selected according to the user's actual needs; no specific limitations are made here.
[0160] In some embodiments, the original seismic data and the noise data are converted to the frequency domain by Fourier transform, respectively.
[0161] Optionally, Fourier transforms can be performed on the original seismic data and the noise data predicted from the original seismic data to transform the data to the frequency domain.
[0162] In some embodiments, the cross-correlation module 403 performs cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain, and determines the cross-correlation result when the cross-correlation coefficient is maximized.
[0163] Optionally, the original record and the predicted noise signal are cross-correlated. The result with the highest cross-correlation coefficient is considered to best match the geological reality and is used as the final high-resolution processing result of the characteristic dominant spectrum extension. This allows for amplitude matching between them, providing a basis for adaptive subtraction.
[0164] In some embodiments, the inner product operation is performed on the cross-correlation results using the following formula:
[0165]
[0166] Where m and n are both channel numbers, i and j are both variables, and P() is the cross-correlation result.
[0167] In some embodiments, the determining module 404 performs an inner product operation on the cross-correlation results under least squares constraints.
[0168] In some embodiments, the determining module 404 determines the filter value by iteratively solving the gradient descent method.
[0169] Optionally, under the least squares constraint, by taking the inner product of the cross-correlation results using equation (1), the least squares expression in the minimum energy form is obtained, where equation (1) can be expressed as follows:
[0170]
[0171] Where m and n are both channel numbers, i and j are both variables, and P() is the cross-correlation result.
[0172] The least-squares adaptive filter is solved and implemented quickly using the robust gradient descent method. The preprocessing of the filter operator is studied, specifically as follows:
[0173] P(x) = arg min p(x)
[0174] To minimize p(t), where P(x) is the optimal filter value obtained through iterative solution, this value is used in the subtraction process.
[0175] In some embodiments, the preset processing model includes:
[0176] R(x) = P(x)[s(x) - N(x)]
[0177] Where R() is the result of the subtraction, s() is the original seismic data, N() is the noise data, and P() is the filter value.
[0178] Optionally, the adaptively subtracted seismic data can be output to obtain data after eliminating surface wave dispersion noise.
[0179] Those skilled in the art will understand that Figure 4 The structures shown do not constitute a limitation on the apparatus of the embodiments of this application. They may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0180] It should be noted that the above modules / units can be either functional modules or program modules, and can be implemented through software or hardware. For modules / units implemented in hardware, the above modules / units can reside in the same processor; or the above modules / units can be located in different processors in any combination.
[0181] The apparatus disclosed in this embodiment includes: an acquisition module 401 for acquiring raw seismic data and noise data, wherein the noise data includes noise data predicted from the raw seismic data; a conversion module 402 for converting the raw seismic data and the noise data to the frequency domain respectively, to obtain corresponding frequency domain seismic data and frequency domain noise data; a cross-correlation module 403 for performing cross-correlation processing on the frequency domain seismic data and the frequency domain noise data to determine the cross-correlation result; a determination module 404 for performing an inner product operation on the cross-correlation result to determine a filter value; and a removal module 405 for removing noise from the raw seismic data according to the raw seismic data, the noise data, and the filter value using a preset processing model. By utilizing the technical solution of this application, noise can be effectively and adaptively subtracted, exhibiting high flexibility, applicability to noise subtraction under complex conditions, and the ability to improve the signal-to-noise ratio and optimize seismic profiles. Compared with existing technologies, it achieves better adaptive subtraction effects.
[0182] Example 4
[0183] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the methods described in the foregoing method embodiments:
[0184] The first step is to acquire raw seismic data and noise data, wherein the noise data includes noise data predicted from the raw seismic data.
[0185] In some embodiments, the noise data is obtained from the original seismic data using a preset prediction method.
[0186] Those skilled in the art will understand that the method for predicting noise data from the raw seismic data can be selected according to the user's actual needs, and the preset prediction method can also be selected according to the user's actual needs; no specific limitations are made here.
[0187] The second step is to convert the original seismic data and the noise data to the frequency domain respectively, so as to obtain the corresponding frequency domain seismic data and frequency domain noise data.
[0188] In some embodiments, the original seismic data and the noise data are converted to the frequency domain by Fourier transform, respectively.
[0189] Optionally, Fourier transforms can be performed on the original seismic data and the noise data predicted from the original seismic data to transform the data to the frequency domain.
[0190] The third step is to perform cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain to determine the cross-correlation results.
[0191] In some embodiments, the step of performing cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain to determine the cross-correlation result includes:
[0192] Cross-correlation processing is performed on the seismic data in the frequency domain and the noise data in the frequency domain, and the cross-correlation result is determined when the cross-correlation coefficient is maximized.
[0193] Optionally, the original record and the predicted noise signal are cross-correlated. The result with the highest cross-correlation coefficient is considered to best match the geological reality and is used as the final high-resolution processing result of the characteristic dominant spectrum extension. This allows for amplitude matching between them, providing a basis for adaptive subtraction.
[0194] Fourth step: Perform an inner product operation on the cross-correlation results to determine the filter value.
[0195] In some embodiments, the inner product operation is performed on the cross-correlation results using the following formula:
[0196]
[0197] Where m and n are both channel numbers, i and j are both variables, and P() is the cross-correlation result.
[0198] In some embodiments, performing the inner product operation on the cross-correlation result includes:
[0199] The inner product operation is performed on the cross-correlation results under the least squares constraint.
[0200] In some embodiments, determining the filter value includes:
[0201] The filter value is determined by iterative solution using the gradient descent method.
[0202] Optionally, under the least squares constraint, by taking the inner product of the cross-correlation results using equation (1), the least squares expression in the minimum energy form is obtained, where equation (1) can be expressed as follows:
[0203]
[0204] Where m and n are both channel numbers, i and j are both variables, and P() is the cross-correlation result.
[0205] The least-squares adaptive filter is solved and implemented quickly using the robust gradient descent method. The preprocessing of the filter operator is studied, specifically as follows:
[0206] P(x) = arg min p(x)
[0207] To minimize p(t), where P(x) is the optimal filter value obtained through iterative solution, this value is used in the subtraction process.
[0208] Step 5: Based on the original seismic data, the noise data, and the filter value, remove the noise from the original seismic data using a preset processing model.
[0209] In some embodiments, the preset processing model includes:
[0210] R(x) = P(x)[s(x) - N(x)]
[0211] Where R() is the result of the subtraction, s() is the original seismic data, N() is the noise data, and P() is the filter value.
[0212] Optionally, the adaptively subtracted seismic data can be output to obtain data after eliminating surface wave dispersion noise.
[0213] 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.
[0214] Example 5
[0215] Figure 5 A connection block diagram of an electronic device provided in an embodiment of this application, such as... Figure 5 As shown, the electronic device 500 may include: one or more processors 501, memory 502, multimedia components 503, input / output (I / O) interface 504, and communication components 505.
[0216] The memory 502 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. One or more processors 501 are used to execute all or part of the steps as described in the foregoing method embodiments.
[0217] The first step is to acquire raw seismic data and noise data, wherein the noise data includes noise data predicted from the raw seismic data.
[0218] In some embodiments, the noise data is obtained from the original seismic data using a preset prediction method.
[0219] Those skilled in the art will understand that the method for predicting noise data from the raw seismic data can be selected according to the user's actual needs, and the preset prediction method can also be selected according to the user's actual needs; no specific limitations are made here.
[0220] The second step is to convert the original seismic data and the noise data to the frequency domain respectively, so as to obtain the corresponding frequency domain seismic data and frequency domain noise data.
[0221] In some embodiments, the original seismic data and the noise data are converted to the frequency domain by Fourier transform, respectively.
[0222] Optionally, Fourier transforms can be performed on the original seismic data and the noise data predicted from the original seismic data to transform the data to the frequency domain.
[0223] The second step is to perform cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain, and determine the cross-correlation result.
[0224] In some embodiments, the step of performing cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain to determine the cross-correlation result includes:
[0225] Cross-correlation processing is performed on the seismic data in the frequency domain and the noise data in the frequency domain, and the cross-correlation result is determined when the cross-correlation coefficient is maximized.
[0226] Optionally, the original record and the predicted noise signal are cross-correlated. The result with the highest cross-correlation coefficient is considered to best match the geological reality and is used as the final high-resolution processing result of the characteristic dominant spectrum extension. This allows for amplitude matching between them, providing a basis for adaptive subtraction.
[0227] Fourth step: Perform an inner product operation on the cross-correlation results to determine the filter value.
[0228] In some embodiments, the inner product operation is performed on the cross-correlation results using the following formula:
[0229]
[0230] Where m and n are both channel numbers, i and j are both variables, and P() is the cross-correlation result.
[0231] In some embodiments, performing the inner product operation on the cross-correlation result includes:
[0232] The inner product operation is performed on the cross-correlation results under the least squares constraint.
[0233] In some embodiments, determining the filter value includes:
[0234] The filter value is determined by iterative solution using the gradient descent method.
[0235] Optionally, under the least squares constraint, by taking the inner product of the cross-correlation results using equation (1), the least squares expression in the minimum energy form is obtained, where equation (1) can be expressed as follows:
[0236]
[0237] Where m and n are both channel numbers, i and j are both variables, and P() is the cross-correlation result.
[0238] The least-squares adaptive filter is solved and implemented quickly using the robust gradient descent method. The preprocessing of the filter operator is studied, specifically as follows:
[0239] P(x) = arg min p(x)
[0240] To minimize p(t), where P(x) is the optimal filter value obtained through iterative solution, this value is used in the subtraction process.
[0241] Step 5: Based on the original seismic data, the noise data, and the filter value, remove the noise from the original seismic data using a preset processing model.
[0242] In some embodiments, the preset processing model includes:
[0243] R(x) = P(x)[s(x) - N(x)]
[0244] Where R() is the result of the subtraction, s() is the original seismic data, N() is the noise data, and P() is the filter value.
[0245] Optionally, the adaptively subtracted seismic data can be output to obtain data after eliminating surface wave dispersion noise.
[0246] It should be noted that one or more processors 501 may be implemented as application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, for performing the methods described above.
[0247] The memory 502 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.
[0248] Multimedia component 503 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.
[0249] I / O interface 504 provides an interface between one or more processors 501 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons can be virtual buttons or physical buttons.
[0250] The communication component 505 is used for wired or wireless communication between the electronic device 500 and other devices. Wired communication includes communication via network ports, serial ports, etc.; wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, 5G, or one or more combinations thereof. Therefore, the corresponding communication component 505 may include a Wi-Fi module, a Bluetooth module, and an NFC module.
[0251] In summary, this application provides a method, apparatus, storage medium, and electronic device for earthquake noise removal. The earthquake noise removal method utilizes the core principle of least-squares adaptive filtering subtraction, employing a weighted minimum energy criterion, i.e., a cost function, to ultimately obtain the filter value. Specifically: it acquires original earthquake data and noise data, wherein the noise data includes noise data predicted from the original earthquake data; it converts the original earthquake data and the noise data to the frequency domain, respectively, to obtain corresponding frequency-domain earthquake data and frequency-domain noise data; it performs cross-correlation processing on the frequency-domain earthquake data and the frequency-domain noise data to determine the cross-correlation result; it performs an inner product operation on the cross-correlation result to determine the filter value; and it removes noise from the original earthquake data using a preset processing model based on the original earthquake data, the noise data, and the filter value. By utilizing the technical solution of this application, noise can be effectively and adaptively subtracted, exhibiting high flexibility, applicability to noise subtraction under complex conditions, and the ability to improve the signal-to-noise ratio and optimize seismic profiles, achieving better adaptive subtraction results compared to existing technologies.
[0252] It should also be understood that the methods or systems disclosed in the embodiments provided in this application can also be implemented in other ways. The method or system 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 apparatus according to various embodiments of this application. In this regard, each block in the 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 diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, 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.
[0253] 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 a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, apparatus, or apparatus that includes that element. The use of terms such as "first," "second," etc., 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 expressly defined, terms such as "noise data," "filter value," "cross-correlation processing," "inner product," and "least square constraint" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of these terms in this application based on the specific content of the technical solution. Furthermore, in the description of this application, unless otherwise stated, the terms "multiple" or "many" mean at least two.
[0254] 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.
[0255] 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 method for removing earthquake noise, characterized in that, The method includes: Acquire raw seismic data and noise data, wherein the noise data includes noise data predicted from the raw seismic data; The original seismic data and the noise data are respectively converted to the frequency domain to obtain the corresponding frequency domain seismic data and frequency domain noise data; Cross-correlation processing is performed on the seismic data in the frequency domain and the noise data in the frequency domain to determine the cross-correlation result; Perform an inner product operation on the cross-correlation results to determine the filter value; Based on the original seismic data, the noise data, and the filter value, noise in the original seismic data is removed using a preset processing model. The inner product operation is performed on the cross-correlation results using the following formula: in, The number of time sampling points, For the Tao, For cross-correlation results; The preset processing model includes: in, The result is the data after subtraction. The original earthquake data, For noisy data, The filter value; The step of performing the inner product operation on the cross-correlation result includes: The inner product operation is performed on the cross-correlation results under the least squares constraint.
2. The method according to claim 1, characterized in that, The original seismic data and the noise data were converted to the frequency domain using Fourier transform.
3. The method according to claim 1, characterized in that, The determination of the filter value includes: The filter value is determined by iterative solution using the gradient descent method.
4. The method according to claim 1, characterized in that, The process of cross-correlation processing of the seismic data in the frequency domain and the noise data in the frequency domain to determine the cross-correlation result includes: Cross-correlation processing is performed on the seismic data in the frequency domain and the noise data in the frequency domain, and the cross-correlation result is determined when the cross-correlation coefficient is maximized.
5. An apparatus based on any one of the earthquake noise removal methods according to claims 1 to 4, characterized in that, include: An acquisition module is used to acquire raw seismic data and noise data, wherein the noise data includes noise data predicted from the raw seismic data; The conversion module is used to convert the original seismic data and the noise data to the frequency domain respectively, so as to obtain the corresponding frequency domain seismic data and frequency domain noise data. The cross-correlation module is used to perform cross-correlation processing on the seismic data in the frequency domain and the noise data in the frequency domain, and determine the cross-correlation result; The determination module is used to perform an inner product operation on the cross-correlation results to determine the filter value; The noise removal module is used to remove noise from the original seismic data using a preset processing model based on the original seismic data, the noise data, and the filter value.
6. A computer-readable storage medium, characterized in that, The computer program stored in the computer-readable storage medium can be executed by one or more processors to implement the method as described in any one of claims 1 to 4.
7. An electronic device, characterized in that, The device includes 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. When the computer program is executed by the one or more processors, it performs the method as described in any one of claims 1 to 4.