Earthquake fracture identification method and device, electronic equipment and storage medium

Abstract

The invention provides an earthquake fracture identification method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining earthquake data, carrying out the coherent calculation of the earthquake data, obtaining an earthquake coherent data body, and determining fracture data based on the earthquake coherent data body; wherein the fracture data comprises a fracture direction and a fracture inclination angle; dividing the seismic coherence data volume into a plurality of seismic coherence sub-data segments based on the change amplitude of the fracture inclination angle; based on the fracture trend, sampling each seismic coherence sub-data segment to obtain sampling data; filtering processing is carried out on the sampling data along fracture guidance to obtain filtered data, and coherent calculation is carried out on the filtered data to obtain a new seismic coherent data volume; the new seismic coherent data volume is used to identify seismic fracture features. According to the method, the spatial development law of the boundary of the geologic body to be identified is fully considered, and more accurate identification is realized for the phenomena that the breakpoint is unclear, the continuity of stratums on two sides of the breakpoint is relatively good, the occurrence is basically consistent and the like.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to a method, apparatus, electronic device, and storage medium for identifying seismic faults. Background Technology

[0002] Accurate fault identification plays a crucial role in the overall understanding of fault systems and hydrocarbon accumulation patterns. Faults can cause changes in the amplitude, phase, and polarity of seismic reflected waves, which manifest as low or high discontinuities in the phase axis on seismic profiles. In seismic data interpretation, coherence properties are an important indicator for detecting discontinuities in geological bodies, and seismic coherence analysis is a commonly used method for identifying faults in seismic data.

[0003] Currently, most seismic coherence analysis techniques aim to accurately identify faults by improving seismic coherence calculation methods and enhancing the quality of seismic data. However, most existing coherence techniques only emphasize the detection of discontinuities in the seismic data itself, without fully considering the spatial development patterns of the boundaries of the geological bodies to be identified. This makes it difficult to accurately identify faults in cases where the fault point is unclear, or where the strata on both sides of the fault point have good continuity and essentially the same attitude. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, electronic device, and storage medium for earthquake fracture identification to address the aforementioned technical problems.

[0005] A method for identifying earthquake faults, comprising:

[0006] Seismic data is acquired, coherence calculations are performed on the seismic data to obtain a seismic coherence data volume, and fault data is determined based on the seismic coherence data volume; wherein, the fault data includes fault strike and fault dip.

[0007] Extract the fracture strike and fracture dip angle from the fracture data to obtain the fracture strike data point set and the fracture dip angle data point set;

[0008] Based on the set of fault dip angle data points, the variation range of the fault dip angle is determined, and the seismic coherence data volume is divided into multiple seismic coherence sub-data segments according to the variation range.

[0009] Based on the fault strike data point set, each of the seismic coherence sub-data segments is sampled to obtain sampled data;

[0010] The sampled data is filtered along the fracture guide to obtain filtered data, wherein the fracture guide is a direction parallel to the fracture direction;

[0011] The filtered data is subjected to coherent calculations to obtain a new seismic coherent data volume;

[0012] The new seismic coherence data volume is used to identify seismic fault features.

[0013] In one embodiment, the step of filtering the sampled data along the fracture guide to obtain filtered data includes:

[0014] The sampled data is filtered along the fracture guide, while the seismic data volumes in other directions remain unchanged, to obtain the filtered data.

[0015] In one embodiment, the step of dividing the seismic coherence data volume into multiple seismic coherence sub-data segments according to the change amplitude includes:

[0016] The tilt angle scanning step size is determined based on the aforementioned change range.

[0017] Based on the change amplitude and tilt scan step size, determine the number of segments to be divided into the seismic coherence sub-data segments;

[0018] The seismic coherence data volume is divided based on the number of segments to obtain multiple seismic coherence sub-segments.

[0019] In one embodiment, the step of determining the tilt scan step size based on the change amplitude includes:

[0020] Detect whether the change amplitude is greater than or equal to a preset change threshold;

[0021] When the change amplitude is greater than or equal to a preset change threshold, the tilt angle scanning step size is determined as the first scanning step size; otherwise, the tilt angle scanning step size is determined as the second scanning step size, wherein the first scanning step size is greater than the second scanning step size.

[0022] In one embodiment, in the step of determining the number of segments for the seismic coherence sub-data segment based on the change amplitude and the preset dip angle scanning step size, the calculation formula for the number of segments is as follows:

[0023]

[0024] Where m is the number of segments, Δθ is the change range, and ω is the preset tilt angle scanning step size.

[0025] In one embodiment, after the step of identifying seismic fault features using the new seismic coherence data volume, the method further includes:

[0026] Determine the fracture identification accuracy of the new seismic coherence data volume;

[0027] If the fracture identification accuracy is less than the preset accuracy threshold, the tilt scan step size is reduced to obtain a third scan step size. Based on the change amplitude and the third tilt scan step size, the number of segments is re-determined, resampling and re-filtering are performed until the re-determined fracture identification accuracy is greater than or equal to the preset accuracy threshold.

[0028] In one embodiment, the step of sampling each of the seismic coherence sub-data segments based on the fault strike data point set includes:

[0029] Based on the fault strike data point set, each of the seismic coherence sub-data segments is sampled within a preset distance on both sides of the fault, wherein the preset distance is calculated as follows:

[0030] l = f × k,

[0031] Where l is the preset distance, f is the earthquake sampling frequency, and k is a preset empirical value.

[0032] An earthquake fault identification device, comprising:

[0033] The acquisition module is used to acquire seismic data, perform coherence calculations on the seismic data to obtain a seismic coherence data volume, and determine fault data based on the seismic coherence data volume; wherein, the fault data includes fault strike and fault dip.

[0034] The extraction module is used to extract the fracture strike and fracture dip angle from the fracture data to obtain the fracture strike data point set and the fracture dip angle data point set.

[0035] The partitioning module is used to determine the variation range of the fault dip angle based on the fault dip angle data point set, and to partition the seismic coherence data volume into multiple seismic coherence sub-data segments according to the variation range.

[0036] The sampling module is used to sample each of the seismic coherence sub-data segments based on the fault strike data point set to obtain sampled data;

[0037] A filtering module is used to filter the sampled data along the fracture guide to obtain filtered data, wherein the fracture guide is a direction parallel to the fracture direction;

[0038] The calculation module is used to perform coherent calculations on the filtered data to obtain a new seismic coherent data volume;

[0039] The identification module is used to identify seismic fault features using the new seismic coherence data volume.

[0040] In one embodiment, the filtering module includes:

[0041] A filtering unit is used to filter the sampled data along the fracture guide, while keeping the seismic data volume unchanged in other directions, to obtain the filtered data.

[0042] In one embodiment, the partitioning module includes:

[0043] The step size determination unit is used to determine the tilt angle scanning step size based on the change amplitude.

[0044] The segment number determination unit is used to determine the number of segments to be divided into the seismic coherence sub-data segments based on the change amplitude and the dip angle scanning step size;

[0045] A partitioning unit is used to partition the seismic coherence data volume based on the number of partitioning segments to obtain multiple seismic coherence sub-data segments.

[0046] In one embodiment, the step size determination unit includes:

[0047] A detection subunit is used to detect whether the change amplitude is greater than or equal to a preset change threshold.

[0048] A determining subunit is configured to determine the tilt angle scanning step size as a first scanning step size when the change amplitude is greater than or equal to a preset change threshold, and conversely, determine the tilt angle scanning step size as a second scanning step size when the change amplitude is less than or equal to a preset change threshold. In one embodiment, the device further includes:

[0049] The accuracy determination module is used to determine the fracture identification accuracy of the new seismic coherence data volume.

[0050] The step size adjustment module is used to reduce the dip angle scanning step size to obtain a third scanning step size when the accuracy determination module determines the fracture identification accuracy of the new seismic coherence data volume. If the fracture identification accuracy is less than a preset accuracy threshold, the module then determines the number of segments, resamples, and refilters based on the change amplitude and the third dip angle scanning step size, until the re-determined fracture identification accuracy is greater than or equal to the preset accuracy threshold.

[0051] In one embodiment, the sampling module includes:

[0052] A sampling unit is used to sample each of the seismic coherence sub-data segments within a preset distance on both sides of the fault, based on the fault strike data point set. The preset distance is calculated as follows:

[0053] l = f × k,

[0054] Where l is the preset distance, f is the earthquake sampling frequency, and k is a preset empirical value.

[0055] An electronic device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the earthquake fault identification method described in any of the above embodiments.

[0056] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the earthquake fault identification method described in any of the above embodiments.

[0057] The aforementioned methods, devices, electronic equipment, and storage media for seismic fault identification, through coherent computation to obtain seismic coherent data volumes, can improve the quality and clarity of seismic data, thereby more accurately reflecting subsurface structural characteristics and providing a reliable data foundation for subsequent fault analysis. Determining fault data based on seismic coherent data volumes helps to quickly identify the location and distribution of subsurface faults. Extracting fault strike and dip angle data helps to better understand the geometric morphology and structural properties of faults, providing important parameters for three-dimensional simulation and structural interpretation of faults. Based on the fault dip angle data point set, the seismic coherent data volume is divided into multiple seismic coherent sub-data segments, dividing the fault structure into smaller regions, thus enabling more refined analysis of the characteristics and distribution patterns of subsurface faults. Based on the fault strike data point set, sampling each seismic coherent sub-data segment can effectively extract key fault information, helping to more intuitively present the geometric morphology and spatial distribution characteristics of subsurface faults. This series of steps fully considers the spatial development patterns of the boundaries of the geological bodies to be identified. For faults with unclear fault points, good continuity of strata on both sides of the fault point, and generally consistent attitudes, it can identify not only faults oblique to the strike of the strata but also faults parallel to the strike. Subsequently, filtering the sampled data along the fault guideline enhances the continuity of the seismic wave reflection phase axes on both sides of the fault, improves the signal-to-noise ratio of the seismic data, and reduces coherence noise, thus more effectively highlighting the characteristics of the fault zone and helping to more accurately interpret the distribution and properties of the fault. Coherence calculations are performed on the filtered data to obtain a new seismic coherence data volume, which further emphasizes the continuity and geometry of the fault zone, making the fault clearer and more prominent in the coherence volume. Finally, the new seismic coherence data volume is used to identify seismic fault features, effectively improving the accuracy and reliability of fault identification. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating a seismic fault identification method in one embodiment;

[0059] Figure 2 This is another flowchart illustrating the earthquake fault identification method in one embodiment;

[0060] Figure 3This is a schematic diagram of 3D seismic data after median filtering and denoising processing in one embodiment.

[0061] Figure 4 A 3D seismic coherence map generated based on a 3D seismic coherence data volume in one embodiment;

[0062] Figure 5 This is a schematic diagram of the sampling method for each seismic coherence sub-data segment in one embodiment;

[0063] Figure 6 This is a schematic diagram of a 3D seismic coherence data volume after filtering on a fracture guide in one embodiment.

[0064] Figure 7 This is a schematic diagram of a new seismic coherence data volume obtained after secondary coherence calculation in one embodiment;

[0065] Figure 8 This is a structural block diagram of a seismic fault data processing device in one embodiment;

[0066] Figure 9 This is a diagram of the internal structure of an electronic device in one embodiment. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0068] Example 1

[0069] In this application, as Figure 1 As shown, a method for identifying earthquake faults is provided, including:

[0070] Step 110: Acquire seismic data, perform coherence calculation on the seismic data to obtain seismic coherence data volume, and determine fault data based on the seismic coherence data volume; wherein, the fault data includes fault strike and fault dip angle;

[0071] In this embodiment, the seismic data can be initial 3D seismic data, which can be acquired through seismic observation or seismic recording instruments. Initial 3D seismic data typically includes information such as the amplitude, frequency, and time history of seismic waves, recording the process of seismic events propagating within the Earth. By analyzing and processing initial 3D seismic data, information such as underground geological structures, lithological distribution, and fault conditions can be inferred, providing important basis for geological exploration and resource development.

[0072] In seismic exploration, seismic data often contains a significant amount of noise and interference. By extracting seismic signals and suppressing noise through coherence calculations, a seismic coherence data volume is obtained, thereby enhancing the quality and clarity of the seismic data and facilitating more accurate analysis of geological structures. The seismic coherence data volume provides information about subsurface geological structures, including the potential existence of subsurface faults. By analyzing the seismic coherence data volume, subsurface fault data can be identified.

[0073] In one embodiment, the step of performing coherence calculations on the seismic data to obtain a seismic coherence data volume includes:

[0074] The seismic data were denoised using median filtering.

[0075] Coherence calculations are performed on the denoised seismic data to obtain the seismic coherence data volume.

[0076] In this embodiment, the preset median filtering time window length is n. Taking the i-th point of the seismic data as the center, n samples are taken. These n samples are then reordered according to their size. The sample value at the center of the reordered n data points is taken as the filtered output value for that point, thus obtaining the denoised seismic data. Median filtering effectively removes local noise without significantly affecting the signal characteristics, enhancing the reliability of the denoised seismic data and making it more suitable for subsequent coherence calculations and geological analysis.

[0077] Subsequently, coherence calculations are performed on the denoised 3D seismic data to obtain the 3D seismic coherence data volume. In this embodiment, the third-generation eigenvalue analysis algorithm is used to perform coherence calculations on the denoised seismic data to obtain the seismic coherence data volume.

[0078] The third-generation eigenvalue analysis algorithm calculates the correlation of seismic data using the eigenvalues ​​of the covariance matrix, highlighting incoherent seismic data. The specific formula for calculating the correlation parameter C or cross-correlation coefficient C is as follows:

[0079]

[0080] Where, λ max λ is the largest eigenvalue of the covariance matrix. j Let be the j-th eigenvalue of the covariance matrix.

[0081] Based on the calculated cross-correlation coefficient C, the denoised seismic data are screened or weighted to extract the seismic data volume with high coherence, and this high-coherence seismic data volume is taken as the seismic coherence data volume.

[0082] In this embodiment, the third-generation eigenvalue analysis algorithm is used to highlight the correlation between seismic data by analyzing the eigenvalues ​​of the covariance matrix. This effectively identifies seismic signals related to the research object and reduces interference from unrelated signals. At the same time, by selecting the main eigenvalues ​​and corresponding eigenvectors of the covariance matrix, dimensionality reduction of the seismic data can be achieved, simplifying the complexity of data analysis and improving computational efficiency. This helps to analyze seismic events, underground structures, etc. more accurately, and thus make more accurate predictions and assessments.

[0083] In one embodiment, the step of determining the fracture data based on the seismic coherence data volume includes:

[0084] The fractures are initially identified based on the similarity of seismic signals between adjacent seismic traces in the seismic coherence data volume.

[0085] In this embodiment, after acquiring the seismic coherence data volume, specialized seismic data processing software or programming languages ​​can be used to draw the corresponding 3D seismic coherence map. Typically, the coherence between adjacent seismic traces in the seismic coherence map is influenced by geological structures. When fault zones exist within the geological structure, seismic waves propagating around the fault zone are affected by the geological structure, resulting in similar waveforms. Therefore, seismic signals on both sides of the fault zone may exhibit high coherence. Thus, in the coherence map, data regions with high coherence are identified, and fault data is determined through these regions.

[0086] Step 120: Extract the fracture strike and fracture dip angle from the fracture data to obtain the fracture strike data point set and the fracture dip angle data point set;

[0087] In this embodiment, the fracture direction α and dip angle θ can be extracted using the ant body tracking method to obtain the fracture direction data point set (α1…α). i …α n ) and fracture dip angle data point set (θ1…θ i …θ n Based on the fault strike data point set and the fault dip angle data point set, a fault spatial distribution feature database [(α1, θ1), (α2, θ2), ..., (α...] is established. i θ i )...,(α n θ n )).

[0088] For example, when identifying fracture data, if two fracture zones are identified, the fracture strike and dip angle of the first fracture zone are extracted to establish a first fracture spatial distribution feature database, and the fracture strike and dip angle of the second fracture zone are extracted to establish a second fracture spatial distribution feature database.

[0089] Subsurface faults are often associated with the distribution of mineral resources. Accurately extracting the strike and dip angle of subsurface faults and establishing a database of fault spatial distribution characteristics can help to more clearly describe the spatial development of geological body boundaries, more accurately identify fault patterns, and accurately guide resource exploration and development.

[0090] Step 130: Based on the set of fault dip angle data points, determine the variation range of the fault dip angle, and divide the seismic coherence data volume into multiple seismic coherence sub-data segments according to the variation range;

[0091] In this embodiment, by dividing the seismic coherence data volume into multiple segments based on the change in fault dip angle, the fault data within each seismic coherence sub-segment can be analyzed independently, further improving the accuracy of fault feature identification. Dividing the seismic data into multiple segments according to the fault dip angle makes the geological structure within each dip segment clearer, allowing for more accurate identification and analysis of geological structural changes within the fault zone, and helping to solve the problem of unclear fault points. The geological features within each dip segment may differ; segmented analysis based on the fault dip angle makes local geological features more prominent, helping to better identify geological differences within the fault zone when the strata on both sides of the fault point have good continuity and basically consistent attitudes. Simultaneously, segmenting the seismic data for independent analysis reduces interference and confusion between different dip segments, allowing for a clearer understanding of the changes in geological features within each dip segment, and helping to solve the problem of continuous fault axes in equal-layered faults.

[0092] In summary, dividing seismic data into multiple segments based on fault dip angle for independent analysis can, to some extent, improve the situation where fault points are unclear, the strata on both sides of the fault point have good continuity and basically consistent attitude, and enhance geological resolution and identification capabilities.

[0093] Step 140: Based on the fault strike data point set, sample each of the seismic coherence sub-data segments to obtain sampled data;

[0094] Fault zones are typically important interfaces in geological structures, and their seismic reflection characteristics often exhibit discontinuities. By sampling seismic data along the fault zone's strike, this discontinuity can be highlighted, making the seismic reflection characteristics of the fault zone clearer and more prominent. This improves the resolution of seismic data in the fault zone region, enhances seismic imaging, and helps to more clearly display changes in the geological structure around the fault zone, providing more reliable geological information for geological exploration and resource assessment.

[0095] Step 150: Filter the sampled data along the fracture guide to obtain filtered data, wherein the fracture guide is a direction parallel to the fracture direction.

[0096] Filtering the sampled data along the fault guideline can enhance the continuity of the seismic wave reflection phase axis on both sides of the fault, improve the signal-to-noise ratio of the seismic data, and reduce coherence noise, thereby more effectively highlighting the characteristics of the fault zone. This helps to more accurately interpret the distribution and properties of the fault, making the fault zone clearer on the seismic profile.

[0097] Specifically, the calculation expression for the filtered data g0 after the fracture guide filtering process is as follows:

[0098]

[0099] Where μ(x,y,z) represents the sampled data, and θ i The angle of inclination is the fracture angle.

[0100] Step 160: Perform coherence calculation on the filtered data to obtain a new seismic coherence data volume;

[0101] In this embodiment, coherent calculations are performed on the filtered data to obtain a new seismic coherent data volume, which can further emphasize the continuity and geometric morphology of the fault zone, making the fault clearer and more prominent on the coherent volume.

[0102] The specific coherence calculation method can be the same as in step 110 above, using the third-generation eigenvalue analysis algorithm, or it can use a coherence algorithm based on subvolume characteristics (C4 algorithm). The C4 algorithm considers the subvolume characteristics of the seismic data volume and improves the accuracy and reliability of coherence calculation through a more complex calculation method. The specific algorithm selected depends on the actual needs and is not limited here.

[0103] Step 170: Use the new seismic coherence data volume to identify seismic fault features.

[0104] In this embodiment, a 3D seismic coherence map can be generated using a new seismic coherence data volume. Compared with a seismic coherence map generated based on an initial seismic coherence data volume, this 3D seismic coherence map provides a clearer display of fault zones on the seismic profile and a more prominent spatial distribution feature, which helps to identify and coarse seismic fault zone features more quickly and accurately.

[0105] In summary, the seismic fault identification method, device, electronic equipment, and storage medium of this embodiment, through coherent computation to obtain seismic coherent data volumes, can improve the quality and clarity of seismic data, thereby more accurately reflecting the characteristics of underground structures and providing a reliable data foundation for subsequent fault analysis. Determining fault data based on the seismic coherent data volume helps to quickly identify the location and distribution of underground faults. Extracting fault strike and dip angle data helps to better understand the geometric morphology and structural properties of faults, providing important parameters for three-dimensional simulation and structural interpretation of faults. Based on the fault dip angle data point set, dividing the seismic coherent data volume into multiple seismic coherent sub-data segments divides the fault structure into smaller regions, thereby enabling more refined analysis of the characteristics and distribution patterns of underground faults. Based on the fault strike data point set, sampling each seismic coherent sub-data segment can effectively extract key fault information, helping to present the geometric morphology and spatial distribution characteristics of underground faults more intuitively. This series of steps fully considers the spatial development patterns of the boundaries of the geological bodies to be identified. For faults with unclear fault points, good continuity of strata on both sides of the fault point, and generally consistent attitudes, it can identify not only faults oblique to the strike of the strata but also faults parallel to the strike. Subsequently, filtering the sampled data along the fault guideline enhances the continuity of the seismic wave reflection phase axes on both sides of the fault, improves the signal-to-noise ratio of the seismic data, and reduces coherence noise, thus more effectively highlighting the characteristics of the fault zone and helping to more accurately interpret the distribution and properties of the fault. Coherence calculations are performed on the filtered data to obtain a new seismic coherence data volume, which further emphasizes the continuity and geometry of the fault zone, making the fault clearer and more prominent in the coherence volume. Finally, the new seismic coherence data volume is used to identify seismic fault features, effectively improving the accuracy and reliability of fault identification.

[0106] In one embodiment, the step of filtering the sampled data along the fracture guide to obtain filtered data includes:

[0107] The sampled data is filtered along the fracture guide, while the seismic data volumes in other directions are kept unchanged, to obtain the filtered data;

[0108] In this embodiment, filtering is performed only on the sampled data parallel to the fault strike, and not on the seismic data volumes in other directions. This has at least three advantages:

[0109] Firstly, it can enhance the continuity of the reflection phase axis of seismic waves on both sides of the fault, improve the signal-to-noise ratio of seismic data, and reduce coherence noise, thereby more effectively highlighting the characteristics of the fault zone, helping to more accurately interpret the distribution and nature of the fault, and making the fault zone clearer on the seismic profile.

[0110] Secondly, the sampled data contains a large amount of information, much of which is redundant for specific geological interpretation tasks. By filtering only the data parallel to the faults, the amount of data to be processed can be reduced, thus improving data processing efficiency.

[0111] Thirdly, by not filtering seismic data volumes from other directions, the original characteristics and information of these data can be preserved, avoiding unnecessary errors or distortions introduced by filtering. This helps to utilize the diversity of these data for comprehensive analysis and comparison in subsequent geological interpretation, thereby obtaining a more comprehensive and accurate geological understanding.

[0112] In one embodiment, the step of dividing the seismic coherence data volume into multiple seismic coherence sub-data segments according to the change amplitude includes:

[0113] The tilt angle scanning step size is determined based on the change amplitude;

[0114] Based on the change amplitude and tilt scan step size, determine the number of segments to be divided into the seismic coherence sub-data segments;

[0115] The seismic coherence data volume is divided based on the number of segments to obtain multiple seismic coherence sub-segments.

[0116] In this embodiment, the variation in fault dip angle reflects the complexity of the geological structure and the geometry of the fault zone. By considering the variation range, it can be ensured that the sub-data segmentation more accurately reflects the actual geological conditions. For example, in areas with large dip angle variations, more detailed sub-data segmentation may be needed to capture the detailed features of the fault zone.

[0117] The preset scan step size can be set according to interpretation needs and accuracy requirements. By selecting an appropriate step size, the size and number of seismic coherence sub-segments can be controlled, thereby ensuring that the data within each sub-segment have similar dip characteristics. This helps to reduce the impact of dip changes on coherence calculations and improve the accuracy and reliability of interpretation results.

[0118] In one embodiment, the step of determining the tilt scan step size based on the change amplitude includes:

[0119] Detect whether the change amplitude is greater than or equal to a preset change threshold;

[0120] When the change amplitude is greater than or equal to a preset change threshold, the tilt angle scanning step size is determined as the first scanning step size; otherwise, the tilt angle scanning step size is determined as the second scanning step size, wherein the first scanning step size is greater than the second scanning step size.

[0121] In this embodiment, the variation range of the fracture inclination angle Δθ=θ n-θ1| is used to determine whether the value of Δθ is greater than or equal to the preset change threshold θ0. If Δθ is greater than or equal to θ0, the first scan step size ω1 is determined to be the preset dip angle scan step size ω. Then, based on the change in fault dip angle and the step size, the seismic coherence sub-data segment is divided into m segments.

[0122] In one embodiment, m is calculated as follows:

[0123]

[0124] When Δθ is less than θ0, the second scanning step size ω2 is determined to be the preset tilt angle scanning step size ω, and the calculation is obtained.

[0125]

[0126] The first scan step size ω1 is greater than the second scan step size ω2.

[0127] Using small step sizes within a small dip angle range helps to more precisely characterize the features of gently sloping fractures at small angles, reducing the loss or blurring of fracture information caused by excessively large step sizes. Conversely, using large step sizes within a large dip angle range allows for more effective capture of the salient features of steep, high-angle fractures, ensuring clear and accurate representation of both types of fractures. In practical processing, the dip angle of fractures can vary significantly under different geological conditions. By flexibly adjusting the step size, it is possible to adapt to various complex geological conditions, making the processing of seismic coherence data volumes in this embodiment more universal and practical.

[0128] In one embodiment, after the step of identifying seismic fault features using the new seismic coherence data volume, the method further includes:

[0129] Determine the fracture identification accuracy of the new seismic coherence data volume;

[0130] If the fracture identification accuracy is less than the preset accuracy threshold, the tilt scan step size is reduced to obtain a third scan step size. Based on the change amplitude and the third tilt scan step size, the number of segments is re-determined, resampling and re-filtering are performed until the re-determined fracture identification accuracy is greater than or equal to the preset accuracy threshold.

[0131] In this embodiment, it is determined whether the fracture identification accuracy of the new seismic coherence data volume is less than the preset accuracy. When the fracture identification accuracy is less than the preset accuracy, the preset dip angle scanning step size is reduced, and the seismic coherence data volume is re-divided into multiple seismic coherence sub-data segments according to the reduced dip angle scanning step size.

[0132] like Figure 2As shown, when the identification accuracy of the new seismic coherence data volume is not ideal, the number of segments m in the seismic coherence sub-data segment is re-determined by reducing the preset dip angle scanning step size ω to the third scanning step size ω. The calculation formula for m is as follows:

[0133]

[0134] After sampling and filtering m seismic coherence sub-segments, a new seismic coherence data volume is obtained. The accuracy of this new seismic coherence data volume is then reassessed to determine if it meets the accuracy requirements. The identification accuracy of the fault data is continuously improved by flexibly adjusting the preset dip angle scanning step size until the accuracy requirements are met.

[0135] In one embodiment, the step of sampling each of the seismic coherence sub-data segments based on the fault strike data point set includes:

[0136] Based on the fault strike data point set, each of the seismic coherence sub-data segments is sampled within a preset distance on both sides of the fault, wherein the preset distance is calculated as follows:

[0137] l = f × k,

[0138] Where l is the preset distance, f is the earthquake sampling frequency, and k is a preset empirical value.

[0139] In this embodiment, as Figure 5 As shown, the sampling process is concentrated within a preset distance *l* on both sides of the fault, making the sampling more focused and enabling more accurate capture of coherence changes near the fault zone. This helps to highlight the characteristics of the fault zone, reduce interference from data in non-fault areas, and improve the accuracy of fault detection. The calculation of the preset distance combines the seismic sampling frequency and preset empirical values, making the sampling process more flexible. By adjusting the empirical values, it is possible to adapt to different fault characteristics and interpretation needs, realizing personalized sampling strategies.

[0140] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0141] Example 2

[0142] In this embodiment, as Figure 2 As shown, a method for identifying earthquake faults is provided, including the following steps:

[0143] Step 1: Denoise the original 3D seismic data using median filtering to obtain denoised 3D seismic data. (See attached image.) Figure 3 The image shows 3D seismic data after median filtering and denoising.

[0144] Step 2: Using the third-generation eigenvalue analysis algorithm, coherence calculation is performed on the denoised 3D seismic data to obtain the 3D seismic coherence data volume, as shown in the attached figure. Figure 4 The image shown is a 3D seismic coherence map generated based on 3D seismic coherence data volume.

[0145] Step 3: In the 3D seismic coherence map, based on the similarity of seismic signals between adjacent seismic traces in the seismic data volume, fractures are initially identified, resulting in fracture zones F1, F2, and F3. The fracture identification results are attached. Figure 4 As shown;

[0146] Step 4: Extract the fracture strike α and dip angle θ using the ant-body tracking method, and establish a fracture spatial distribution feature database, as shown in the table below:

[0147] fracture number Fault Strike α Fracture angle θ F1 N40°E 60-80° F2 N36°E 65-85° F3 N50°W 60～75°

[0148] Step 5: Divide the 3D seismic coherence data volume into m segments based on the change in fault dip angle Δθ;

[0149] First, based on the change in fracture dip angle Δθ=|θ n -θ1|=30, set the step size ω=3 when scanning at the tilt angle;

[0150] Then, based on the changes in fault dip angle and step size, the 3D seismic coherence data volume is divided into m segments.

[0151]

[0152] Step 6: Along the fault zone strike α i Within a certain distance l on both sides of the fault, l = seismic sampling rate f × k, where k ranges from 3 to 8, the 3D seismic coherence data volume of each segment is resampled. A schematic diagram of the resampling of each seismic data segment is attached. Figure 5 As shown;

[0153] Step 7: Filter only the resampled 3D seismic coherence data volume parallel to the fault strike; do not filter seismic data volumes in other directions. The 3D seismic coherence data volume after fault-guided filtering is shown in the attached figure. Figure 6 As shown;

[0154] Step 8: Using the third-generation eigenvalue analysis algorithm, coherence calculation is performed again on the 3D seismic coherence data volume after fracture-guided filtering to obtain a new seismic coherence data volume. The 3D seismic coherence map is then optimized based on this new data volume. The optimized results are attached. Figure 7 As shown;

[0155] Step 9: In the optimized 3D seismic coherence map, identify the fractures in detail.

[0156] This embodiment provides a seismic fault identification method, which is a fine-grained fault identification method that combines fault dip scanning, fault-guided filtering, and coherence attribute analysis. First, the original 3D seismic data is denoised using median filtering. Then, third-generation eigenvalue coherence calculation is performed on the denoised 3D seismic data, and faults are initially identified based on the similarity of seismic signals between adjacent seismic traces in the seismic data volume. Next, the strike and dip of the fault are extracted using the ant-body tracking method. The 3D seismic coherence data volume is segmented according to the change in fault dip, and each segment of the 3D seismic coherence data volume is resampled along the fault strike. Based on this, fault-guided filtering is performed on the resampled 3D seismic coherence data volume, and third-generation eigenvalue coherence calculation is performed again on the filtered 3D seismic coherence data volume to achieve the goal of fine-grained fault identification. This invention integrates fracture dip scanning technology, fracture-guided filtering, and 3D seismic coherence attribute analysis technology. This not only effectively improves the accuracy and precision of fracture identification, reduces the uncertainty of using single methods for fracture identification, and achieves a comprehensive and accurate understanding of fractures at different scales, but also has significant implications for the exploration and development of oil and gas reservoirs. This method is highly applicable, effective, and has great potential for widespread application.

[0157] Example 3

[0158] In this embodiment, as Figure 8 As shown, a seismic fault data processing apparatus is provided, comprising:

[0159] The acquisition module 810 is used to acquire seismic data, perform coherence calculation on the seismic data to obtain a seismic coherence data volume, and determine fault data based on the seismic coherence data volume; wherein, the fault data includes fault strike and fault dip.

[0160] Extraction module 820 is used to extract the fracture strike and fracture dip angle from the fracture data to obtain a fracture strike data point set and a fracture dip angle data point set;

[0161] The partitioning module 830 is used to determine the variation range of the fault dip angle based on the fault dip angle data point set, and to partition the seismic coherence data volume into multiple seismic coherence sub-data segments according to the variation range.

[0162] The sampling module 840 is used to sample each of the seismic coherence sub-data segments based on the fault strike data point set to obtain sampled data;

[0163] The filtering module 850 is used to filter the sampled data along the fracture guide to obtain filtered data, wherein the fracture guide is a direction parallel to the fracture direction;

[0164] Calculation module 860 is used to perform coherent calculations on the filtered data to obtain a new seismic coherent data volume;

[0165] The identification module 870 is used to identify earthquake fault features using the new seismic coherence data volume.

[0166] In this embodiment, the acquisition module may acquire seismic data, perform coherence calculations on the seismic data to obtain a seismic coherence data volume, and determine fault data based on the seismic coherence data volume. The fault data includes fault strike and fault dip, and the seismic data is sent to the calculation module. The calculation module performs coherence calculations on the seismic data to obtain a seismic coherence data volume, and determines fault data based on the seismic coherence data volume, then sends the fault data to the extraction module. The extraction module extracts the fault strike and fault dip from the fault data, obtaining a fault strike data point set and a fault dip data point set, and sends these sets to the partitioning module. The partitioning module then determines the fault data based on the fault dip... The data point set is used to determine the variation range of the fault dip angle. Based on the variation range, the seismic coherence data volume is divided into multiple seismic coherence sub-data segments, and these sub-data segments are sent to the sampling module. The sampling module samples each of the seismic coherence sub-data segments based on the fault strike data point set to obtain sampled data, which is then sent to the filtering module. The filtering module filters the sampled data along the fault guideline to obtain filtered data, which is then sent to the calculation module. The calculation module performs coherence calculations on the filtered data to obtain a new seismic coherence data volume, which is then sent to the identification module. The identification module uses the new seismic coherence data volume to identify seismic fault features.

[0167] In one embodiment, the filtering module includes:

[0168] A filtering unit is used to filter the sampled data along the fracture guide, while keeping the seismic data volume in other directions unchanged, to obtain the filtered data;

[0169] In this embodiment, the filtering unit may filter the sampled data along the fracture guide, while keeping the seismic data volume in other directions unchanged, thus obtaining filtered data.

[0170] In one embodiment, the partitioning module includes:

[0171] The step size determination unit is used to determine the tilt angle scanning step size based on the change amplitude.

[0172] The segment number determination unit is used to determine the number of segments to be divided into the seismic coherence sub-data segments based on the change amplitude and the dip angle scanning step size;

[0173] A partitioning unit is used to partition the seismic coherence data volume based on the number of partitioning segments to obtain multiple seismic coherence sub-data segments.

[0174] In one embodiment, the step size determination unit includes:

[0175] A detection subunit is used to detect whether the change amplitude is greater than or equal to a preset change threshold.

[0176] A determining subunit is configured to determine the tilt angle scanning step size as a first scanning step size when the change amplitude is greater than or equal to a preset change threshold, and conversely, determine the tilt angle scanning step size as a second scanning step size when the change amplitude is less than or equal to a preset change threshold. In one embodiment, the device further includes:

[0177] The accuracy determination module is used to determine the fracture identification accuracy of the new seismic coherence data volume.

[0178] The step size adjustment module is used to reduce the dip angle scanning step size to obtain a third scanning step size when the accuracy determination module determines the fracture identification accuracy of the new seismic coherence data volume. If the fracture identification accuracy is less than a preset accuracy threshold, the module then determines the number of segments, resamples, and refilters based on the change amplitude and the third dip angle scanning step size, until the re-determined fracture identification accuracy is greater than or equal to the preset accuracy threshold.

[0179] In one embodiment, the sampling module includes:

[0180] A sampling unit is used to sample each of the seismic coherence sub-data segments within a preset distance on both sides of the fault, based on the fault strike data point set. The preset distance is calculated as follows:

[0181] l = f × k,

[0182] Where l is the preset distance, f is the earthquake sampling frequency, and k is a preset empirical value.

[0183] Specific limitations regarding the seismic fault data processing device can be found in the limitations of the seismic fault identification method described above, and will not be repeated here. Each unit in the aforementioned seismic fault data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These units can be embedded in or independent of the processor in an electronic device, or stored in the memory of an electronic device in software form, so that the processor can call and execute the corresponding operations of each unit.

[0184] Example 4

[0185] In this embodiment, an electronic device is provided. Its internal structure diagram can be shown as follows: Figure 9 As shown, the electronic device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs, and also contains a database for initial earthquake data. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with other electronic devices that have deployed application software. When the computer program is executed by the processor, it implements a seismic fracture identification method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the device's casing, or an external keyboard, touchpad, or mouse.

[0186] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0187] In one embodiment, an electronic device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to perform the following steps:

[0188] Seismic data is acquired, coherence calculations are performed on the seismic data to obtain a seismic coherence data volume, and fault data is determined based on the seismic coherence data volume; wherein, the fault data includes fault strike and fault dip.

[0189] Extract the fracture strike and fracture dip angle from the fracture data to obtain the fracture strike data point set and the fracture dip angle data point set;

[0190] Based on the set of fault dip angle data points, the variation range of the fault dip angle is determined, and the seismic coherence data volume is divided into multiple seismic coherence sub-data segments according to the variation range.

[0191] Based on the fault strike data point set, each of the seismic coherence sub-data segments is sampled to obtain sampled data;

[0192] The sampled data is filtered along the fracture guide to obtain filtered data, wherein the fracture guide is a direction parallel to the fracture direction;

[0193] The filtered data is subjected to coherent calculations to obtain a new seismic coherent data volume;

[0194] The new seismic coherence data volume is used to identify seismic fault features.

[0195] In one embodiment, when the processor executes a computer program, it also performs the following steps:

[0196] The sampled data is filtered along the fracture guide, while the seismic data volumes in other directions remain unchanged, to obtain the filtered data.

[0197] In one embodiment, when the processor executes a computer program, it also performs the following steps:

[0198] The tilt angle scanning step size is determined based on the aforementioned change range.

[0199] Based on the change amplitude and tilt scan step size, determine the number of segments to be divided into the seismic coherence sub-data segments;

[0200] The seismic coherence data volume is divided based on the number of segments to obtain multiple seismic coherence sub-segments.

[0201] In one embodiment, when the processor executes a computer program, it also performs the following steps:

[0202] Detect whether the change amplitude is greater than or equal to a preset change threshold;

[0203] When the change amplitude is greater than or equal to a preset change threshold, the tilt angle scanning step size is determined as the first scanning step size; otherwise, the tilt angle scanning step size is determined as the second scanning step size, wherein the first scanning step size is greater than the second scanning step size.

[0204] In one embodiment, when the processor executes a computer program, it also performs the following steps:

[0205] Determine the fracture identification accuracy of the new seismic coherence data volume;

[0206] If the fracture identification accuracy is less than the preset accuracy threshold, the tilt scan step size is reduced to obtain a third scan step size. Based on the change amplitude and the third tilt scan step size, the number of segments is re-determined, resampling and re-filtering are performed until the re-determined fracture identification accuracy is greater than or equal to the preset accuracy threshold.

[0207] In one embodiment, when the processor executes a computer program, it also performs the following steps:

[0208] Based on the fault strike data point set, each of the seismic coherence sub-data segments is sampled within a preset distance on both sides of the fault, wherein the preset distance is calculated as follows:

[0209] l = f × k,

[0210] Where l is the preset distance, f is the earthquake sampling frequency, and k is a preset empirical value.

[0211] Example 5

[0212] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it performs the following steps:

[0213] Seismic data is acquired, coherence calculations are performed on the seismic data to obtain a seismic coherence data volume, and fault data is determined based on the seismic coherence data volume; wherein, the fault data includes fault strike and fault dip.

[0214] Extract the fracture strike and fracture dip angle from the fracture data to obtain the fracture strike data point set and the fracture dip angle data point set;

[0215] Based on the set of fault dip angle data points, the variation range of the fault dip angle is determined, and the seismic coherence data volume is divided into multiple seismic coherence sub-data segments according to the variation range.

[0216] Based on the fault strike data point set, each of the seismic coherence sub-data segments is sampled to obtain sampled data;

[0217] The sampled data is filtered along the fracture guide to obtain filtered data, wherein the fracture guide is a direction parallel to the fracture direction;

[0218] The filtered data is subjected to coherent calculations to obtain a new seismic coherent data volume;

[0219] The new seismic coherence data volume is used to identify seismic fault features.

[0220] In one embodiment, when the computer program is executed by the processor, the following steps are also performed:

[0221] The sampled data is filtered along the fracture guide, while the seismic data volumes in other directions remain unchanged, to obtain the filtered data.

[0222] In one embodiment, when the computer program is executed by the processor, the following steps are also performed:

[0223] The tilt angle scanning step size is determined based on the aforementioned change range.

[0224] Based on the change amplitude and tilt scan step size, determine the number of segments to be divided into the seismic coherence sub-data segments;

[0225] The seismic coherence data volume is divided based on the number of segments to obtain multiple seismic coherence sub-segments.

[0226] In one embodiment, when the computer program is executed by the processor, the following steps are also performed:

[0227] Detect whether the change amplitude is greater than or equal to a preset change threshold;

[0228] When the change amplitude is greater than or equal to a preset change threshold, the tilt angle scanning step size is determined as the first scanning step size; otherwise, the tilt angle scanning step size is determined as the second scanning step size, wherein the first scanning step size is greater than the second scanning step size.

[0229] In one embodiment, when the computer program is executed by the processor, the following steps are also performed:

[0230] Determine the fracture identification accuracy of the new seismic coherence data volume;

[0231] If the fracture identification accuracy is less than the preset accuracy threshold, the tilt scan step size is reduced to obtain a third scan step size. Based on the change amplitude and the third tilt scan step size, the number of segments is re-determined, resampling and re-filtering are performed until the re-determined fracture identification accuracy is greater than or equal to the preset accuracy threshold.

[0232] In one embodiment, when the computer program is executed by the processor, the following steps are also performed:

[0233] Based on the fault strike data point set, each of the seismic coherence sub-data segments is sampled within a preset distance on both sides of the fault, wherein the preset distance is calculated as follows:

[0234] l = f × k,

[0235] Where l is the preset distance, f is the earthquake sampling frequency, and k is a preset empirical value.

[0236] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0237] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0238] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for identifying earthquake faults, characterized in that, include: Seismic data is acquired, coherence calculations are performed on the seismic data to obtain a seismic coherence data volume, and fault data is determined based on the seismic coherence data volume; wherein, the fault data includes fault strike and fault dip. Extract the fracture strike and fracture dip angle from the fracture data to obtain the fracture strike data point set and the fracture dip angle data point set; Based on the set of fault dip angle data points, the variation range of the fault dip angle is determined, and the seismic coherence data volume is divided into multiple seismic coherence sub-data segments according to the variation range. Based on the fault strike data point set, each of the seismic coherence sub-data segments is sampled to obtain sampled data; The sampled data is filtered along the fracture guide to obtain filtered data, wherein the fracture guide is a direction parallel to the fracture direction; The filtered data is subjected to coherent calculations to obtain a new seismic coherent data volume; The new seismic coherence data volume is used to identify seismic fault features.

2. The method according to claim 1, characterized in that, The step of filtering the sampled data along the fracture guide to obtain filtered data includes: The sampled data is filtered along the fracture guide, while the seismic data volumes in other directions remain unchanged, to obtain the filtered data.

3. The method according to claim 1, characterized in that, The step of dividing the seismic coherence data volume into multiple seismic coherence sub-data segments according to the change amplitude includes: The tilt angle scanning step size is determined based on the aforementioned change range. Based on the change amplitude and tilt scan step size, determine the number of segments to be divided into the seismic coherence sub-data segments; The seismic coherence data volume is divided based on the number of segments to obtain multiple seismic coherence sub-segments.

4. The method according to claim 3, characterized in that, The step of determining the tilt angle scanning step size based on the change amplitude includes: Detect whether the change amplitude is greater than or equal to a preset change threshold; When the change amplitude is greater than or equal to a preset change threshold, the tilt angle scanning step size is determined as the first scanning step size; otherwise, the tilt angle scanning step size is determined as the second scanning step size, wherein the first scanning step size is greater than the second scanning step size.

5. The method according to claim 3, characterized in that, In the step of determining the number of segments for the seismic coherence sub-data segment based on the change amplitude and dip angle scanning step size, the formula for calculating the number of segments is as follows: Where m is the number of segments, Δθ is the change range, and ω is the tilt angle scanning step size.

6. The method according to claim 3, characterized in that, Following the step of using the new seismic coherence data volume to identify seismic fault features, the method further includes: Determine the fracture identification accuracy of the new seismic coherence data volume; If the fracture identification accuracy is less than the preset accuracy threshold, the tilt scan step size is reduced to obtain a third scan step size. Based on the change amplitude and the third tilt scan step size, the number of segments is re-determined, resampling and re-filtering are performed until the re-determined fracture identification accuracy is greater than or equal to the preset accuracy threshold.

7. The method according to any one of claims 1-6, characterized in that, The step of sampling each of the seismic coherence sub-data segments based on the fault strike data point set includes: Based on the fault strike data point set, each of the seismic coherence sub-data segments is sampled within a preset distance on both sides of the fault, wherein the preset distance is calculated as follows: l = f × k, Where l is the preset distance, f is the earthquake sampling frequency, and k is a preset empirical value.

8. An earthquake fault identification device, characterized in that, include: The acquisition module is used to acquire seismic data, perform coherence calculations on the seismic data to obtain a seismic coherence data volume, and determine fault data based on the seismic coherence data volume; wherein, the fault data includes fault strike and fault dip. The extraction module is used to extract the fracture strike and fracture dip angle from the fracture data to obtain the fracture strike data point set and the fracture dip angle data point set; The partitioning module is used to determine the variation range of the fault dip angle based on the fault dip angle data point set, and to partition the seismic coherence data volume into multiple seismic coherence sub-data segments according to the variation range. The sampling module is used to sample each of the seismic coherence sub-data segments based on the fault strike data point set to obtain sampled data; A filtering module is used to filter the sampled data along the fracture guide to obtain filtered data, wherein the fracture guide is a direction parallel to the fracture direction; The calculation module is used to perform coherent calculations on the filtered data to obtain a new seismic coherent data volume; The identification module is used to identify seismic fault features using the new seismic coherence data volume.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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