Seismic data fault enhancement method based on optimal local spatiotemporal vector

Abstract

The application provides a seismic data fault enhancement method based on an optimal local space-time vector, and relates to the technical field of seismic data processing.The method comprises the following steps: calculating and correcting the dip angle of seismic data events to obtain a corrected dip angle of the events; obtaining a fault enhancement local space-time vector based on the corrected dip angle of the events, and calculating a fault enhancement coefficient and a fault enhancement distance threshold; calculating the distance between a central reference waveform vector and the local space-time vector; extracting an optimal local space-time vector according to the distance threshold; and finally, weighting and stacking the seismic data by using the optimal local space-time vector to enhance the seismic fault features.The method effectively overcomes the defects of the existing reflection seismic data extraction and enhancement method, that is, the strong dependence on seismic fault information and the damage to the seismic fault, and can significantly enhance the discontinuous features of the strata, so that the fault plane is clearer and better serves the needs of oil and gas exploration and development.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic data processing, and particularly relates to a seismic data fault enhancement method based on an optimal local space-time vector. BACKGROUND

[0002] The fault geological structure of the underground influences the storage, sealing and migration of oil and gas resources, and is one of the key information for oil and gas exploration and development. Seismic data images the underground geological structure, including strata, anticlines, synclines and faults, by reflecting seismic waves, and is the most effective method for researching the fault information of the deep underground. Obtaining seismic data containing high-quality fault information plays an important role in the interpretation of the fault geological structure and the exploration and development of oil and gas.

[0003] Actual seismic data is inevitably disturbed in the field acquisition process, so a reflection data extraction method needs to be used in the seismic data processing process to obtain high-quality seismic data. The existing reflection seismic data extraction methods mainly include linear and nonlinear methods. The linear methods include F-X predictive filtering, singular value spectrum analysis and Radon transform methods, and this kind of method usually extracts the reflection seismic signal based on the transform function with linear signal expression capability. Since the fault information is spatially discontinuous information, this leads to the destruction of the fault information in the actual application of this kind of linear method. The nonlinear methods include median filtering and anisotropic diffusion filtering methods. The median filtering can be realized in two ways of scalar and vector, and it has a certain edge preservation, but since the median is used in the signal extraction process, the reflection data extraction result has local anomalies. The anisotropic diffusion filtering has the ability to enhance the fault, but needs to provide the fault information, which leads to the fact that the enhancement result is seriously dependent on the provided fault information, limiting the application of this kind of method. Therefore, it is important to develop a fault-enhanced seismic data processing method to serve the needs of oil and gas exploration and development. SUMMARY

[0004] Therefore, the present application provides a seismic data fault enhancement method based on an optimal local space-time vector, which can enhance the fault information in the seismic data and overcome the defects of the existing reflection seismic data extraction and enhancement methods that are too dependent on the seismic fault information and damage the seismic fault.

[0005] To achieve the above object, the present application adopts the following technical scheme:

[0006] A seismic data fault enhancement method based on an optimal local space-time vector, the method comprising:

[0007] calculating the dip correction weight of the dip angle of the seismic data event, and correcting the dip angle of the event to obtain the corrected dip angle of the event;

[0008] Based on the modified phase axis dip angle, obtain fault enhancement local space-time vector, and calculate fault enhancement coefficient and fault enhancement distance threshold value;

[0009] Calculate the distance between the central reference waveform vector and the local space-time vector;

[0010] According to the distance threshold value, extract the best local space-time vector matched with the central reference waveform vector;

[0011] Weighted stacking of seismic data using the best local space-time vector to enhance seismic faults.

[0012] Preferably,

[0013] Calculate the dip correction weight of the seismic data phase axis dip angle, and correct the phase axis dip angle to obtain the modified phase axis dip angle, including:

[0014] Based on the seismic data, construct a structure tensor matrix, and calculate the initial phase axis dip angle of the seismic data;

[0015] Extract local space-time vectors along the initial phase axis dip angle direction in the seismic data to obtain the local space-time vectors before correction; The local space-time vectors before correction have time direction length and space direction length;

[0016] In each spatial position of the local space-time vector before correction, the local space-time vector before correction is divided into two parts, and respectively stacked along the spatial direction to generate two waveform vectors corresponding to each spatial position;

[0017] Calculate the Pearson correlation coefficient and cosine similarity between the two waveform vectors corresponding to each spatial position of the local space-time vector before correction, and calculate the dip correction weight of the phase axis dip angle according to the combination of the two;

[0018] According to the dip correction weight, weight the Gaussian smoothing function to obtain the weighted Gaussian smoothing function;

[0019] Smooth the derivatives of the seismic data in the time direction and the spatial direction using the weighted Gaussian smoothing function, construct a new structure tensor based on the smoothed derivatives, and calculate the preliminary modified phase axis dip angle by performing eigenvalue decomposition on the new structure tensor;

[0020] Smooth the preliminary modified phase axis dip angle using the weighted Gaussian smoothing function to obtain the modified phase axis dip angle.

[0021] Preferably,

[0022] Based on the modified phase axis dip angle, obtain fault enhancement local space-time vector, and calculate fault enhancement coefficient and fault enhancement distance threshold value, including:

[0023] extracting local space-time vectors along the modified event dip direction from the seismic data to obtain fault-enhanced local space-time vectors; the fault-enhanced local space-time vectors have time direction length and space direction length;

[0024] dividing the fault-enhanced local space-time vectors into two parts at each space position of the fault-enhanced local space-time vectors, and respectively stacking along the space direction to generate two waveform vectors corresponding to each space position;

[0025] calculating the Pearson correlation coefficient and the cosine similarity between the two waveform vectors corresponding to each space position of the fault-enhanced local space-time vectors, and determining a fault-enhancement coefficient according to the calculation results;

[0026] setting a fault-enhancement distance coefficient, and calculating an adaptive fault-enhancement distance threshold according to the cosine similarity between the two waveform vectors corresponding to each space position of the fault-enhanced local space-time vectors and the fault-enhancement distance coefficient;

[0027] Preferably,

[0028] calculating the distance between the central reference waveform vector and the local space-time vector, comprising:

[0029] setting a region threshold, and extracting local space-time vectors from the seismic data along the modified event dip direction to obtain signal-enhanced local space-time vectors for space-time positions with fault-enhancement coefficients less than the region threshold; the signal-enhanced local space-time vectors have time direction length and space direction length;

[0030] calculating the distance between the signal-enhanced local space-time vector and the signal-enhanced reference waveform vector to obtain a signal-enhancement distance; and calculating the distance between the fault-enhanced local space-time vector and the fault-enhanced reference waveform vector to obtain a fault-enhancement distance;

[0031] The signal-enhanced reference waveform vector is a vector corresponding to the space center position in the signal-enhanced local space-time vector; the fault-enhanced reference waveform vector is a vector corresponding to the space center position in the fault-enhanced local space-time vector; the waveform vector refers to a vector corresponding to a certain specific space position in the local space-time vector, which only has time direction length;

[0032] performing maximum and minimum normalization processing on the signal-enhancement distance and the fault-enhancement distance to generate a normalized signal-enhancement distance and a normalized fault-enhancement distance.

[0033] Preferably,

[0034] According to the distance cutoff threshold, the best local space-time vector matching the center reference waveform vector is extracted, including:

[0035] The signal enhancement distance is normalized, a signal enhancement distance cutoff threshold is set, and a signal enhancement weight is determined according to the signal enhancement distance cutoff threshold;

[0036] According to the fault enhancement distance cutoff threshold, a fault enhancement weight is determined for the normalized fault enhancement distance;

[0037] According to the signal enhancement weight and the fault enhancement weight, the signal enhancement local space-time vector and the fault enhancement local space-time vector are weighted, and the best fault enhancement local space-time vector and the best signal enhancement local space-time vector matching the center reference waveform vector are extracted.

[0038] Preferably,

[0039] The seismic data is weighted and stacked using the best local space-time vector to enhance the seismic fault, including:

[0040] At the space-time position where the fault enhancement coefficient is greater than the regional threshold, the best fault enhancement local space-time vector is weighted and stacked along the spatial direction using a one-dimensional Gaussian smoothing function to obtain a fault enhancement waveform vector;

[0041] At the space-time position where the fault enhancement coefficient is less than the regional threshold, the best signal enhancement local space-time vector is weighted and stacked along the spatial direction using a one-dimensional Gaussian smoothing function to obtain a signal enhancement waveform vector;

[0042] The fault enhancement waveform vector and the signal enhancement waveform vector are combined to realize fault enhancement of the seismic data. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0044] Figure 1 The flowchart of the method for fault enhancement of seismic data based on the best local space-time vector provided by the embodiments of the present application is shown;

[0045] Figure 2 The seismic data provided by the embodiments of the present application is shown;

[0046] Figure 3 The corresponding Figure 2The seismic data fault enhancement method based on the optimal local space-time vector of the present invention is used to enhance the seismic data fault. DETAILED DESCRIPTION

[0047] To help those skilled in the art better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other implementations obtained by those skilled in the art without creative work are within the scope of protection of the present invention. Example

[0048] Figure 1 This is a flow chart provided by an embodiment of the seismic data fault enhancement method based on the optimal local space-time vector of the present invention. Figure 1 As shown, the method includes the following steps:

[0049] Step S1, calculating the dip correction weight of the event dip of the seismic data, and correcting the event dip to obtain a corrected event dip;

[0050] Step S2: based on the corrected event dip, obtain the fault enhancement local space-time vector, and calculate the fault enhancement coefficient and the fault enhancement distance cutoff threshold;

[0051] Step S3, calculating the distance between the central reference waveform vector and the local space-time vector;

[0052] Step S4: extracting the best local spatiotemporal vector that matches the central reference waveform vector according to the distance cutoff threshold;

[0053] Step S5: weighted stacking of seismic data using the optimal local space-time vector to enhance seismic faults.

[0054] Step S1 is specifically as follows:

[0055] The structural tensor matrix is ​​constructed based on the seismic data, and the initial event dip of the seismic data is calculated. Specifically, the seismic data has a sampling time dimension and a spatial distance dimension, such as Figure 2 As shown, the coordinate axis t represents the sampling time in seconds (s), the coordinate axis x represents the spatial distance in meters (m), and the seismic data can be expressed as: d(t,x); a structural tensor matrix is ​​constructed for the seismic data, and eigenvalue decomposition is performed to obtain the initial event axis dip angle θ1(t,x) of the seismic data.

[0056] Set the half-length of the local window in the time direction to t h1 (t h1 >0), the half length in the space direction is x h1 (xh1 At all space-time position points (t, x) in the seismic data d(t, x), a local window data is extracted along the direction of the initial dip angle θ1(t, x) of the initial event to obtain a pre-modified local space-time vector. The pre-modified local space-time vector is expressed as follows:

[0057] d1(t1, x1) = d(t + tan(θ1)x1, x + x1) (1)

[0058] wherein -t h1 ≤ t1≤ t h1 , -x h1 ≤ x1≤ x h1 ; the pre-modified local space-time vector has a length of 2t h1 in the time direction and a length of 2x1in the space direction.

[0059] At each space position x′1of the pre-modified local space-time vector, the pre-modified local space-time vector is divided into two parts, and superimposed along the space direction respectively to generate two waveform vectors R a1 (x′1) and R b1 (x′1) corresponding to each space position. The two waveform vectors R a1 (x′1) and R b1 (x′1) corresponding to each space position can be expressed as follows:

[0060]

[0061] wherein -x h1 ≤ x′1≤ x h1 .

[0062] The Pearson correlation coefficient P ab1 (x′1) and the cosine similarity S ab1 (x′1) between the two waveform vectors corresponding to each space position of the pre-modified local space-time vector are calculated, and the dip angle correction weight of the event dip angle is calculated according to the combination of the two. The dip angle correction weight of the event dip angle is expressed as:

[0063]

[0064] wherein t and x represent the position of the center of the pre-modified local space-time vector in the seismic data, x h1 is half of the space length of the pre-modified local space-time vector, x ang1 (0 < x ang1 < x h1 ) is a preset space length, S1is a similarity threshold, and x′1(0 < x′1 < 2x h1) is the space position division point dividing the local space-time vector before correction into two parts, x' 1min = argmin(P ab1 ).

[0065] According to the inclination correction weight, the Gaussian smoothing function is weighted to obtain a weighted Gaussian smoothing function I s (m,n). The expression of the weighted Gaussian smoothing function is:

[0066] I s (m,n) = Angw(t+m,x+n)I(m,n) (5)

[0067] Wherein, I(m,n) is a two-dimensional Gaussian smoothing function, and the formula is as follows:

[0068]

[0069] The derivatives and of the seismic data in the time direction and the spatial direction are smoothed by using the weighted Gaussian smoothing function, a new structure tensor is constructed based on the smoothed derivatives, and the preliminary corrected dip of the event is calculated by performing eigenvalue decomposition on the new structure tensor.

[0070] Specifically, the derivative of the seismic data in the time direction is d t , and the derivative in the spatial direction is d x , the derivatives d t d x , d t d t , d x d x are smoothed by using the modified Gaussian smoothing function, and the following results are obtained:

[0071]

[0072] Wherein, m a (m a > 0) and n a (n a > 0) are the range size of the weighted Gaussian smoothing function selected for smoothing the derivatives of the seismic data, C2 is an integral normalization coefficient of the smoothing weight,

[0073] A new structure tensor J1(t,x) is constructed through <d t d t > s , <d t d x > s , <d x d x > s The new structure tensor can be expressed as:

[0074]

[0075] Eigenvalue decomposition is performed on the new structure tensor to extract the maximum eigenvalue λ1 and the minimum eigenvalue λ2, and finally the preliminary corrected event dip angle θ2(t, x) is calculated. The formula for calculating the preliminary corrected event dip angle is as follows:

[0076]

[0077] The preliminary corrected event dip angle is smoothed by using the weighted Gaussian smoothing function to obtain a corrected event dip angle. The formula for the corrected event dip angle is as follows:

[0078]

[0079] wherein m b (m b > 0) and n b (n b > 0) are the range sizes of the weighted Gaussian smoothing function selected for obtaining the corrected event dip angle, and C3 is an integral normalization coefficient of the smoothing weight,

[0080] Step S2 is specifically:

[0081] The half length of the local window in the time direction is set to t h2 (t h2 > 0), and the half length in the spatial direction is set to x h2 (x h2 > 0), and at all spatiotemporal position points (t, x) in the seismic data d(t, x), a local spatiotemporal vector is extracted along the corrected event dip angle θ3(t, x) direction to obtain a fault-enhanced local spatiotemporal vector. The fault-enhanced local spatiotemporal vector can be expressed as:

[0082] d2(t2, x2) = d(t + tan(θ3)x2, x + x2) (11)

[0083] wherein -t h2 ≤ t2 ≤ t h2 , -x h2 ≤ x2 ≤ x h2 ; the fault-enhanced local spatiotemporal vector has a length of 2t h2 in the time direction and a length of 2x2 in the spatial direction.

[0084] At each spatial position x'2 of the fault-enhanced local spatiotemporal vector, the fault-enhanced local spatiotemporal vector is divided into two parts, and each part is superimposed along the spatial direction to generate two waveform vectors R corresponding to each spatial position.a2 (x'2) and R b2 (x'2). Wherein, the two waveform vectors R a2 (x'2) and R b2 (x'2) are calculated as follows:

[0085]

[0086] wherein, -x h2 <x′2<x h2 .

[0087] The two waveform vectors R a2 (x'2) and R b2 (x'2) are calculated according to the spatial position corresponding to each spatial position of the fault-enhanced local space-time vector. ab2 (x'2) and the cosine similarity S ab2 (x'2), and determines the fault-enhanced coefficient according to the calculation result. The formula for determining the fault-enhanced coefficient is as follows:

[0088]

[0089] wherein, t and x represent the position of the center of the fault-enhanced local space-time vector in the seismic data, x h2 is half of the spatial length of the fault-enhanced local space-time vector, x ang2 (0<x ang2 <x h2 ) is a preset spatial length, S2 is a similarity threshold, x'2(0<x'2<2x h2 ) is a spatial position division point for dividing the fault-enhanced local space-time vector into two parts, x'2 2min = argmin(P ab2 ).

[0090] A fault-enhanced distance coefficient σ1 is set, which affects the selection of the optimal local space-time vector. According to the calculation of the cosine similarity S ab2 (x'2) between the two waveform vectors corresponding to each spatial position of the fault-enhanced local space-time vector and the fault-enhanced distance coefficient σ1, an adaptive fault-enhanced distance threshold σ2(t, x) is calculated, and the expression is as follows:

[0091]

[0092] wherein, L0 = σ1·(max(1-S ab2 )) 0.5 , 0≤λ≤1.

[0093] Step S3 is specifically:

[0094] The preset area threshold is 0.5, and for the space-time position with the fault enhancement coefficient less than the area threshold, a local window is set with a half length t h3 (t h3 >0) in the time direction and a half length x h3 (x h3 >0) in the space direction, and a local space-time vector is extracted from the seismic data along the modified in-phase axis inclination direction to obtain a signal enhancement local space-time vector d3(t3, x3). The signal enhancement local space-time vector is expressed as:

[0095] d3(t3, x3) = d(t + tan(θ3)x3, x + x3) (16)

[0096] wherein -t h3 ≤t3≤t h3 , -x h3 ≤x3≤x h3 . The signal enhancement local space-time vector has a length of 2t h3 in the time direction and a length of 2x3 in the space direction.

[0097] The distance between the signal enhancement local space-time vector d3(t3, x3) and a signal enhancement reference waveform vector d3(t3, 0) is calculated to obtain a signal enhancement distance D3(x3), and the distance between the fault enhancement local space-time vector d2(t2, x2) and a fault enhancement reference waveform vector d2(t2, 0) is calculated to obtain a fault enhancement distance D2(x2). The two distance expressions are as follows:

[0098]

[0099] It should be noted that the signal enhancement reference waveform vector is a vector corresponding to the spatial center position in the signal enhancement local space-time vector, the fault enhancement reference waveform vector is a vector corresponding to the spatial center position in the fault enhancement local space-time vector, and the waveform vector refers to a vector corresponding to a certain specific spatial position in the local space-time vector, which only has a length in the time direction.

[0100] The maximum and minimum normalization processing is performed on the signal enhancement distance D3(x3) and the fault enhancement distance D2(x2) to generate a normalized signal enhancement distance D n3 (x3) and a normalized fault enhancement distance D n2 (x2). The two normalized distance expressions are as follows:

[0101]

[0102] wherein ε is a very small positive number to prevent the denominator from being 0, and D n2 and Dn3 The value of D is between 0 and 1.

[0103] The step S4 is specifically:

[0104] The signal enhancement distance is normalized, a signal enhancement distance threshold σ3 is set, and a signal enhancement weight is determined according to the signal enhancement distance threshold. The signal enhancement weight W3(x3) can be expressed as:

[0105]

[0106] The fault enhancement distance is normalized, and a fault enhancement weight is determined according to the fault enhancement distance threshold σ2(t,x). The expression for determining the fault enhancement weight is:

[0107]

[0108] The weight sum of the signal enhancement local space-time vector d3(t3,x3) is The minimum weight sum z is set, and if I3<z, the normalized distance D n3 is arranged in ascending order, the corresponding signal enhancement local space-time vector in the front local space [0,z] is selected as the best local space-time vector, and the weight is set to 1.

[0109] It should be noted that W2(x2) and W3(x3) only have values of 1 and 0. According to the signal enhancement weight W3(x3) and the fault enhancement weight W2(x2), the signal enhancement local space-time vector and the fault enhancement local space-time vector are multiplied, respectively, to extract the best fault enhancement local space-time vector and the best signal enhancement local space-time vector that match the respective center reference waveform vector.

[0110] The step S5 is specifically:

[0111] At the space-time position where the fault enhancement coefficient FP(t,x) is greater than the region threshold, a one-dimensional Gaussian smoothing function is used to weight and superimpose the best fault enhancement local space-time vector along the spatial direction to obtain a fault enhancement waveform vector. The calculation method of the fault enhancement waveform vector can be expressed as:

[0112]

[0113] where G(x) is a one-dimensional Gaussian function, W2(x) is a fault enhancement weight, d2(t2,x2) is a fault enhancement local space-time vector, W2(x2)d2(t2,x2) is a best fault enhancement local space-time vector, and the one-dimensional Gaussian function formula is as follows:

[0114]

[0115] wherein p is a scale of the one-dimensional Gaussian function.

[0116] At the space-time position where the fault enhancement coefficient FP(t,x) is less than the region threshold, the optimal signal enhancement local space-time vector is weighted and superimposed along the spatial direction by using a one-dimensional Gaussian smoothing function to obtain a signal enhancement waveform vector.

[0117]

[0118] wherein G(x) is a one-dimensional Gaussian function, W3(x) is a signal enhancement weight, d3(t3,x3) is a signal enhancement local space-time vector, and W3(x3)d3(t3,x3) is an optimal signal enhancement local space-time vector.

[0119] Finally, the fault enhancement waveform vectors obtained at all space-time positions where the fault enhancement coefficient FP(t,x) is greater than the region threshold and the signal enhancement waveform vectors obtained at all space-time positions where the fault enhancement coefficient FP(t,x) is less than the region threshold are combined to realize fault enhancement of the seismic data.

[0120] The above technical scheme is adopted in the present application, and a seismic data fault enhancement method based on an optimal local space-time vector comprises the following steps: calculating a dip angle correction weight of a seismic data event dip angle and correcting the event dip angle to obtain a corrected event dip angle; obtaining a fault enhancement local space-time vector based on the corrected event dip angle and calculating a fault enhancement coefficient and a fault enhancement distance threshold; calculating a distance between a center reference waveform vector and the local space-time vector; extracting an optimal local space-time vector matched with the center reference waveform vector according to the distance threshold; and weighting and superimposing the seismic data by using the optimal local space-time vector to enhance a seismic fault. Based on this, the seismic data fault enhancement method based on the optimal local space-time vector is introduced, the defects that the existing reflection seismic data extraction and enhancement method is too dependent on seismic fault information and damages the seismic fault are effectively overcome, the discontinuous characteristics of a stratum can be significantly enhanced, the fault plane is clearer, and the needs of oil and gas exploration and development are met.

[0121] In specific embodiments, Figure 2 is seismic data not processed by the method of the present application, Figure 3 is seismic data processed by the method of the present application, Figure 2 is the result of fault enhancement of the seismic data by using the seismic data fault enhancement method based on the optimal local space-time vector of the present application. Figure 2 and Figure 3 It can be found that, Figure 2 the seismic fault characteristics in are relatively fuzzy; after the method of the present application is applied, the seismic fault characteristics are significantly enhanced, the fault plane is clearer, and presents a direct and linear structure, which is in sharp contrast with the surrounding continuous stratum.

Claims

1. A method for fault enhancement of seismic data based on optimal local space-time vectors, characterized in that: include: Calculating the dip correction weight of the event dip of the seismic data, and correcting the event dip to obtain a corrected event dip; Based on the corrected event dip, the fault enhancement local space-time vector is obtained, and the fault enhancement coefficient and the fault enhancement distance cutoff threshold are calculated. Before obtaining the fault enhancement local space-time vector, the half-length of the local window in the time direction is set to t h1 , the half length in the spatial direction is x h1 , extract local window data from all time-space positions (t, x) in the seismic data d(t, x) along the initial event dip angle θ1(t, x) to obtain the local time-space vector before correction; Calculating the distance between the central reference waveform vector and the local space-time vector; According to the distance cutoff threshold, the best local spatiotemporal vector matching the central reference waveform vector is extracted; Weighted stacking of seismic data using optimal local space-time vectors to enhance earthquake faults; Based on the corrected event dip, a fault enhancement local space-time vector is obtained, and a fault enhancement coefficient and a fault enhancement distance cutoff threshold are calculated, including: extracting a local space-time vector along the corrected event dip direction in the seismic data to obtain a fault enhancement local space-time vector; the fault enhancement local space-time vector has a time direction length and a space direction length; At each spatial position of the fault-enhanced local space-time vector, the fault-enhanced local space-time vector is divided into two parts, and the two parts are superimposed along the spatial direction to generate two waveform vectors corresponding to each spatial position; Calculate the Pearson correlation coefficient P between the two waveform vectors corresponding to each spatial position of the fault enhancement local space-time vector ab2 (x2′) and cosine similarity S ab2 (x′2), and determine the fault enhancement coefficient FP(t,x) based on the calculation results, Among them, x h2 is half the spatial length of the fault enhancement local space-time vector, x ang2 is the preset spatial length, S2 is the similarity threshold, x′2 is the spatial position dividing point for dividing the fault enhancement local space-time vector into two parts, and x′ 2min =argmin(P ab2 ); Setting a fault enhancement distance coefficient, calculating an adaptive fault enhancement distance cutoff threshold σ2(t,x) based on the cosine similarity between the two waveform vectors corresponding to each spatial position of the fault enhancement local space-time vector and the fault enhancement distance coefficient, Where L0=σ1·(max(1-S ab2 )) 0.5 , σ1 is the fault enhancement distance coefficient, η is the weight coefficient, 0≤η≤1.

2. The method according to claim 1, characterized in that Calculate the dip correction weight of the event dip of the seismic data, and correct the event dip to obtain the corrected event dip, including: Construct a structural tensor matrix based on seismic data and calculate the initial event dip of seismic data; Extracting a local space-time vector along the initial event dip direction from the seismic data to obtain a local space-time vector before correction; the local space-time vector before correction has a length in the time direction and a length in the space direction; At each spatial position of the local space-time vector before correction, the local space-time vector before correction is divided into two parts, and the two parts are superimposed along the spatial direction to generate two waveform vectors corresponding to each spatial position; Calculating the Pearson correlation coefficient and cosine similarity between two waveform vectors corresponding to each spatial position of the local space-time vector before correction, and calculating the tilt correction weight of the event tilt according to a combination of the two; weighting the Gaussian smoothing function according to the tilt correction weight to obtain a weighted Gaussian smoothing function; Using the weighted Gaussian smoothing function to smooth the derivatives of the seismic data in the time direction and the space direction, constructing a new structural tensor based on the smoothed derivatives, and calculating a preliminary revised event dip by performing eigenvalue decomposition on the new structural tensor; The weighted Gaussian smoothing function is used to smooth the preliminary corrected event dip to obtain a corrected event dip.

3. The method according to claim 1, characterized in that Calculate the distance between the central reference waveform vector and the local space-time vector, including: Preset a regional threshold, and for a spatiotemporal position where the fault enhancement coefficient is less than the regional threshold, extract a local spatiotemporal vector from the seismic data along the direction of the corrected event dip to obtain a signal-enhanced local spatiotemporal vector; the signal-enhanced local spatiotemporal vector has a length in a time direction and a length in a space direction; Calculating the distance between the signal enhancement local space-time vector and the signal enhancement reference waveform vector to obtain the signal enhancement distance; calculating the distance between the fault enhancement local space-time vector and the fault enhancement reference waveform vector to obtain the fault enhancement distance; The signal enhancement reference waveform vector is a vector corresponding to the spatial center position within the signal enhancement local space-time vector; the fault enhancement reference waveform vector is a vector corresponding to the spatial center position within the fault enhancement local space-time vector; the waveform vector refers to a vector corresponding to a specific spatial position in the local space-time vector and has only a length in the time direction; Normalization processing of the maximum value and the minimum value of the signal enhancement distance and the fault enhancement distance is performed to generate a normalized signal enhancement distance and a normalized fault enhancement distance.

4. The method according to claim 3, characterized in that According to the distance cutoff threshold, the best local spatiotemporal vector matching the central reference waveform vector is extracted, including: For the normalized signal enhancement distance, setting a signal enhancement distance cutoff threshold, and determining a signal enhancement weight according to the signal enhancement distance cutoff threshold; For the normalized fault enhancement distance, determining a fault enhancement weight according to the fault enhancement distance cutoff threshold; The signal enhancement local space-time vector and the fault enhancement local space-time vector are weighted according to the signal enhancement weight and the fault enhancement weight, and the optimal fault enhancement local space-time vector and the optimal signal enhancement local space-time vector matching the central reference waveform vector are extracted.

5. The method according to claim 4, characterized in that Weighted stacking of seismic data using optimal local space-time vectors to enhance earthquake faults, including: At the spatiotemporal position where the fault enhancement coefficient is greater than the regional threshold, a one-dimensional Gaussian smoothing function is used to perform weighted superposition along the spatial direction on the optimal fault enhancement local spatiotemporal vector to obtain a fault enhancement waveform vector; At the spatiotemporal position where the fault enhancement coefficient is less than the regional threshold, a one-dimensional Gaussian smoothing function is used to perform weighted superposition along the spatial direction on the optimal signal enhancement local spatiotemporal vector to obtain a signal enhancement waveform vector; The fault enhancement waveform vector and the signal enhancement waveform vector are combined to achieve fault enhancement of seismic data.

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