Pre-stored fracture geophysical recognition method and system

Abstract

The invention relates to the technical field of geological exploration, in particular to a pre-stored fracture geophysical recognition method and system, and the method comprises the steps: data collection, data screening, data fusion, position determination, filtering processing, clustering pickup and recognition analysis. Compared with the prior art that data collected at a single time is directly processed, the problem that an identification result is inaccurate possibly due to insufficient data precision is solved, the scheme adopts multiple times of collection and data screening, multiple original data sets are established, and a data fusion algorithm of the Bayesian theorem is applied to carry out comprehensive processing, so that the accuracy of the identification result is improved. According to the scheme, the precision and quality of the data are remarkably improved by fusing the information of the multiple data sets, and the defect can be overcome by fusing the information of the multiple data sources even if the precision of the data collected at a time is insufficient, so that the accuracy and reliability of pre-stored fracture identification are improved.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and in particular to a method and system for geophysical identification of pre-existing faults. Background Art

[0002] In oil and gas geological exploration, the identification of pre-existing faults is of great significance for understanding the structure of underground rock formations, determining the distribution of oil and gas reservoirs, and evaluating the potential of oil and gas resources. However, when the existing technology directly processes the data collected in a single time, it often faces the problem of insufficient data accuracy, which may lead to inaccurate results of pre-existing fault identification.

[0003] Specifically, the data collected in a single time may be affected by many factors, such as the attenuation of seismic waves, the complexity of underground rock formations, and the limitations of acquisition equipment, all of which may limit the accuracy and quality of the data. Therefore, when using these data to identify pre-existing faults, misjudgments, omissions, or unclear identification results may occur.

[0004] In order to overcome this problem, the present invention proposes a method and system for geophysical identification of pre-existing faults. The method collects and screens data multiple times to establish multiple original data sets, and applies the Bayesian data fusion algorithm for comprehensive processing. This scheme can fuse the information of multiple data sets, thereby significantly increasing the accuracy and quality of the data. Even if the accuracy of single-time data collection is insufficient, this defect can be compensated by fusing the information of multiple data sources. Summary of the invention

[0005] In order to overcome the problems raised in the above background technology, the present invention proposes a method and system for geophysical identification of pre-existing faults.

[0006] The technical solution of the present invention is: a method for geophysical identification of pre-existing faults, comprising the following steps: S11: data acquisition, using seismic exploration equipment to acquire a three-dimensional seismic data volume of the underground structure, and repeating the acquisition N times, wherein the three-dimensional seismic data volume of the underground structure includes physical properties and structural information of underground rocks; S12: Data screening, performing simple identification on the data collected each time, screening the data irrelevant to the existing fracture, retaining the remaining data, and establishing multiple original data sets, wherein each original data set stores the remaining data collected once; S13: data fusion, fusing the data in multiple original data sets to obtain a final data set; S14: Position determination, projecting the original 3D seismic data volume into 3D space for display; S15: filtering processing, performing low-pass filtering on the original 3D seismic data volume to highlight the characteristics of medium and low frequency seismic reflections; S16: cluster picking, using clustering algorithm to pick continuous reflection interfaces in the seismic data volume; S17: Identification analysis: performing inclination filtering on the cluster analysis results below the determined basement position to remove low-angle results, and then identifying the distribution of pre-existing faults based on the filtered results.

[0007] Preferably, when using seismic exploration equipment to obtain a three-dimensional seismic data volume of an underground structure and repeating the acquisition N times, the following steps are included: S21: Define the exploration objectives and, based on the needs of geological exploration, the types, depths and ranges of underground structures to be explored; S22: Select seismic exploration equipment. Select appropriate seismic exploration equipment according to the characteristics and requirements of the exploration target; S23: Plan the exploration area, according to the geological characteristics and exploration objectives, plan the exploration area, and determine the layout and spacing of the survey lines; S24: Arrange survey lines. Arrange survey lines according to the plan in the exploration area to ensure that the spacing between survey lines is uniform and covers the entire area; S25: Excite seismic waves, using a source device to excite seismic waves underground; S26: receiving seismic wave signals, using a geophone to receive seismic wave signals, and converting them into electrical signals for transmission; S27: Repeat the acquisition, repeat step S25 and step S26, acquire seismic wave signals for multiple times, check the seismic wave signals, and remove data with abnormalities or insufficient data accuracy.

[0008] Preferably, after collecting the data, the following steps are also included: S31: Data preprocessing, performing denoising and interference removal preprocessing operations on the collected original seismic data; S32: data sorting, sorting the pre-processed seismic data according to the survey line and time dimension to form a three-dimensional seismic data volume; S33: Data quality control, quality inspection and control of 3D seismic data to ensure data integrity and accuracy.

[0009] Preferably, when simply identifying the data collected each time, screening the data irrelevant to the existing fracture, retaining the remaining data, and establishing multiple original data sets, the following steps are included: S41: data loading, loading the three-dimensional seismic data volume obtained each time into the data processing system; S42: Preliminary identification: using a preset algorithm, the loaded data is preliminarily identified to quickly filter out the data parts that are obviously irrelevant to the existing fractures; S43: Data screening: based on the results of the preliminary identification, the data not related to the existing faults are screened, that is, the information not directly related to the fault identification is removed from these data; S44: Data set establishment, retaining the remaining data after screening, and establishing multiple original data sets according to the number of acquisitions, wherein each original data set corresponds to data acquired once, and stores key information that may be related to the existing fracture.

[0010] Preferably, when fusing the data in multiple original data sets to obtain a final data set, the following steps are included: S51: further preprocessing, performing precise preprocessing on the data to make the data consistent; S52: Data registration, spatially registering the data in different data sets, i.e. ensuring that the same position in each data set corresponds to the same position in the actual underground structure; S53: Application of data fusion algorithm, using the selected data fusion algorithm to comprehensively process the data in multiple data sets to obtain a final data set.

[0011] Preferably, when the selected data fusion algorithm is used to comprehensively process the data in the multiple data sets to obtain the final data set, the following steps are included: S61: Data modeling, building a probability model for each data source to describe the observed data of that data; S62: Calculate the prior probability and assign a prior probability to each observation data based on historical data and experience; S63: For each hypothesis or category, calculate the likelihood of the observed data under that category; S64: Apply Bayes' theorem, use Bayes' theorem to calculate the posterior probability; S65: Decision making, selecting the best category based on the posterior probability and the set decision threshold.

[0012] Preferably, when the original three-dimensional seismic data volume is projected into the three-dimensional space for display, the basement position is determined by the three-dimensional space sea flooding method, specifically: Taking the Z direction as the background, the plane composed of the XY directions is translated up and down to close the layers interpreted by the basement in three-dimensional space, and the same-direction axis during closure is determined as the basement position.

[0013] Preferably, when picking up the continuous reflection interface in the seismic data volume by using the clustering algorithm, the following steps are included: S71: Algorithm parameter setting, setting the parameters of the DBSCAN algorithm according to the characteristics of the seismic data. The parameters of the DBSCAN algorithm include the minimum number of samples and the neighborhood radius; S72: algorithm execution, inputting seismic data into DBSCAN algorithm for cluster analysis; S73: Result picking, extracting continuous reflection interfaces from the clustering results.

[0014] Preferably, when performing dip filtering on the cluster analysis results below the determined basement position to remove low-angle results, and then identifying the distribution of pre-existing faults based on the filtered results, the following steps are included: S81: First, the normal vector of the seismic reflection interface is used to calculate the dip angle, and the dip angle threshold is set according to the characteristics of the geological structure and seismic data. Finally, the cluster analysis results are subjected to dip angle filtering to remove the low-angle results. S82: According to the filtered results, the distribution of pre-existing faults is identified.

[0015] Pre-existing fault geophysical identification systems include: The data acquisition module is responsible for acquiring the original three-dimensional seismic data volume, including the reflection and scattering information of the underground rock formation; The data processing module is used to process and process the raw data acquired by the data acquisition module to obtain the final data set; The position determination module is used to determine the basement position by using the three-dimensional sea flooding method. First, the original three-dimensional seismic data volume is projected into the three-dimensional space for display, and then the basement interpretation horizon is closed in the three-dimensional space through translation and adjustment, thereby determining the basement position. A filtering processing module is used to perform low-pass filtering on the original three-dimensional seismic data volume; A cluster picking module is used to pick up continuous reflection interfaces in the seismic data volume using a clustering algorithm to obtain cluster analysis results; The identification module is used to perform dip filtering on the cluster analysis results below the determined basement position and remove low-angle results to obtain the distribution of pre-existing faults.

[0016] Beneficial effects of the present invention: 1. Compared with the prior art that directly processes the data collected once, which may lead to inaccurate recognition results due to insufficient data accuracy, this solution collects and screens data multiple times, establishes multiple original data sets, and applies the Bayesian data fusion algorithm for comprehensive processing. This solution significantly increases the accuracy and quality of the data by fusing the information of multiple data sets. Even if the accuracy of the single-collection data is insufficient, this defect can be compensated by fusing the information of multiple data sources, thereby improving the accuracy and reliability of the recognition of pre-existing faults; 2. The accuracy and reliability of data are improved through multiple data collection and screening. The characteristics of medium and low frequency seismic reflection are highlighted through data fusion and filtering, which helps to more accurately identify pre-existing faults. The clustering algorithm is used to pick up continuous reflection interfaces, and combined with the dip filtering technology, the accuracy and efficiency of pre-existing fault identification are further improved. This method can provide important geological information for oil and gas exploration and development, which helps to reduce exploration risks and improve development benefits. 3. When using seismic exploration equipment to obtain three-dimensional seismic data of underground structures, first clarify the exploration target and select appropriate seismic exploration equipment according to needs, then plan the exploration area and arrange the survey line, then excite seismic waves underground and use detectors to receive seismic wave signals, repeat the acquisition process many times and eliminate data anomalies and data with insufficient accuracy to ensure that accurate and reliable three-dimensional seismic data of underground structures are finally obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 What is shown is a schematic flow chart of the geophysical identification method of pre-existing faults of the present invention; Figure 2 Shown is a schematic diagram of the structure of the pre-existing fracture geophysical identification system of the present invention. DETAILED DESCRIPTION

[0018] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0019] See also Figure 1 The present invention provides an embodiment: a method for geophysical identification of pre-existing faults, comprising the following steps: S11: data acquisition, using seismic exploration equipment to acquire a three-dimensional seismic data volume of the underground structure, and repeating the acquisition N times, wherein the three-dimensional seismic data volume of the underground structure includes physical properties and structural information of underground rocks; S12: Data screening, performing simple identification on the data collected each time, screening the data irrelevant to the existing fracture, retaining the remaining data, and establishing multiple original data sets, wherein each original data set stores the remaining data collected once; S13: data fusion, fusing the data in multiple original data sets to obtain a final data set; S14: Position determination, projecting the original 3D seismic data volume into 3D space for display; S15: filtering processing, performing low-pass filtering on the original 3D seismic data volume to highlight the characteristics of medium and low frequency seismic reflections; S16: cluster picking, using clustering algorithm to pick continuous reflection interfaces in the seismic data volume; S17: Identification analysis: performing inclination filtering on the cluster analysis results below the determined basement position to remove low-angle results, and then identifying the distribution of pre-existing faults based on the filtered results.

[0020] As described above, the geophysical identification method of pre-existing faults includes the steps of data acquisition, data screening, data fusion, location determination, filtering processing, cluster picking and identification analysis. First, the three-dimensional seismic data volume of the underground structure is collected multiple times using seismic exploration equipment, and then the data unrelated to the existing faults are screened, and the remaining data are retained and fused. Next, the original data is projected into three-dimensional space, low-pass filtered to highlight the characteristics of medium and low-frequency seismic reflections, and a clustering algorithm is used to pick up continuous reflection interfaces. Finally, the cluster analysis results below the basement position are dip-filtered to identify the distribution of pre-existing faults.

[0021] By collecting and screening data multiple times, the accuracy and reliability of the data are improved; through data fusion and filtering processing, the medium and low frequency seismic reflection characteristics are highlighted, which helps to more accurately identify pre-existing faults; the clustering algorithm is used to pick up continuous reflection interfaces, and combined with dip filtering technology, the accuracy and efficiency of pre-existing fault identification are further improved. This method can provide important geological information for oil and gas exploration and development, help reduce exploration risks and improve development benefits.

[0022] Preferably, when using seismic exploration equipment to obtain a three-dimensional seismic data volume of an underground structure and repeating the acquisition N times, the following steps are included: S21: Define the exploration objectives and, based on the needs of geological exploration, the types, depths and ranges of underground structures to be explored; S22: Select seismic exploration equipment. Select appropriate seismic exploration equipment according to the characteristics and requirements of the exploration target; S23: Plan the exploration area, according to the geological characteristics and exploration objectives, plan the exploration area, and determine the layout and spacing of the survey lines; S24: Arrange survey lines. Arrange survey lines according to the plan in the exploration area to ensure that the spacing between survey lines is uniform and covers the entire area; S25: Excite seismic waves, using a source device to excite seismic waves underground; S26: receiving seismic wave signals, using a geophone to receive seismic wave signals, and converting them into electrical signals for transmission; S27: Repeat the acquisition, repeat step S25 and step S26, acquire seismic wave signals for multiple times, check the seismic wave signals, and remove data with abnormalities or insufficient data accuracy.

[0023] As described above, when using seismic exploration equipment to obtain three-dimensional seismic data of underground structures, first clarify the exploration target and select appropriate seismic exploration equipment according to needs, then plan the exploration area and arrange the survey lines, then excite seismic waves underground and use detectors to receive seismic wave signals, repeat the acquisition process many times and eliminate data anomalies and data with insufficient accuracy to ensure that accurate and reliable three-dimensional seismic data of underground structures are finally obtained.

[0024] Preferably, after collecting the data, the following steps are also included: S31: Data preprocessing, performing denoising and interference removal preprocessing operations on the collected original seismic data; S32: data sorting, sorting the pre-processed seismic data according to the survey line and time dimension to form a three-dimensional seismic data volume; S33: Data quality control, quality inspection and control of 3D seismic data to ensure data integrity and accuracy.

[0025] As described above, the present invention enhances the signal-to-noise ratio of data through denoising and interference removal preprocessing, and data organization ensures the structuring and orderliness of the data, which is convenient for subsequent analysis; and strict data quality control ensures the integrity and accuracy of the three-dimensional seismic data body, laying a solid foundation for subsequent geophysical identification and geological interpretation.

[0026] Preferably, when simply identifying the data collected each time, screening the data irrelevant to the existing fracture, retaining the remaining data, and establishing multiple original data sets, the following steps are included: S41: data loading, loading the three-dimensional seismic data volume obtained each time into the data processing system; S42: Preliminary identification: using a preset algorithm, the loaded data is preliminarily identified to quickly filter out the data parts that are obviously irrelevant to the existing fractures; S43: Data screening: based on the results of the preliminary identification, the data not related to the existing faults are screened, that is, the information not directly related to the fault identification is removed from these data; S44: Data set establishment, retaining the remaining data after screening, and establishing multiple original data sets according to the number of acquisitions, wherein each original data set corresponds to data acquired once, and stores key information that may be related to the existing fracture.

[0027] Preferably, when fusing the data in multiple original data sets to obtain a final data set, the following steps are included: S51: further preprocessing, performing precise preprocessing on the data to make the data consistent; S52: Data registration, spatially registering the data in different data sets, i.e. ensuring that the same position in each data set corresponds to the same position in the actual underground structure; S53: Application of data fusion algorithm, using the selected data fusion algorithm to comprehensively process the data in multiple data sets to obtain a final data set.

[0028] Preferably, when the selected data fusion algorithm is used to comprehensively process the data in the multiple data sets to obtain the final data set, the following steps are included: S61: Data modeling, building a probability model for each data source to describe the observed data of that data; S62: Calculate the prior probability and assign a prior probability to each observation data based on historical data and experience; S63: For each hypothesis or category, calculate the likelihood of the observed data under that category; S64: Apply Bayes' theorem, use Bayes' theorem to calculate the posterior probability; S65: Decision making, selecting the best category based on the posterior probability and the set decision threshold.

[0029] As described above, in the data screening stage, the scheme first loads the three-dimensional seismic data body collected each time into the processing system, and quickly screens out the data parts that are not related to the existing faults through a preset algorithm. Then, these irrelevant data are screened, and key information is retained and multiple original data sets are established. In the data fusion stage, precise preprocessing and data alignment are first performed to ensure data consistency and spatial position accuracy. Then, a data fusion algorithm based on the Bayesian theorem is used to model, calculate prior probability, likelihood and posterior probability, and finally obtain the final data set that integrates the information of multiple data sets.

[0030] Compared with the prior art that directly processes data collected once, which may lead to inaccurate recognition results due to insufficient data accuracy, this solution collects and screens data multiple times, establishes multiple original data sets, and applies the Bayesian theorem data fusion algorithm for comprehensive processing. This solution significantly increases the accuracy and quality of the data by fusing the information of multiple data sets. Even when the accuracy of single-collection data is insufficient, this defect can be compensated by fusing the information of multiple data sources, thereby improving the accuracy and reliability of pre-existing fracture identification.

[0031] Preferably, when the original three-dimensional seismic data volume is projected into the three-dimensional space for display, the basement position is determined by the three-dimensional space sea flooding method, specifically: Taking the Z direction as the background, the plane composed of the XY directions is translated up and down to close the layers interpreted by the basement in three-dimensional space, and the same-direction axis during closure is determined as the basement position.

[0032] As described above, the present invention determines the basement position by the three-dimensional sea flooding method, and the beneficial effect is that the basement can be positioned intuitively and accurately in three-dimensional space. By translating the XY plane up and down and finding closed isotropic axes, the continuity and consistency of the layers interpreted by the basement in three-dimensional space are ensured, the accuracy and reliability of the basement position determination are improved, and a solid foundation is provided for subsequent seismic data analysis and fault identification.

[0033] Preferably, when picking up the continuous reflection interface in the seismic data volume by using the clustering algorithm, the following steps are included: S71: Algorithm parameter setting, setting the parameters of the DBSCAN algorithm according to the characteristics of the seismic data. The parameters of the DBSCAN algorithm include the minimum number of samples and the neighborhood radius; S72: algorithm execution, inputting seismic data into DBSCAN algorithm for cluster analysis; S73: Result picking, extracting continuous reflection interfaces from the clustering results.

[0034] As described above, the present invention sets reasonable parameters for the DBSCAN algorithm and applies it to seismic data, which can efficiently and accurately pick up continuous reflection interfaces in seismic data bodies, avoid omissions or misjudgments that may be made by traditional methods, improve the accuracy and automation of reflection interface identification, and provide reliable data support for fault identification and geological modeling.

[0035] Preferably, when performing dip filtering on the cluster analysis results below the determined basement position to remove low-angle results, and then identifying the distribution of pre-existing faults based on the filtered results, the following steps are included: S81: First, the normal vector of the seismic reflection interface is used to calculate the dip angle, and the dip angle threshold is set according to the characteristics of the geological structure and seismic data. Finally, the cluster analysis results are subjected to dip angle filtering to remove the low-angle results. S82: According to the filtered results, the distribution of pre-existing faults is identified.

[0036] As described above, the present invention effectively removes low-angle results that are not directly related to the identification of pre-existing faults by calculating the dip of the seismic reflection interface and performing dip filtering, and highlights the medium and high angle features related to faults, thereby improving the accuracy and clarity of the identification of the distribution of pre-existing faults, and providing more reliable information support for geological interpretation and oil and gas exploration.

[0037] See also Figure 2The present invention provides an embodiment: a geophysical identification system for pre-existing faults, comprising: The data acquisition module is responsible for acquiring the original three-dimensional seismic data volume, including the reflection and scattering information of the underground rock formation; The data processing module is used to process and process the raw data acquired by the data acquisition module to obtain the final data set; The position determination module is used to determine the basement position by using the three-dimensional sea flooding method. First, the original three-dimensional seismic data volume is projected into the three-dimensional space for display, and then the basement interpretation horizon is closed in the three-dimensional space through translation and adjustment, thereby determining the basement position. A filtering processing module is used to perform low-pass filtering on the original three-dimensional seismic data volume; A cluster picking module is used to pick up continuous reflection interfaces in the seismic data volume using a clustering algorithm to obtain cluster analysis results; The identification module is used to perform dip filtering on the cluster analysis results below the determined basement position and remove low-angle results to obtain the distribution of pre-existing faults.

[0038] As described above, the present invention realizes a fully automated process from raw three-dimensional seismic data to pre-existing fault distribution identification by integrating multiple modules such as data acquisition, processing, position determination, filtering processing, cluster picking and identification, significantly improving the efficiency and accuracy of data processing, ensuring the accuracy of basement position determination, and effectively filtering out irrelevant information, highlighting fault characteristics, and providing more accurate and reliable geological information support for oil and gas exploration and development.

[0039] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for geophysical identification of pre-existing faults; characterized by: The following steps are included: S11: data acquisition, using seismic exploration equipment to acquire a three-dimensional seismic data volume of the underground structure, and repeating the acquisition N times, wherein the three-dimensional seismic data volume of the underground structure includes physical properties and structural information of underground rocks; S12: Data screening, performing simple identification on the data collected each time, screening the data irrelevant to the existing fracture, retaining the remaining data, and establishing multiple original data sets, wherein each original data set stores the remaining data collected once; S13: data fusion, fusing the data in multiple original data sets to obtain a final data set; S14: Position determination, projecting the original 3D seismic data volume into 3D space for display; S15: filtering processing, performing low-pass filtering on the original 3D seismic data volume to highlight the characteristics of medium and low frequency seismic reflections; S16: cluster picking, using clustering algorithm to pick continuous reflection interfaces in the seismic data volume; S17: Identification analysis: performing inclination filtering on the cluster analysis results below the determined basement position to remove low-angle results, and then identifying the distribution of pre-existing faults based on the filtered results.

2. A method for geophysical identification of pre-existing faults according to claim 1, characterized in that: When using seismic exploration equipment to obtain a three-dimensional seismic data volume of an underground structure and repeating the acquisition N times, the following steps are included: S21: Define the exploration objectives and, based on the needs of geological exploration, the types, depths and ranges of underground structures to be explored; S22: Select seismic exploration equipment. Select appropriate seismic exploration equipment according to the characteristics and requirements of the exploration target; S23: Plan the exploration area, according to the geological characteristics and exploration objectives, plan the exploration area, and determine the layout and spacing of the survey lines; S24: Arrange survey lines. Arrange survey lines according to the plan in the exploration area to ensure that the spacing between survey lines is uniform and covers the entire area; S25: Excite seismic waves, using a source device to excite seismic waves underground; S26: receiving seismic wave signals, using a geophone to receive seismic wave signals, and converting them into electrical signals for transmission; S27: Repeat the acquisition, repeat step S25 and step S26, acquire seismic wave signals for multiple times, check the seismic wave signals, and remove data with abnormalities or insufficient data accuracy.

3. A method for geophysical identification of pre-existing faults according to claim 2, characterized in that: After collecting the data, the following steps are also included: S31: Data preprocessing, performing denoising and interference removal preprocessing operations on the collected original seismic data; S32: data sorting, sorting the pre-processed seismic data according to the survey line and time dimension to form a three-dimensional seismic data volume; S33: Data quality control, quality inspection and control of 3D seismic data to ensure data integrity and accuracy.

4. A method for geophysical identification of pre-existing faults according to claim 3, characterized in that: The following steps are included when simply identifying the data obtained from each acquisition, screening the data not related to the existing fracture, retaining the remaining data, and establishing multiple original data sets: S41: data loading, loading the three-dimensional seismic data volume obtained each time into the data processing system; S42: Preliminary identification: using a preset algorithm, the loaded data is preliminarily identified to quickly filter out the data parts that are obviously irrelevant to the existing fractures; S43: Data screening: based on the results of the preliminary identification, the data not related to the existing faults are screened, that is, the information not directly related to the fault identification is removed from these data; S44: Data set establishment, retaining the remaining data after screening, and establishing multiple original data sets according to the number of acquisitions, wherein each original data set corresponds to data acquired once, and stores key information that may be related to the existing fracture.

5. A method for geophysical identification of pre-existing faults according to claim 4, characterized in that: When fusing data from multiple original data sets to obtain a final data set, the following steps are included: S51: further preprocessing, performing precise preprocessing on the data to make the data consistent; S52: Data registration, spatially registering the data in different data sets, i.e. ensuring that the same position in each data set corresponds to the same position in the actual underground structure; S53: Application of data fusion algorithm, using the selected data fusion algorithm to comprehensively process the data in multiple data sets to obtain a final data set.

6. A method for geophysical identification of pre-existing faults according to claim 5, characterized in that: When the selected data fusion algorithm is used to comprehensively process the data in multiple data sets to obtain the final data set, the following steps are included: S61: Data modeling, building a probability model for each data source to describe the observed data of that data; S62: Calculate the prior probability and assign a prior probability to each observation data based on historical data and experience; S63: For each hypothesis or category, calculate the likelihood of the observed data under that category; S64: Apply Bayes' theorem, use Bayes' theorem to calculate the posterior probability; S65: Decision making, selecting the best category based on the posterior probability and the set decision threshold.

7. A method for geophysical identification of pre-existing faults according to claim 6, characterized in that: When the original 3D seismic data volume is projected into 3D space for display, the basement position is determined by the 3D space sea flooding method, specifically: Taking the Z direction as the background, the plane composed of the XY directions is translated up and down to close the layers interpreted by the basement in three-dimensional space, and the same-direction axis during closure is determined as the basement position.

8. A method for geophysical identification of pre-existing faults according to claim 7, characterized in that: When using clustering algorithm to pick up continuous reflection interfaces in seismic data volume, the following steps are included: S71: Algorithm parameter setting, setting the parameters of the DBSCAN algorithm according to the characteristics of the seismic data. The parameters of the DBSCAN algorithm include the minimum number of samples and the neighborhood radius; S72: algorithm execution, inputting seismic data into DBSCAN algorithm for cluster analysis; S73: Result picking, extracting continuous reflection interfaces from the clustering results.

9. A method for geophysical identification of pre-existing faults according to claim 8, characterized in that: The following steps are included when applying dip filtering to the cluster analysis results below the determined basement position, removing low-angle results, and then identifying the distribution of pre-existing faults based on the filtered results: S81: First, the normal vector of the seismic reflection interface is used to calculate the dip angle, and the dip angle threshold is set according to the characteristics of the geological structure and seismic data. Finally, the cluster analysis results are subjected to dip angle filtering to remove the low-angle results. S82: According to the filtered results, the distribution of pre-existing faults is identified.

10. A system for identifying pre-existing faults in geophysical form, used in a method for identifying pre-existing faults in geophysical form according to claim 1-9, characterized in that: Included are: The data acquisition module is responsible for acquiring the original three-dimensional seismic data volume, including the reflection and scattering information of the underground rock formation; The data processing module is used to process and process the raw data acquired by the data acquisition module to obtain the final data set; The position determination module is used to determine the basement position by using the three-dimensional sea flooding method. First, the original three-dimensional seismic data volume is projected into the three-dimensional space for display, and then the basement interpretation horizon is closed in the three-dimensional space through translation and adjustment, thereby determining the basement position. A filtering processing module is used to perform low-pass filtering on the original three-dimensional seismic data volume; A cluster picking module is used to pick up continuous reflection interfaces in the seismic data volume using a clustering algorithm to obtain cluster analysis results; The identification module is used to perform dip filtering on the cluster analysis results below the determined basement position and remove low-angle results to obtain the distribution of pre-existing faults.

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