Seismic coherent noise suppression method and device based on wideband linear array beamforming

Abstract

The application provides a seismic coherent noise suppression method and device based on wideband linear array beam forming, electronic equipment and storage medium, which comprises: performing direction of arrival estimation processing on observed array signal data and calculating a plurality of seismic wave signal incident angles; obtaining expected signal incident angles and interference signal incident angles according to the size relationship between each seismic wave signal incident angle and a preset angle threshold; establishing a target constraint function based on a linearly constrained minimum variance criterion according to the expected signal incident angles and the interference signal incident angles; calculating an optimal weighting vector based on the target constraint function and a seismic wave beam forming minimum output power formula; and processing the array signal data by using the optimal weighting vector to obtain noise-suppressed seismic signal records. The application suppresses interference signals based on the difference between the incident angles of effective signals and interference signals, thereby avoiding the damage of frequency filtering to the frequency components of effective signals.

Description

Technical Field

[0001] This application relates to the field of ground microseismic data processing technology, and in particular to a method, apparatus, electronic device and storage medium for suppressing seismic coherent noise based on broadband linear array beamforming. Background Technology

[0002] With the development of unconventional oil and gas reservoirs, microseismic monitoring technology is being used more and more widely, and it is one of the key technologies for the development of unconventional oil and gas resources (especially shale gas). Among microseismic monitoring technologies, surface microseismic monitoring technology has the advantages of low cost, no influence from monitoring well conditions, large coverage, and flexible deployment of monitoring stations; however, compared with well microseismic monitoring, the signals received are more affected by environmental noise and surrounding surface conditions. Therefore, the quality of surface microseismic data directly affects the subsequent microseismic event location and interpretation.

[0003] Microseismic monitoring primarily involves receiving signals generated by rock fractures and processing these signals, including localization, to determine the location of the fractures and reveal the fracturing effect. Therefore, microseismic event identification is a crucial prerequisite for rock fracture localization. However, surface microseismic data often contains strong coherent interference signals, such as shear wave signals and linear interference signals, with shear wave interference signals being particularly prone to being identified as microseismic events. Currently, manual quality control is used to eliminate false events to improve the reliability of event identification. However, due to the large volume of surface microseismic monitoring data, manual quality control to identify false events is extremely time-consuming.

[0004] The relevant literature currently available includes:

[0005] The patent document "CN110554434 Earthquake Noise Suppression Method and Device" uses frequency shifting, synchronous squeezing wavelet transform, and fitting prediction to use the energy region unaffected by noise to fit and predict the energy region where the noise is located, thereby separating the effective signal from the noise. This method suppresses the effective signal and noise in the frequency domain.

[0006] The journal article "Progress in Geophysics, December 2018, Noise Suppression Methods for Surface Microseismic Data" mainly focuses on a combined denoising method that combines single-frequency noise suppression, frequency-divisional anomalous amplitude attenuation, and random noise suppression, and does not involve coherent noise suppression techniques.

[0007] The journal article "Petroleum Geophysical Exploration, July 2016, Data Regularization of Irregular Observation Systems and Its Application in Coherent Noise Suppression" uses five-dimensional interpolation technology to process irregular gathers to meet the assumptions of coherent noise suppression, and then uses FX denoising technology or FK filtering for noise suppression. Essentially, it is a filtering and suppression technique.

[0008] My master's thesis, "Research on a Deep Learning-Based Method for Suppressing Coherent Noise in Marine Exploration (December 2019)," is a suppression technique for marine coherent noise, also known as aliasing noise. Aliasing noise is a unique noise in marine exploration and differs significantly from surface coherent noise.

[0009] Therefore, there is an urgent need to study an effective technique for suppressing coherent noise in array signals. Summary of the Invention

[0010] To address the aforementioned issues, this application provides a method, apparatus, electronic device, and storage medium for suppressing seismic coherent noise based on broadband linear array beamforming.

[0011] This application provides a method for suppressing seismic coherent noise based on broadband linear array beamforming, including:

[0012] The observed array signal data is processed to estimate the direction of arrival and the incident angles of multiple seismic wave signals are calculated.

[0013] The desired signal incident angle and the interference signal incident angle are obtained based on the relationship between the incident angle of each seismic wave signal and the preset angle threshold.

[0014] Based on the incident angle of the desired signal and the incident angle of the interference signal, a target constraint function is established according to the linear constraint minimum variance criterion.

[0015] The optimal weighting vector is calculated based on the target constraint function and the formula for minimum output power of seismic wave beamforming.

[0016] The array signal data is processed using the optimal weighting vector to obtain a noise-suppressed seismic signal record.

[0017] In some embodiments, the process of estimating the direction of arrival (DOA) of the observed array signal data and calculating multiple seismic wave signal incident angles includes:

[0018] Extract time-domain sampling data x with a time length of T from the M array signal data. m (t)(m=1,2,…,M; t=1,2,…,T);

[0019] The extracted time-domain sampled data of length T is divided into K sub-segments, and the time-domain sampled data of each sub-segment is subjected to a Discrete Fourier Transform to obtain the transformed data of each sub-segment, as shown below:

[0020] X k (f j ) = [X 1k (f j ),…,X Mk (fj )] H ---(1)

[0021] Where k = 1 to K, j = 1 to J, j represents the j-th sub-frequency band, and k and j are both integers, f j Let X represent the j-th frequency. k (f j The frequency of the k-th sub-segment of the array signal is f. j Frequency domain data, X 1k (f j f represents the k-th sub-segment of the first array signal. j Frequency component, X Mk (f j f represents the k-th sub-segment of the M-th array signal. j Frequency components;

[0022] The focusing matrix T(f) of each sub-band is calculated using the rotating signal subspace transformation algorithm. j ), represented as:

[0023] T(f j )=V(f j )U H (f j )---(2)

[0024] Where U(f) j ) and V(f j ) are respectively A(f j ,θ)A H (f c The left and right singular vectors of f(θ) c To determine the focusing frequency, the main frequency is often chosen as the focusing frequency, A(f j θ) is the array manifold matrix composed of direction vectors. This represents the incident angle of the nth signal. It is a direction vector. d is the spacing between adjacent arrays, and v is the seismic wave propagation velocity;

[0025] The data covariance matrix is ​​calculated based on the focusing matrix of each sub-band and the transform data of each sub-band, and the data covariance matrix is ​​expressed as follows:

[0026]

[0027] Among them, X H k (f j ) is X k (f j The transpose of ) T H (fj ) is T(f j The transpose of R y Let X be the data covariance matrix. k (f j ) represents the frequency of the k-th sub-segment of the array signal as f. j Frequency domain data, T(f j () represents the focusing matrix;

[0028] The noise subspace feature vector E is obtained by performing eigenvalue decomposition on the data covariance matrix. N ;

[0029] According to the noise subspace feature vector E N Solving the spectral estimation formula yields the incident angle of the seismic wave signal. The spectral estimation formula of the signal subspace transformation algorithm is expressed as follows:

[0030]

[0031] Wherein, α(f c α, θ) is the search direction vector, and α H (f c ,θ) is α(f c The transpose of θ, E N H For E N The transpose of f c The focusing frequency is usually chosen as the main frequency, and θ is the incident angle of the signal.

[0032] In some embodiments, obtaining the desired signal incident angle and the interference signal incident angle based on the relationship between the incident angle of each seismic wave signal and a preset angle threshold includes:

[0033] Calculate the difference between the incident angle of each seismic wave signal and the preset angle threshold;

[0034] The calculated differences are sorted, and the incident angle of the seismic wave signal corresponding to the smallest difference is taken as the expected incident angle θ. d Except for the incident angle of the seismic wave signal corresponding to the minimum difference, all other incident angles of the seismic wave signal are taken as the incident angles θ of the interference signal. noise .

[0035] In some embodiments, the target constraint function is:

[0036]

[0037] The objective constraint function requires θ d The desired signal is incident in a direction and passes through without loss, θ noiseInterference signals incident from a specific direction cannot pass through;

[0038] make If F = (1, 0), then the objective constraint function simplifies to:

[0039] W H C = F H ---(6)

[0040] Where W is the optimal weighting vector.

[0041] In some embodiments, the calculation of the optimal weighting vector based on the target constraint function and the minimum output power formula for seismic wave beamforming includes:

[0042] The optimal weighted vector expression is obtained using Lagrange multipliers when the target constraint function is satisfied and the weighted vector satisfies the condition of minimizing the output power of seismic wave beamforming. The optimal weighted vector expression is:

[0043] W=R -1 C[C H R -1 C] -1 F---(7)

[0044] Where R is the covariance matrix of the array signal data. F = (1,0); the formula for the minimum output power of the seismic wave beamforming is:

[0045] J(W)=minE[|y(t| 2 ]=min{W H RW}---(8)

[0046] Where y(t) is the desired output signal;

[0047] The optimal weighted vector is calculated based on the optimal weighted vector expression.

[0048] In some embodiments, processing the array signal data using the optimal weighting vector to obtain a noise-suppressed seismic signal record includes:

[0049] The broadband linear array beam is obtained by multiplying the optimal weighting vector W with the array signal data to form a noise-suppressed seismic signal record. The calculation formula is as follows:

[0050]

[0051] Where y(f) is the denoised frequency domain signal, W(f) is the optimal weight vector, and W H (f) is the transpose of W(f), x m(f) represents the frequency domain data of the m-th array signal (m = 1, 2, ..., M).

[0052] In some embodiments, the method further includes:

[0053] The ground is observed through radial survey lines to obtain array signal data of microseisms, wherein each survey line is a linear array and each linear array includes multiple receiving points;

[0054] The array signal data is subjected to longitudinal wave time difference correction processing.

[0055] This application provides a seismic coherent noise suppression device based on broadband linear array beamforming, comprising:

[0056] The angle estimation module is used to perform direction-of-arrival estimation on the observed array signal data and calculate the incident angles of multiple seismic wave signals.

[0057] The desired signal angle acquisition module is used to obtain the desired signal incident angle and the interference signal incident angle based on the relationship between the incident angle of each seismic wave signal and a preset angle threshold.

[0058] The objective function establishment module is used to establish an objective constraint function based on the linear constraint minimum variance criterion according to the incident angle of the desired signal and the incident angle of the interference signal.

[0059] The weight vector calculation module is used to calculate the optimal weight vector based on the target constraint function and the formula for minimum output power of seismic wave beamforming.

[0060] The noise suppression processing module is used to process the array signal data using the optimal weighting vector to obtain a noise-suppressed seismic signal record.

[0061] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, performs the seismic coherent noise suppression method based on broadband linear beamforming as described above.

[0062] This application provides a storage medium storing a computer program that can be executed by one or more processors and can be used to implement the seismic coherent noise suppression method based on broadband linear beamforming as described above.

[0063] This application discloses a method, apparatus, electronic device, and storage medium for suppressing coherent seismic noise based on broadband linear array beamforming. The method involves calculating the incident angle of seismic waves from seismic data, determining the incident angle of the desired signal and the incident angle of the interference signal, establishing a target constraint function, solving for the optimal weight vector, and finally using broadband beamforming to estimate the effective signal, thus obtaining the noise-suppressed seismic record. This application suppresses the interference signal based on the difference in the incident angles of the effective signal and the coherent interference signal, unlike traditional coherent noise suppression techniques. It avoids the damage to the frequency components of the effective signal caused by frequency filtering. This method can be used not only for coherent noise suppression of ground microseismic data, providing assistance for microseismic data processing and event identification, but also for coherent noise suppression in conventional ground 3D seismic exploration, thus possessing broad application prospects. Attached Figure Description

[0064] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings.

[0065] Figure 1 A schematic diagram illustrating the implementation process of a seismic coherent noise suppression method based on broadband linear array beamforming, provided in an embodiment of this application;

[0066] Figure 2 A schematic diagram illustrating the implementation process of a seismic wave signal incident angle calculation method in a seismic coherent noise suppression method based on broadband linear array beamforming, provided in an embodiment of this application;

[0067] Figure 3 A schematic diagram illustrating the implementation process of the desired signal incident angle determination method in a seismic coherent noise suppression method based on broadband linear array beamforming provided in this application embodiment;

[0068] Figure 4 A schematic diagram illustrating the implementation process of the optimal weighted vector calculation method in a seismic coherent noise suppression method based on broadband linear array beamforming, provided in an embodiment of this application;

[0069] Figure 5 A schematic diagram illustrating the implementation process of another seismic coherent noise suppression method based on broadband linear array beamforming provided in this application embodiment;

[0070] Figure 6 This is a schematic diagram of the linear array and seismic wave incident provided in an embodiment of this application;

[0071] Figures 7(a)-(b) are schematic diagrams of seismic records and beamforming results provided in the embodiments of this application;

[0072] Figures 8(a)-(c) are schematic diagrams of the beamforming directional gain, estimated signal source (b), and denoised seismic record provided in the embodiments of this application;

[0073] Figure 9 A structural block diagram of a seismic coherent noise suppression device based on broadband linear array beamforming provided in this application embodiment;

[0074] Figure 10 This is a structural block diagram of an electronic device provided in an embodiment of this application.

[0075] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0077] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0078] If the application documents contain similar descriptions such as "first, second, third", the following explanation shall be added: In the following description, the terms "first, second, third" are used only to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0079] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0080] To address the problems existing in related technologies, this application provides a seismic coherent noise suppression method based on broadband linear beamforming. This method is applied to electronic devices, such as computers and mobile terminals. The functions implemented by the seismic coherent noise suppression method based on broadband linear beamforming provided in this application can be achieved by the processor of the electronic device calling program code, which can be stored in a computer storage medium.

[0081] Example 1

[0082] This application provides a method for suppressing seismic coherent noise based on broadband linear array beamforming. Figure 1 This application provides a schematic diagram illustrating the implementation process of a seismic coherent noise suppression method based on broadband linear array beamforming, as shown in the embodiments below. Figure 1 As shown, it includes:

[0083] Step S100: Perform direction of arrival estimation processing on the observed array signal data and calculate the incident angles of multiple seismic wave signals;

[0084] The seismic wave incident angle calculation technique in this embodiment is based on the Rotating Signal Subspace (RSS) method for angle estimation. Specifically:

[0085] Take time-domain sampled data x with a time length of T m The data (t)(m=1,2,…,M;t=1,2,…,T) is divided into K sub-segments. The discrete Fourier transform is performed on each sub-segment to obtain the transformed data for each sub-segment.

[0086] X k (f j ) = [X 1k (f j ),…,X Mk (f j )] H ---(1);

[0087] Where k = 1 to K, j = 1 to J, j represents a sub-frequency band, and k and j are both integers, f j Let X represent the j-th frequency. k (f j The frequency of the k-th sub-segment of the array signal is f. j Frequency domain data, X 1k (f j f represents the k-th sub-segment of the first array signal. j Frequency component, X Mk (f j f represents the k-th sub-segment of the M-th array signal. j Frequency components;

[0088] For sub-bands j = 1, ..., J, the obtained focusing matrix T(f) j ),

[0089] T(f j )=V(f j )U H (f j )---(2)

[0090] Where U(f) j ) and V(f j) are respectively A(f j ,θ)A H (f c The left and right singular vectors of f(θ) c To determine the focusing frequency, the main frequency is often chosen as the focusing frequency, A(f j θ) is the array manifold matrix composed of direction vectors. This represents the incident angle of the nth signal. The direction vector is expressed as:

[0091]

[0092] Where v is the seismic wave propagation velocity and d is the distance between adjacent arrays;

[0093] Calculate the covariance matrix of the focused data based on the obtained focusing matrix.

[0094]

[0095] Among them, X H k (f j ) is X k (f j The transpose of T) H (f j ) is T(f j The transpose of R is given by R. y X is the data covariance matrix; k (f j ) represents the frequency of the k-th sub-segment of the array signal as f. j Frequency domain data, T(f j () represents the focusing matrix;

[0096] Eigenvalue decomposition is performed on the focused covariance matrix to obtain the noise subspace eigenvector E. N ,

[0097] The specific method is as follows: sort the eigenvalues ​​of the covariance matrix from largest to smallest, and the eigenvectors corresponding to the remaining relatively small eigenvalues ​​constitute the noise subspace.

[0098]

[0099] Wherein, α(f c α, θ) is the search direction vector, and α H (f c ,θ) is α(f c E is the transpose of (θ). N H For E N The transpose vector, f cThe focusing frequency is usually chosen as the main frequency, and θ is the incident angle of the signal.

[0100] The final angle estimate is obtained according to equation (4).

[0101] Step S200: Obtain the desired signal incident angle and the interference signal incident angle based on the relationship between the incident angle of each seismic wave signal and the preset angle threshold.

[0102] In this embodiment, the desired signal angle is close to zero, and signals incident at other angles are interference signals. After the microseismic data is corrected for P-wave time difference, the desired P-wave signal phase axis is approximately horizontal, that is, the P-wave is similar to a plane wave incident with an incident angle close to 0. However, after the coherent signal S-wave is corrected for P-wave time difference, the phase axis remains curved, that is, the incident angle is greater than 0.

[0103] Step S300: Establish a target constraint function based on the linear constraint minimum variance criterion according to the incident angle of the desired signal and the incident angle of the interference signal;

[0104] In this embodiment, the optimal weighted vector is solved based on the Linear Constraint Minimum Variance (LCMV) criterion. The constraint requires that θ... d The desired signal is incident in a direction and passes through without loss, θ noise Interference signals incident in a specific direction cannot pass through; the specific expression is as follows:

[0105]

[0106] make If F = (1, 0), then the objective constraint function simplifies to:

[0107] W H C = F H ---(6)

[0108] Where W is the optimal weighting vector.

[0109] Step S400: Calculate the optimal weighting vector based on the target constraint function and the minimum output power formula for seismic wave beamforming;

[0110] In this embodiment of the application, under the condition of satisfying the constraints, the weighting vector w minimizes the output power of the beamforming, that is:

[0111] J(W)=minE[|y(t| 2 ]=min{W H RW}---(7)

[0112] Where R is the covariance matrix of the observed data, and y(t) is the desired output signal. The optimal weighting vector expression obtained using Lagrange multipliers is:

[0113] W=R -1 C[C H R -1 C] -1 F---(8)

[0114] Where R is the covariance matrix of the array signal data, and... F = (1,0);

[0115] The optimal weight vector is obtained based on the above formula.

[0116] Step S500: The array signal data is processed using the optimal weighting vector to obtain a noise-suppressed seismic signal record.

[0117] In this embodiment, the seismic record after noise suppression by broadband linear array beamforming is obtained by multiplying the optimal weighting vector W with the array signal reception record. The specific calculation expression is as follows:

[0118]

[0119] Where y(f) is the denoised frequency domain signal, W(f) is the optimal weight vector, and W H (f) is the transpose of W(f), x m (f) represents the frequency domain data of the m-th array signal (m = 1, 2, ..., M).

[0120] This application provides a method for suppressing coherent seismic noise based on broadband linear array beamforming. The method calculates the incident angle of seismic waves from seismic data, determines the incident angle of the desired signal and the incident angle of the interference signal, establishes a target constraint function, solves for the optimal weight vector, and finally uses broadband beamforming to estimate the effective signal, obtaining the noise-suppressed seismic record. This method suppresses the interference signal based on the difference in the incident angles of the effective signal and the coherent interference signal, unlike traditional coherent noise suppression techniques. It avoids the damage to the frequency components of the effective signal caused by frequency filtering. This method can be used not only for coherent noise suppression of ground microseismic data, providing assistance for microseismic data processing and event identification, but also for coherent noise suppression in conventional ground 3D seismic exploration, thus having broad application prospects.

[0121] Example 2

[0122] Based on the foregoing embodiments, this application further provides a method for suppressing seismic coherent noise based on broadband linear array beamforming. Figure 2This is a schematic diagram illustrating the implementation process of a seismic wave signal incident angle calculation method in a seismic coherent noise suppression method based on broadband linear array beamforming, as provided in an embodiment of this application. Figure 2 As shown, it includes:

[0123] Step S101: Extract time-domain sampling data with a time length of T from the array signal data;

[0124] Step S102: Divide the extracted time-domain sampling data of time length T into K sub-segments, and perform discrete Fourier transform on the time-domain sampling data of each sub-segment to obtain the transformed data of each sub-segment;

[0125] Step S103: Calculate the focusing matrix for each sub-band using the rotating signal subspace transformation algorithm;

[0126] Direction of arrival (DOA) estimation is widely used in radar, sonar, satellite, and mobile communication systems. Its fundamental problem is determining the spatial location (DOA) of multiple signals of interest simultaneously located in a given area. The processing methods vary depending on the signal type. Currently, broadband signal processing algorithms mainly fall into two categories: methods based on incoherent signals and methods based on coherent signals. This application focuses on suppressing coherent signals in microseismic signal data; therefore, a coherent signal-based processing method is employed. The core idea of ​​this method is to transform data from various frequency points into data from a unified frequency point using a focusing matrix, thereby forming a correlation matrix. The choice of algorithm for processing the focusing matrix is ​​crucial; in this embodiment, the Rotated Signal Subspace Transform (RSS) algorithm is used.

[0127] Step S104: Calculate the data covariance matrix based on the focusing matrix of each sub-band and the transform data of each sub-band;

[0128] Step S105: Perform eigenvalue decomposition on the data covariance matrix to obtain the noise subspace feature vector; sort the eigenvalues ​​of the covariance matrix from largest to smallest, and the feature vectors corresponding to the remaining relatively smaller eigenvalues ​​constitute the noise subspace.

[0129] Step S106: Obtain the incident angle of the seismic wave signal by solving the spectrum estimation formula based on the noise subspace characteristic vector. Based on the spatial spectrum calculation results, starting from the origin, mark the local maxima in the spatial spectrum as the signal incident angle.

[0130] The seismic coherent noise suppression method based on broadband linear beamforming in this application first uses discrete Fourier transform time-domain sampling data, then uses a rotating signal subspace transformation algorithm to calculate the focusing matrix of each sub-band, and combines the transformed time-domain sampling data to calculate the data covariance matrix, solve for the noise subspace eigenvector, and finally obtain the seismic wave signal incident angle by solving the spectrum estimation formula based on the noise subspace eigenvector. The seismic wave signal incident angle calculation method in this application can accurately calculate the seismic wave signal incident angle.

[0131] Example 3

[0132] Based on the foregoing embodiments, this application further provides a method for suppressing seismic coherent noise based on broadband linear array beamforming. Figure 3 This is a schematic diagram illustrating the implementation process of the desired signal incident angle determination method in another seismic coherent noise suppression method based on broadband linear array beamforming provided in this application embodiment, as shown below. Figure 3 As shown, it includes:

[0133] The step of obtaining the desired signal incident angle and the interference signal incident angle based on the relationship between the incident angle of each seismic wave signal and a preset angle threshold includes:

[0134] Step S201: Calculate the difference between the incident angle of each seismic wave signal and the preset angle threshold;

[0135] For example: the incident angles of the seismic wave signals are θ1, θ2, θ3, θ4...θ n ;

[0136] The preset angle threshold is θ0; in this embodiment, the preset angle threshold is 0.

[0137] The differences between the incident angle of each seismic wave signal and the preset angle threshold are as follows:

[0138] A1 = |θ1 - θ0|,

[0139] A2 = |θ2 - θ0|,

[0140] A3 = |θ3 - θ0|,

[0141] A4 = |θ4 - θ0|, ......

[0143] An = |θn - θ0|.

[0144] Step S202: Sort the calculated differences, take the incident angle of the seismic wave signal corresponding to the smallest difference as the incident angle of the desired signal, and take the incident angles of the seismic wave signal other than the incident angle of the seismic wave signal corresponding to the smallest difference as the incident angles of the interference signal.

[0145] Compare A1 to An, sort them from largest to smallest, and filter out the one with the smallest difference, such as A1.

[0146] The incident angle θ1 of the seismic wave signal corresponding to A1 is taken as the incident angle of the desired signal, and the incident angles of the desired signals corresponding to the other signals A2-An are taken as the incident angles of the interference signals. Based on this, matrices C and F are obtained.

[0147] In the seismic coherent noise suppression method based on broadband linear beamforming in this application embodiment, the method for determining the incident angle of the desired signal is to quickly filter out the incident angle of the desired signal and the incident angle of the interference signal by comparing the magnitude relationship between the incident angle of each seismic wave signal and a preset angle threshold, so as to distinguish between the effective signal and the interference signal, thereby improving the processing efficiency of seismic coherent noise suppression.

[0148] Example 4

[0149] Based on the foregoing embodiments, this application further provides a method for suppressing seismic coherent noise based on broadband linear array beamforming. Figure 4 This is a schematic diagram illustrating the implementation process of the optimal weighted vector calculation method in a seismic coherent noise suppression method based on broadband linear array beamforming, as provided in an embodiment of this application. Figure 4 As shown, it includes:

[0150] Step S401: When the target constraint function is satisfied and the weighting vector satisfies the minimum output power of seismic wave beamforming, the optimal weighting vector expression is obtained using Lagrange multipliers.

[0151]

[0152] Construct the Lagrange equation: L = W H RW+λ(W H CF), we get:

[0153] L=W H RW+λ(W H CF)---(11)

[0154]

[0155] Therefore, W opt =R -1 Cλ---(13)

[0156] Because of C H R -1 Cλ=F then λ=(C H R -1 C) -1 F---(14)

[0157] Substituting equation (14) into equation (13) yields the optimal weight vector expression:

[0158] W opt =R -1 C(C H R -1 C) -1 F

[0159] Step S402: Calculate the optimal weighted vector according to the optimal weighted vector expression.

[0160] After obtaining the covariance matrices R and C, substitute them into the formula W. opt =R -1 C(C H R -1 C) -1 The optimal weight vector W can be obtained by calculating F.

[0161] This application provides a seismic coherent noise suppression method based on broadband linear array beamforming. Under the condition of satisfying the target constraint function and minimizing the output power of beamforming, the optimal weighting vector expression is obtained by using Lagrange multipliers. The optimal weighting vector is then calculated for noise suppression processing of array signal data. The obtained optimal weighting vector can effectively suppress interference signals, avoid damage to the frequency components of the effective signal caused by frequency filtering, and improve the accuracy and efficiency of noise suppression.

[0162] Example 5

[0163] Based on the foregoing embodiments, this application further provides a method for suppressing seismic coherent noise based on broadband linear array beamforming. Figure 5 This application provides a schematic diagram illustrating the implementation process of a seismic coherent noise suppression method based on broadband linear array beamforming, as shown in the embodiments below. Figure 5 As shown, it includes:

[0164] Step S100a: Observe the ground through radial survey lines to obtain array signal data of microseismic events, wherein each survey line is a linear array, and each linear array includes multiple receiving points;

[0165] In this embodiment, the ground is observed using radial survey lines to obtain array signal data of microseismic events. Each survey line is a linear array, and each linear array includes multiple receiving points, such as... Figure 6As shown, for example, there are 20 receiving points.

[0166] Step S100b: Perform longitudinal wave time difference correction processing on the array signal data.

[0167] In this embodiment, after P-wave time difference correction, the received microseismic array signal data has an expected P-wave phase axis that is approximately horizontal, meaning the P-wave is similar to a plane wave incident with an incident angle close to 0. However, after P-wave time difference correction, the coherent shear wave signal still has a curved phase axis, meaning its incident angle is greater than 0. For example... Figure 6 As shown, the desired signal S1 is incident from 0°, and the time difference between adjacent channels on the same phase axis is 0ms.

[0168] Step S100: Perform direction of arrival estimation processing on the observed array signal data and calculate the incident angles of multiple seismic wave signals;

[0169] In this embodiment of the application, in order to analyze the spatial accuracy of the direction of arrival (DOA) estimation, an interference signal S2 with a time difference very close to that of the adjacent channel of the desired signal is deliberately set. The time difference of the adjacent channel is 2ms. The time difference of the interference signal S3 with the adjacent channel is 5ms. Figures 7(a)-(b) are schematic diagrams of the seismic record and beamforming results provided in this embodiment of the application. Figure 7(a) is the seismic record, and Figure 7(b) is the beamforming result. The congruent record is shown in Figure 7(a).

[0170] The directional spectrum obtained by DOA technology is shown in Figure 7(b). The accurate time difference between adjacent channels (signal incident angle) is obtained, as shown by the points inside the circles in the figure.

[0171] Step S200: Obtain the desired signal incident angle and the interference signal incident angle based on the relationship between the incident angle of each seismic wave signal and the preset angle threshold.

[0172] In this embodiment, the desired signal angle is determined based on the incident angles of the three signals. Typically, the smallest time difference closest to zero is selected as the time difference of the desired signal, and the rest are the time differences of adjacent channels of the interference signal. Figure 7(b) shows that the DOA directional spectrum has high resolution. Even when the incident angles of the interference signal and the effective signal are similar, the incident angles of the desired signal and the interference signal can be accurately determined through the DOA directional spectrum.

[0173] Step S300: Establish a target constraint function based on the linear constraint minimum variance criterion according to the incident angle of the desired signal and the incident angle of the interference signal;

[0174] Step S400: Calculate the optimal weighting vector based on the target constraint function and the minimum output power formula for seismic wave beamforming;

[0175] Step S500: The array signal data is processed using the optimal weighting vector to obtain a noise-suppressed seismic signal record.

[0176] The optimal weighting vector is obtained by setting the target constraint function. The optimal weighting vector forms a suppression band in the direction of the interference signal. Figures 8(a)-(c) are schematic diagrams of the beamforming directional gain, estimated signal source (b), and denoised seismic record provided in the embodiments of this application. Figure 8(a) is the directional gain, Figure 8(b) is the estimated signal source, and Figure 8(c) is the seismic record.

[0177] As shown in Figure 8(a), the estimated desired signal is shown in Figure 8(b). LCMV is the adaptively estimated signal source. Compared with the signal source estimated by conventional beamforming (CBF), both methods can suppress large-angle interference signals. However, signals close to the incident direction of the target signal can only be suppressed by adaptive beamforming. Based on the estimated signal source and the time difference between adjacent channels of the desired signal, time shifting is performed, and a denoised record is obtained.

[0178] Based on the obtained optimal weighting vector, the directional response of the array (observation arrangement) is calculated, as shown in Figure 8(a). The figure plots the directional response at some frequencies. It can be seen from the figure that the weighting vector enables the array to set a very low negative gain in the two interference directions, and the gain in the desired direction is 0dB, that is, there is no damage to the target signal, which meets the expected requirements.

[0179] In summary, the seismic coherent noise suppression method based on broadband linear beamforming in this application can suppress coherent interference signals and obtain seismic records containing only valid signals. Its operation process is simple and practical, providing assistance for microseismic data processing and event identification.

[0180] Example 6

[0181] Based on the foregoing embodiments, this application provides a seismic coherent noise suppression device based on broadband linear beamforming. The various modules and units included in the device can be implemented by a processor in a computer device; of course, they can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0182] This application provides a seismic coherent noise suppression device based on broadband linear array beamforming. Figure 9 A schematic diagram of a seismic coherent noise suppression device based on broadband linear beamforming provided in this application embodiment is shown below. Figure 9 As shown, the seismic coherent noise suppression device 600 based on broadband linear array beamforming includes:

[0183] Angle estimation module 601 is used to perform direction of arrival estimation processing on the observed array signal data and calculate the incident angles of multiple seismic wave signals;

[0184] The desired signal angle acquisition module 602 is used to obtain the desired signal incident angle and the interference signal incident angle according to the relationship between the incident angle of each seismic wave signal and the preset angle threshold.

[0185] The objective function establishment module 603 is used to establish an objective constraint function based on the linear constraint minimum variance criterion according to the incident angle of the desired signal and the incident angle of the interference signal.

[0186] The weight vector calculation module 604 is used to calculate the optimal weight vector based on the target constraint function and the minimum output power formula for seismic wave beamforming.

[0187] In addition, a noise suppression processing module 605 is used to process the array signal data using the optimal weighting vector to obtain a noise-suppressed seismic signal record.

[0188] In some embodiments, the angle estimation module 601 includes:

[0189] The data extraction unit is used to extract time-domain sampled data with a time length of T from the array signal data;

[0190] The Fourier transform unit is used to divide the extracted time-domain sampled data of length T into K sub-segments, and perform a discrete Fourier transform on the time-domain sampled data of each sub-segment to obtain the transformed data of each sub-segment.

[0191] The focusing matrix calculation unit is used to calculate the focusing matrix of each sub-band using the rotating signal subspace transformation algorithm.

[0192] The covariance matrix calculation unit is used to calculate the data covariance matrix based on the focusing matrix of each sub-band and the transformation data of each sub-band.

[0193] The eigenvalue decomposition unit is used to perform eigenvalue decomposition on the data covariance matrix to obtain the noise subspace feature vector;

[0194] An angle estimation unit is used to obtain the incident angle of the seismic wave signal by solving the spectral estimation formula based on the noise subspace feature vector.

[0195] In some embodiments, the desired signal angle acquisition module 602 includes:

[0196] The difference calculation unit is used to calculate the difference between the incident angle of each seismic wave signal and the preset angle threshold.

[0197] An angle determination unit is used to sort the calculated differences, take the incident angle of the seismic wave signal corresponding to the smallest difference as the expected signal incident angle, and take the incident angle of the seismic wave signal other than the incident angle of the seismic wave signal corresponding to the smallest difference as the incident angle of the interference signal.

[0198] In some embodiments, the objective function establishment module 603 requires that the signal in the direction of the incident angle of the desired signal pass through without loss, while the signal in the direction of the incident angle of the interference signal cannot pass through.

[0199] In some embodiments, the weight vector calculation module 604 includes:

[0200] The weighted vector expression acquisition unit is used to obtain the optimal weighted vector expression using Lagrange multipliers when the target constraint function is satisfied and the weighted vector satisfies the minimum output power of seismic wave beamforming.

[0201] The optimal weighted vector calculation unit is used to calculate the optimal weighted vector based on the optimal weighted vector expression.

[0202] In some embodiments, the noise suppression processing module 605 obtains a broadband linear array beam by multiplying the array signal data with the optimal weighting vector to form a noise-suppressed seismic signal record.

[0203] In some embodiments, the seismic coherence noise suppression device 600 based on broadband linear array beamforming further includes:

[0204] An array signal acquisition module is used to observe the ground through radial survey lines to obtain array signal data of microseismic events, wherein each survey line is a linear array, and each linear array includes multiple receiving points;

[0205] The correction processing module is used to perform longitudinal wave time difference correction processing on the array signal data.

[0206] This application provides a seismic coherent noise suppression device based on broadband linear beamforming. The device calculates the seismic wave incident angle from the seismic data using an angle estimation module. Then, a desired signal angle acquisition module determines the incident angles of the desired signal and the interference signal. An objective function establishment module establishes the objective constraint function, followed by a weight vector calculation module that solves for the optimal weight vector. Finally, a noise suppression processing module uses broadband beamforming to estimate the effective signal, obtaining the noise-suppressed seismic record. This application suppresses the interference signal based on the difference in the incident angles of the effective signal and the coherent interference signal, unlike traditional coherent noise suppression techniques. It avoids the damage to the frequency components of the effective signal caused by frequency filtering. This device can be used not only for coherent noise suppression of ground microseismic data, aiding in microseismic data processing and event identification, but also for coherent noise suppression in conventional ground 3D seismic exploration, thus possessing broad application prospects.

[0207] It should be noted that, in the embodiments of this application, if the above-described coherent noise suppression method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0208] Accordingly, this application provides a storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps in the seismic coherent noise suppression method provided in the above embodiments.

[0209] Example 7

[0210] This application provides an electronic device; Figure 10 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application, such as... Figure 10As shown, the electronic device 700 includes: a processor 701, at least one communication bus 702, a user interface 703, at least one external communication interface 704, and a memory 705. The communication bus 702 is configured to enable communication between these components. The user interface 703 may include a display screen, and the external communication interface 704 may include standard wired and wireless interfaces. The processor 701 is configured to execute a program stored in the memory for a seismic coherent noise suppression method based on broadband linear beamforming, to implement the steps in the seismic coherent noise suppression method based on broadband linear beamforming provided in the above embodiment.

[0211] The descriptions of the display device and storage medium embodiments above are similar to those of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the computer device and storage medium embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0212] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0213] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0214] It should be noted that, in this document, 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. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0215] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0216] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0217] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0218] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0219] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a controller to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0220] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A seismic coherent noise suppression method based on wideband line array beamforming, characterized in that, The method comprises the following steps: Obtaining array signal data of microseisms by observing the ground through radial measuring lines, wherein each measuring line is a linear array, and each linear array comprises a plurality of receiving points; performing P-wave time difference correction processing on the array signal data; Performing direction of arrival estimation processing on the observed array signal data and calculating a plurality of seismic wave signal incident angles; Obtaining an expected signal incident angle and an interference signal incident angle according to the size relationship between each seismic wave signal incident angle and a preset angle threshold value; Establishing a target constraint function based on a linearly constrained minimum variance criterion according to the expected signal incident angle and the interference signal incident angle; Calculating an optimal weighting vector based on the target constraint function and a seismic wave beam forming minimum output power formula; Processing the array signal data by using the optimal weighting vector to obtain a noise-suppressed seismic signal record; The step of obtaining an expected signal incident angle and an interference signal incident angle according to the size relationship between each seismic wave signal incident angle and a preset angle threshold value comprises the following steps: Calculating the difference between each seismic wave signal incident angle and the preset angle threshold value; The calculated difference values are sorted, and the seismic wave signal incidence angle corresponding to the minimum difference value is taken as the expected signal incidence angle , and the seismic wave signal incidence angles other than the seismic wave signal incidence angle corresponding to the minimum difference value are taken as interference signal incidence angles .

2. The method of claim 1, wherein, The step of performing direction of arrival estimation processing on the observed array signal data and calculating a plurality of seismic wave signal incident angles comprises the following steps: extracting time-domain sampling data with a time length of T from M array signal data m = 1, 2, …, M; t = 1, 2, …, T; Dividing the extracted time-domain sampling data with a time length of T into K sub-segments, and performing discrete Fourier transform on the time-domain sampling data of each sub-segment to obtain transform data of each sub-segment, which is represented as: ---(1) Where k = 1 ~ K, j = 1 ~ J, j represents the j-th sub-frequency band, k and j are both integers, f j Let X represent the j-th frequency. k (f j The frequency of the k-th sub-segment of the array signal is f. j Frequency domain data, X 1k (f j f represents the k-th sub-segment of the first array signal. j Frequency component, X Mk (f j f represents the k-th sub-segment of the M-th array signal. j Frequency components; The focusing matrix T(f) of each sub-band is calculated using the rotating signal subspace transformation algorithm. j ), represented as: ---（2） wherein and are respectively left and right singular vectors of is a focusing frequency, and the main frequency is selected as the focusing frequency, is an array manifold matrix composed of direction vectors , n = 1, 2, …, N, θ n represents the incident angle of the nth signal, is a direction vector, , d is the distance between adjacent arrays, and v is the seismic wave propagation speed; Calculating a data covariance matrix according to the focusing matrix of each sub-frequency segment and the transform data of each sub-segment, wherein the data covariance matrix is represented as: ---(3) wherein X H k (f j ) is the transpose of X k (f j ), T H (f j ) is the transpose of T j (f y ), R k is the data covariance matrix, X j (f j ) represents the frequency domain data of the kth sub-segment of the array signal with frequency f j , and T is the focusing matrix. performing eigen decomposition on the data covariance matrix to obtain noise subspace eigenvectors ; According to the noise subspace eigenvector The incidence angle of the seismic wave signal is obtained by solving a spectral estimation formula, and the spectral estimation formula of the rotated signal subspace transformation algorithm is represented as: ---(4) Wherein, α(f c α, θ) is the search direction vector, and α H (f c ,θ) is α(f c The transpose of θ, E N H for transpose, The main frequency is selected as the focusing frequency, and θ is the incident angle of the signal.

3. The method of claim 1, wherein, The target constraint function is: ---(5) The target constraint function requires directionally incident desired signals to pass through unaltered, directionally incident interfering signals to be blocked. Let , , then the target constraint function is simplified as: ---(6) Wherein W is the optimal weighting vector.

4. The method of claim 3, wherein, The step of calculating an optimal weighting vector based on the target constraint function and a seismic wave beam forming minimum output power formula comprises the following steps: Obtaining an optimal weighting vector expression by using a Lagrange multiplier when the target constraint function is satisfied and the weighting vector satisfies the minimum output power of the seismic wave beam forming, wherein the optimal weighting vector expression is: ---(7) wherein R is a covariance matrix of the array signal data, , ; The seismic wave beam forming minimum output power formula is: ---(8) Wherein y(t) is an expected output signal; Calculating the optimal weighting vector according to the optimal weighting vector expression.

5. The method of claim 4, wherein, The step of processing the array signal data by using the optimal weighting vector to obtain a noise-suppressed seismic signal record comprises the following steps: Obtaining a wideband linear array beam by multiplying the optimal weighting vector W and the array signal data to form a noise-suppressed seismic signal record, and the calculation formula is: ---(9) Wherein, y(f) is the frequency domain signal after denoising, W(f) is the optimal weight vector, W H (f) is the transpose of W(f), x m (f) is the frequency domain data of the mth array signal, m = 1, 2, …, M, wherein M represents the Mth array.

6. A seismic coherent noise suppression apparatus based on wideband linear array beamforming, characterized by, The method comprises the following steps: A time difference correction module is configured to obtain array signal data of microseisms by observing the ground through radial measuring lines, wherein each measuring line is a linear array, and each linear array comprises a plurality of receiving points; and the array signal data is subjected to P-wave time difference correction processing; An angle estimation module is configured to perform direction of arrival estimation processing on the observed array signal data and calculate a plurality of seismic wave signal incident angles; The expected signal angle obtaining module is used for obtaining the expected signal incident angle and the interference signal incident angle according to the size relationship between each of the seismic wave signal incident angles and the preset angle threshold value; the expected signal incident angle and the interference signal incident angle are obtained according to the size relationship between each of the seismic wave signal incident angles and the preset angle threshold value, including: calculating the difference between each of the seismic wave signal incident angles and the preset angle threshold value; sorting the calculated difference values, and taking the seismic wave signal incident angle corresponding to the minimum difference value as the expected signal incident angle , and taking the seismic wave signal incident angles other than the seismic wave signal incident angle corresponding to the minimum difference value as the interference signal incident angles . A target function establishing module is configured to establish a target constraint function based on a linearly constrained minimum variance criterion according to the expected signal incident angle and the interference signal incident angle; and A weight vector calculation module is configured to calculate an optimal weight vector based on the target constraint function and a seismic wave beamforming minimum output power formula; A noise suppression processing module is configured to process the array signal data using the optimal weight vector to obtain a noise-suppressed seismic signal record.

7. An electronic device, comprising: The array signal data is obtained by observing the ground through radial survey lines, each of which is a line array, and each line array includes multiple receiving points.

8. A storage medium, characterized by The memory stores a computer program, and the computer program is executed by the processor to perform the seismic coherent noise suppression method based on the wideband line array beamforming according to any one of claims 1 to 5. The storage medium stores a computer program, and the computer program can be executed by one or more processors to implement the seismic coherent noise suppression method based on the wideband line array beamforming according to any one of claims 1 to 5.

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