Three-dimensional seismic data acquisition method and system

Abstract

The invention relates to the technical field of seismic image recognition, in particular to a three-dimensional seismic data acquisition method and system. The method comprises the following steps: acquiring seismic source region data; performing real-time multi-source sensing data acquisition on the seismic source area data to obtain seismic source area multi-source sensing data; carrying out amplitude conversion on the multi-source sensing data of the seismic source region to generate a seismic source region amplitude conversion diagram; performing pre-earthquake potential easy-to-earthquake area positioning on the seismic source area amplitude conversion diagram to generate pre-earthquake easy-to-earthquake area positioning data; epicentral seismic waveform recording is carried out on the pre-earthquake earthquake-prone area positioning data, and earthquake-prone area epicentral seismic waveform data are generated; and carrying out earthquake instantaneous dynamic analysis on the epicenter seismic waveform data of the earthquake-prone area to generate epicenter instantaneous magnitude data. According to the invention, through real-time multi-source sensing data acquisition, dynamic epicenter analysis and three-dimensional modeling, the real-time performance and dynamic analysis capability of seismic data acquisition are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic image recognition, and in particular, to a three-dimensional seismic data acquisition method and system. Background Art

[0002] The initial seismic exploration technology mainly relied on the acquisition of two-dimensional data and could not effectively reflect the complex underground geological structure. With the improvement of computer technology and data processing capabilities, researchers realized that three-dimensional seismic data could provide more comprehensive underground information, thus promoting the rapid development of this field. With the emergence of digital recorders, the acquisition of seismic data began to transform into digitalization, making data processing and analysis more efficient. In the 1980s, three-dimensional seismic exploration technology began to be applied in oil and gas exploration. The limitations of using two-dimensional data for inversion calculations prompted the research and development of three-dimensional acquisition technology. With the progress of high-performance computers, data storage technology, and seismic wave propagation theory, three-dimensional seismic data acquisition technology has been further developed. The introduction of multi-channel data acquisition systems, combined with advanced seismic exploration algorithms, has enabled the realization of high-resolution three-dimensional seismic images. At the same time, the progress of three-dimensional inversion technology has made the modeling and analysis of complex geological bodies more accurate. However, in the current existing technologies, pre-earthquake, earthquake, and post-earthquake data are often collected separately and are difficult to effectively integrate. At the same time, the analysis of earthquake waveform data at the epicenter is mostly static, lacking in-depth research on instantaneous dynamics, which has led to relatively low real-time performance and dynamic analysis capabilities in seismic data acquisition. Summary of the Invention

[0003] Based on this, it is necessary to provide a three-dimensional seismic data acquisition method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a three-dimensional seismic data acquisition method, the method includes the following steps: Step S1: Obtain data of the seismic source area; perform real-time multi-source sensing data acquisition on the data of the seismic source area to obtain multi-source sensing data of the seismic source area; perform amplitude conversion on the multi-source sensing data of the seismic source area to generate an amplitude conversion map of the seismic source area; perform pre-earthquake potential seismically vulnerable area positioning on the amplitude conversion map of the seismic source area to generate pre-earthquake seismically vulnerable area positioning data; Step S2: Record the earthquake waveform at the epicenter of the pre-earthquake seismically vulnerable area positioning data to generate epicenter earthquake waveform data of the seismically vulnerable area; perform seismic instantaneous dynamic analysis on the epicenter earthquake waveform data of the seismically vulnerable area to generate instantaneous magnitude data at the epicenter; perform earthquake activity analysis based on the instantaneous magnitude data at the epicenter to generate epicenter earthquake activity information data; perform post-earthquake crustal structure change analysis based on the epicenter earthquake activity information data to generate post-earthquake crustal structure change data; Step S3: Integrate the pre-earthquake earthquake-prone area location data, epicenter seismic activity information data, and post-earthquake crustal structure change data to generate comprehensive earthquake acquisition data; perform three-dimensional regional terrain modeling on the comprehensive earthquake acquisition data to generate earthquake regional terrain modeling data; perform three-dimensional scene construction on the earthquake regional terrain modeling data to generate earthquake three-dimensional scene data; Step S4: Conduct focal mechanism analysis on the earthquake three-dimensional scene data to generate focal mechanism analysis data; predict earthquake disasters on the earthquake three-dimensional scene data through the focal mechanism analysis data to generate earthquake disaster prediction data; construct a disaster data acquisition strategy based on the earthquake disaster prediction data to generate an earthquake disaster data acquisition strategy for performing three-dimensional earthquake disaster data acquisition operations.

[0005] Through the real-time data collection of multi-source sensors, the present invention can quickly obtain various environmental information of the seismic source area, ensuring the timeliness and comprehensiveness of the data. By performing amplitude conversion on the data of the seismic source area, potential seismic activities in the seismic source area can be effectively identified, providing an important basis for subsequent analysis. Through the positioning of potentially seismogenic areas before an earthquake, high-risk areas can be identified in advance, providing a reference basis for earthquake early warning. By recording the seismic waveforms at the epicenter of seismogenic areas, accurate seismic waveform data can be obtained, providing a reliable basis for epicenter analysis. By conducting instantaneous dynamic analysis of earthquakes, instantaneous magnitude data of the epicenter can be quickly generated, thus realizing the real-time monitoring of seismic activities. By analyzing the instantaneous magnitude data of the epicenter, information data on seismic activities at the epicenter can be generated, providing a comprehensive information basis for post-earthquake analysis. The post-earthquake crustal structure change analysis based on the seismic activity information at the epicenter helps to understand the impact of earthquakes on the crust, providing important data for subsequent scientific research and post-disaster assessment. Integrating the pre-earthquake seismogenic area positioning data, the seismic activity information data at the epicenter, and the post-earthquake crustal structure change data to generate comprehensive earthquake acquisition data provides a multi-dimensional analysis perspective. Conducting three-dimensional regional terrain modeling on the comprehensive earthquake acquisition data can generate high-precision terrain data, providing a basis for subsequent earthquake analysis and visualization. The generated three-dimensional earthquake scene data helps researchers intuitively understand seismic activities and their impacts, providing visual support for scientific research and education. By conducting seismic source mechanism analysis on the three-dimensional earthquake scene data, the occurrence mechanism of earthquakes can be deeply understood, providing data support for scientific research. Using the seismic source mechanism analysis data for earthquake disaster prediction can provide more accurate prediction results based on scientific data, enhancing the early warning ability. The disaster data acquisition strategy constructed based on the earthquake disaster prediction data can systematically guide subsequent data acquisition work, optimize resource allocation, and improve data acquisition efficiency. Through the implementation of the above steps, the entire system can effectively enhance the society's disaster prevention and mitigation capabilities, improve the prediction and response capabilities to earthquakes, and reduce the losses caused by earthquakes. Therefore, the present invention improves the real-time performance and dynamic analysis ability of earthquake data acquisition through real-time multi-source sensing data collection, dynamic epicenter analysis, and three-dimensional modeling.

[0006] Preferably, step S1 includes the following steps: Step S11: Obtain the data of the seismic source area; Step S12: Arrange a seismic monitoring network for the data of the seismic source area to obtain a seismic monitoring network for the seismic source area; perform real-time multi-source sensing data collection on the seismic monitoring network for the seismic source area to obtain multi-source sensing data for the seismic source area; Step S13: Perform data preprocessing on the multi-source sensing data for the seismic source area to generate standard multi-source sensing data for the seismic source area, where the data preprocessing includes data cleaning, data denoising, filling of missing data values, and data standardization; Step S14: Perform amplitude conversion on the multi-source sensing data of the standard seismic source area to generate an amplitude conversion map of the seismic source area; identify the seismic wave frequency changes in the amplitude conversion map of the seismic source area to generate seismic wave frequency change data; locate the potentially seismically vulnerable areas before an earthquake in the data of the seismic source area based on the seismic wave frequency change data to generate pre-earthquake seismically vulnerable area location data.

[0007] Through step S11, the present invention obtains the data of the seismic source area, and in step S12, a seismic monitoring network is deployed, which can achieve precise monitoring of the seismic source area and obtain multi-source sensing data. This link ensures the high coverage rate and real-time nature of the monitoring network, and helps to collect high-precision earthquake precursor information. In step S13, data cleaning, denoising, missing value filling, and standardization processing make the multi-source sensing data more standard and reliable, avoiding the interference of noise and errors on subsequent analysis, improving the data quality and consistency, and providing a stable basis for subsequent analysis. Through amplitude conversion and seismic wave frequency change identification (step S14), the seismic wave characteristics can be effectively extracted, and the seismic wave fluctuation law can be accurately identified. This process helps to identify the frequency change trend in the seismic source area, providing key data for seismic analysis. Locating the potentially seismically vulnerable areas before an earthquake based on the seismic wave frequency change data improves the ability to identify high-risk areas before an earthquake, facilitating the early adoption of emergency measures and resource deployment, and enhancing the disaster prevention ability.

[0008] Preferably, step S14 includes the following steps: Step S141: Calculate the root mean square value of the multi-source sensing data of the standard seismic source area to obtain the amplitude value of the seismic source area; map the amplitude value of the seismic source area to a preset GIS for amplitude conversion to generate an amplitude intensity map of the seismic source area; perform color gradient identification on the amplitude intensity map of the seismic source area to generate an amplitude conversion map of the seismic source area; Step S142: Identify the position feature points of the amplitude conversion map of the seismic source area to obtain the position feature points of the seismic source area; perform Fourier transform of the amplitude of the feature points on the amplitude conversion map of the seismic source area based on the position feature points of the seismic source area to generate regional feature point seismic wave spectrum data; Step S143: Analyze the frequency component changes in the regional feature point seismic wave spectrum data to generate regional feature point seismic wave frequency rising curvature data and regional feature point seismic wave frequency falling curvature data, and eliminate the regional feature point seismic wave frequency falling curvature data; use the regional feature point seismic wave frequency rising curvature data to analyze the seismic wave frequency change trend of the regional feature point seismic wave spectrum data to generate seismic wave frequency change data; Step S144: Locate the seismically vulnerable area points of the position feature points of the seismic source area through the seismic wave frequency change data to generate seismically vulnerable area point position data; perform real-world mapping on the seismically vulnerable area point position data to generate seismically vulnerable area point position mapping data; aggregate the seismically vulnerable area point position mapping data to generate pre-earthquake seismically vulnerable area location data.

[0009] Through root mean square value calculation and GIS mapping, the present invention generates a visual amplitude intensity map of the seismic source area, and enhances the intuitiveness of the image using color gradient identification. The amplitude intensity map can clearly show the amplitude changes at different positions, providing basic data support for identifying potential seismic sources. Through the identification of position feature points and Fourier transform, the seismic wave spectrum data of regional feature points are generated. These spectrum data effectively capture the frequency and amplitude characteristics of seismic waves, enabling more accurate detection of subtle changes in seismic wave characteristics in subsequent frequency component analysis. Based on the analysis of frequency component changes, the frequency decline curvature data are excluded, and the trend analysis focuses on the frequency rise curvature data to identify the upward trend of seismic wave frequency. This process helps to capture the change trend of pre-seismic fluctuation characteristics, providing a scientific basis for the early warning of earthquake risks. Using the frequency change data to accurately locate the earthquake-prone area points, and mapping these position data into the actual geographical environment to generate pre-seismic earthquake-prone area location data. This achievement can be used for risk area prediction and resource allocation in practical applications, improving the response speed of disaster prevention. Aggregating the mapped data of earthquake-prone area points into pre-seismic earthquake-prone area location data makes the data more comprehensive and systematic, facilitating regional disaster prediction and emergency decision-making by relevant departments.

[0010] Preferably, step S2 includes the following steps: Step S21: Deploy strong motion instruments for the pre-seismic earthquake-prone area location data to obtain strong motion instrument deployment data for the earthquake-prone area; record the seismic waveforms at the epicenter for the strong motion instrument deployment data in the earthquake-prone area to generate seismic waveform data at the epicenter in the earthquake-prone area; Step S22: Conduct seismic instantaneous dynamic analysis on the seismic waveform data at the epicenter in the earthquake-prone area to generate instantaneous magnitude data at the epicenter; compare the instantaneous magnitude data at the epicenter with a preset warning magnitude threshold. When the instantaneous magnitude data at the epicenter is greater than or equal to the preset warning magnitude threshold, use a drone to conduct seismic surface damage analysis on the pre-seismic earthquake-prone area location data to generate surface damage data at the epicenter; Step S23: When the instantaneous magnitude data at the epicenter is less than the preset warning magnitude threshold, conduct seismic hidden layer damage analysis on the pre-seismic earthquake-prone area location data based on the instantaneous magnitude data at the epicenter to generate hidden layer influence data at the epicenter; integrate the surface damage data at the epicenter and the hidden layer influence data at the epicenter to generate seismic activity information data at the epicenter; Step S24: Conduct post-seismic geological exploration based on the seismic activity information data at the epicenter to generate post-seismic geological exploration data; conduct crustal structure change analysis on the post-seismic geological exploration data to generate post-seismic crustal structure change data.

[0011] Through the deployment of strong motion instruments for the positioning data of earthquake-prone areas before an earthquake, the present invention realizes the intensive monitoring of earthquake-prone areas and obtains high-precision epicenter earthquake waveform data, which makes the capture of the dynamic changes at the epicenter more accurate and helps to detect the fluctuation characteristics of earthquake signals earlier. By comparing the instantaneous magnitude data at the epicenter with the warning magnitude threshold, it is ensured that when the magnitude reaches the warning value, the drone is triggered in time to analyze the surface damage in the earthquake-prone area and generate the epicenter surface damage data. This mechanism enhances the timeliness and intelligence of the early warning system and can respond quickly before or during an earthquake. When the instantaneous magnitude at the epicenter is lower than the warning value, the hidden layer damage analysis is triggered to generate the epicenter hidden layer influence data. The integration with the surface damage data forms the complete epicenter earthquake activity information, enabling a more comprehensive assessment of the geological damage caused by different magnitudes and improving the scientific nature of disaster analysis. Through post-earthquake geological exploration, post-earthquake geological exploration data are obtained, and further analysis of the crustal structure changes is carried out to generate post-earthquake crustal structure change data. This link can evaluate the long-term impact of the earthquake on the regional geology and provide an important reference basis for post-disaster recovery and geological stability assessment. The intelligent process combining the deployment of strong motion instruments, drone monitoring, and data integration provides an automated means for information collection and damage analysis before, during, and after an earthquake, which not only improves the efficiency of earthquake disaster prediction but also provides scientific data support for real-time decision-making and emergency rescue.

[0012] Preferably, step S22 includes the following steps: Step S221: Perform earthquake instantaneous dynamic analysis on the epicenter earthquake waveform data of the earthquake-prone area to generate the instantaneous magnitude data at the epicenter; compare the instantaneous magnitude data at the epicenter with the preset warning magnitude threshold. When the instantaneous magnitude data at the epicenter is greater than or equal to the preset warning magnitude threshold, use the drone to collect multi-perspective images of the earthquake-prone area positioning data before the earthquake to obtain the multi-perspective image set of the earthquake-prone area; Step S222: Identify the surface line shapes of the multi-perspective image set of the earthquake-prone area to obtain the crack identification data of the earthquake-prone area; segment the edge area of the multi-perspective image set of the earthquake-prone area according to the crack identification data of the earthquake-prone area to generate the crack area image of the earthquake-prone area; Step S223: Identify the surface texture features of the crack area image of the earthquake-prone area to obtain the texture feature data of the crack area; perform surface subsidence analysis on the crack area image of the earthquake-prone area through the texture feature data of the crack area to generate the surface subsidence data of the crack area; Step S224: Quantify the damage level of the surface subsidence data of the crack area to generate the epicenter surface damage data.

[0013] The present invention collects multi - perspective images of earthquake - prone areas before an earthquake through an unmanned aerial vehicle (UAV), ensuring comprehensive coverage of surface damage caused by the earthquake. The multi - perspective image set enhances the accuracy of monitoring and provides a rich data source for subsequent crack identification and surface analysis. By identifying the shape of surface lines in the image set, rapid crack identification is achieved, and an image of the crack area is generated. Through edge - area segmentation, the crack areas in the earthquake - prone area are clearly marked, providing accurate positioning data for subsequent analysis and improving the accuracy of damage assessment. By identifying the texture features of the crack - area image and further analyzing the surface settlement through these texture - feature data, not only are surface cracks identified, but also the degree of surface damage is evaluated through settlement analysis, revealing the damage depth and distribution in the crack area. By quantifying the damage level based on the surface - settlement data of the crack area, surface damage data at the earthquake epicenter is generated. The quantified data can be used for subsequent damage - level assessment and resource allocation, helping decision - makers better formulate response measures and effectively supporting post - disaster resource scheduling and priority planning. The design process of the entire step S22 organically combines multi - perspective data collection, crack identification, texture analysis, and damage quantification, realizing systematic surface - damage analysis. Through real - time image collection and automated analysis by the UAV, post - earthquake damage data is quickly provided, saving valuable time for emergency rescue.

[0014] Preferably, step S23 includes the following steps: Step S231: When the instantaneous earthquake magnitude data at the epicenter is less than the preset warning earthquake - magnitude threshold, electromagnetic detection is performed on the pre - earthquake earthquake - prone area positioning data based on the instantaneous earthquake magnitude data at the epicenter, generating an electromagnetic imaging map of the earthquake - prone area at the epicenter; Step S232: Perform tomographic color - system distribution analysis on the electromagnetic imaging map of the earthquake - prone area at the epicenter, generating tomographic color - system distribution data; perform hierarchical electromagnetic - characteristic analysis on the electromagnetic imaging map of the earthquake - prone area at the epicenter according to the tomographic color - system distribution data, generating hierarchical electromagnetic - characteristic analysis data; Step S233: Use the hierarchical electromagnetic - characteristic analysis data to identify electromagnetic reflection bands in the tomographic color - system distribution data, generating an electromagnetic reflection area at the earthquake - prone area at the epicenter; perform electromagnetic - anomaly - band analysis on the electromagnetic reflection area at the earthquake - prone area at the epicenter, thereby generating data on the hidden - layer influence at the epicenter; Step S234: Integrate the surface - damage data at the epicenter and the data on the hidden - layer influence at the epicenter, thereby generating data on earthquake - activity information at the epicenter.

[0015] Through the monitoring of the instantaneous magnitude data of the earthquake epicenter, once it is less than the preset warning magnitude threshold, the electromagnetic detection technology can be used to deeply analyze the seismically vulnerable areas. This method can provide direct information about the formation structure and its physical properties, thus helping scientists better understand the geological structure of the epicenter area. Analyzing the fault color system distribution of the electromagnetic imaging map of the epicenter in the seismically vulnerable area can reveal the distribution characteristics of geological faults and their relationship with seismic activities, which provides a scientific basis for evaluating the geological stability of the area and post-earthquake recovery, and lays a foundation for the optimization of the earthquake early warning system. Based on the fault color system distribution data, hierarchical electromagnetic property analysis can distinguish the electromagnetic response characteristics of different geological layers. This hierarchical analysis helps to identify the geological characteristics at different depths and provides important information for understanding the electromagnetic environment in the epicenter area. Using the hierarchical electromagnetic property analysis data to identify the electromagnetic reflection band, thereby generating the electromagnetic reflection area of the epicenter in the seismically vulnerable area. This identification can effectively reveal potential anomalies in the geological structure and help scientists identify the hidden dangers that trigger earthquakes. Integrating the surface damage data of the epicenter with the hidden layer influence data of the epicenter to generate the earthquake activity information data of the epicenter. This integration can provide a comprehensive view of earthquake activities and help decision-makers make more scientific decisions in post-disaster recovery, rescue, and geological assessment. Through the electromagnetic detection and data integration in step S23, researchers can identify potential earthquake hazard areas in advance, enhance the understanding of future earthquake risks. This forward-looking information can effectively improve the ability of earthquake prevention and mitigation and help enhance the public's safety awareness.

[0016] Preferably, step S24 includes the following steps: Based on the earthquake activity information data of the epicenter, confirm the stable time point of frequency change for the amplitude conversion map of the earthquake source area to obtain the gently changing time point; conduct post-earthquake geological detection according to the gently changing time point to generate post-earthquake geological detection echo data; Calculate the echo time for the post-earthquake geological detection echo data to obtain the echo time data; calculate the surface deformation amplitude for the post-earthquake geological detection echo data according to the echo time data to generate the post-earthquake surface deformation difference map; Conduct time series difference analysis on the post-earthquake surface deformation difference area to generate the post-earthquake crustal structure change data.

[0017] Through the analysis of the seismic activity information data at the epicenter, step S24 can identify the time points with stable frequency changes in the amplitude conversion map of the seismic source area. The confirmation of this time point provides an important time reference for subsequent geological exploration, helping to improve the accuracy and reliability of geological exploration results. After confirming the time points of stable changes, post-earthquake geological exploration is carried out based on this time point, and post-earthquake geological exploration echo data can be generated. This method ensures that the exploration is carried out during a relatively stable period of seismic activity, and can more clearly reflect the response of crustal changes to earthquakes. By calculating the echo time of the post-earthquake geological exploration echo data, the generated echo time data can provide the specific response time of each layer structure of the crust, helping researchers to more accurately understand the impact of earthquakes on underground structures. This information is of great significance for subsequent geological research and post-disaster recovery work. By calculating the surface deformation amplitude of the post-earthquake geological exploration echo data using the echo time data, a post-earthquake surface deformation difference map is generated. This image can clearly show the changes in the post-earthquake surface, providing intuitive visual support for the surface deformation caused by earthquakes. By performing time series difference analysis on the post-earthquake surface deformation difference map, post-earthquake crustal structure change data is generated. This analysis can reveal the impact of earthquakes on long-term and short-term changes in the crust, helping scientists to deeply understand the crustal dynamics after seismic activities. The generation of post-earthquake crustal structure change data provides an important scientific basis for post-disaster reconstruction and recovery work. Decision-makers can formulate reasonable recovery strategies based on this data, optimize resource allocation, and ensure the efficient implementation of post-disaster rescue and reconstruction work.

[0018] Preferably, step S3 includes the following steps: Step S31: Integrate the pre-earthquake earthquake-prone area positioning data, the epicenter seismic activity information data, and the post-earthquake crustal structure change data to generate comprehensive earthquake acquisition data; Step S32: Perform three-dimensional coordinate transformation on the comprehensive earthquake acquisition data to generate comprehensive earthquake coordinate transformation data; perform regional terrain modeling on the comprehensive earthquake coordinate transformation data to generate earthquake regional terrain modeling data; perform terrain detail enhancement on the earthquake regional terrain modeling data to generate earthquake regional terrain enhancement data; Step S33: Spatialize the data of the earthquake regional terrain enhancement data to generate earthquake regional vibration spatialization data, where data spatialization includes vibration data spatialization and displacement data visualization; Step S34: Based on three-dimensional modeling software, construct a three-dimensional scene for the earthquake regional vibration spatialization data to generate earthquake three-dimensional scene data.

[0019] By integrating the pre-earthquake seismic-prone area location data, epicenter seismic activity information data, and post-earthquake crustal structure change data, the generated comprehensive earthquake acquisition data provides a comprehensive information basis for subsequent analysis and processing. This integration not only improves the integrity of the data but also makes the relationships between different data sources clearer, contributing to a comprehensive assessment of the earthquake's impact. The three-dimensional coordinate transformation ensures the unity of data from different sources, enabling the comprehensive earthquake coordinate transformation data to be analyzed in the same coordinate system. The accuracy of this process is crucial for subsequent regional terrain modeling and analysis, effectively avoiding data deviations caused by inconsistent coordinate systems. The earthquake regional terrain modeling data generated by performing regional terrain modeling on the comprehensive earthquake coordinate transformation data provides the necessary terrain background information for the spatial analysis of seismic activities. This modeling can better reflect the propagation characteristics of seismic waves under different terrain conditions, providing a reference for earthquake impact assessment. By enhancing the terrain details of the earthquake regional terrain modeling data, the generated earthquake regional terrain enhancement data can display the subtle changes in the terrain, enabling researchers and decision-makers to more clearly identify the specific impact of the earthquake on the terrain. This detail enhancement helps improve the accuracy of earthquake-related research. Spatializing the earthquake regional terrain enhancement data to generate earthquake regional vibration spatialization data enables visualization from two dimensions: vibration and displacement. This multi-dimensional display method not only enriches the data representation but also enhances users' understanding of the dynamic changes in earthquakes. Based on a three-dimensional modeling software, constructing a three-dimensional scene for the earthquake regional vibration spatialization data generates earthquake three-dimensional scene data, which can intuitively display the earthquake's impact and regional characteristics. This visualization effect not only helps scientists better analyze the data but also provides the public with a more intuitive understanding of the earthquake's impact, enhancing the society's disaster prevention awareness.

[0020] Preferably, step S4 includes the following steps: Step S41: Conduct a focal mechanism analysis on the earthquake three-dimensional scene data to generate focal mechanism analysis data, where the focal mechanism analysis data includes reverse fault mechanism data and normal fault mechanism data; Step S42: Use the reverse fault mechanism data and normal fault mechanism data to perform earthquake pattern recognition on the earthquake three-dimensional scene data to obtain earthquake pattern recognition data; extract earthquake pattern feature data from the earthquake pattern recognition data; Step S43: Divide the earthquake pattern feature data into a dataset to generate a model training set and a model test set; use the long short-term memory neural network algorithm to train the model training set to generate a preliminary earthquake disaster prediction model; optimize and iterate the preliminary earthquake disaster prediction model according to the model test set to generate an earthquake disaster prediction model; Step S44: Import the 3D seismic scene data into the earthquake disaster prediction model for earthquake disaster prediction to generate earthquake disaster prediction data; construct a disaster data collection strategy based on the earthquake disaster prediction data to generate an earthquake disaster data collection strategy for performing 3D earthquake disaster data collection operations.

[0021] Through the analysis of the source mechanism of the 3D seismic scene data, the present invention generates reverse fault mechanism data and normal fault mechanism data. This analysis can not only provide the basic characteristics of the earthquake source, but also reveal the deep-seated reasons for earthquake activities, providing a scientific basis for subsequent disaster assessment and prediction. The 3D seismic scene data is subjected to earthquake pattern recognition using the reverse fault mechanism data and the normal fault mechanism data. This process automatically identifies different earthquake patterns, thereby improving the efficiency and accuracy of the analysis and laying a solid foundation for subsequent feature extraction and model construction. Through feature extraction from the earthquake pattern recognition data, step S42 can extract key earthquake feature data, which is crucial for modeling and prediction. Effective feature extraction can enhance the sensitivity and prediction accuracy of the model to earthquake behavior. The earthquake pattern feature data is divided into a model training set and a model test set, and the long short-term memory (LSTM) neural network algorithm is used for training. This method can effectively process time series data and capture the time-varying characteristics of earthquake activities, thereby enhancing the model's prediction ability for future earthquakes. Based on the generated earthquake disaster prediction pre-model, through the optimization and iteration of the model test set, the accuracy and robustness of the prediction model are further improved. Such an iterative process ensures the adaptability of the model to different earthquake scenarios and can effectively respond to diverse earthquake activities. The 3D seismic scene data is imported into the earthquake disaster prediction model for real-time prediction. This real-time prediction can provide timely information about potential earthquake activities, thereby providing support for emergency response and disaster management and greatly enhancing the early warning ability.

[0022] In this specification, a 3D seismic data collection system is provided for performing the above-mentioned 3D seismic data collection method. The 3D seismic data collection system includes: A pre-earthquake regional positioning module, configured to obtain source region data; perform real-time multi-source sensing data collection on the source region data to obtain source region multi-source sensing data; perform amplitude conversion on the source region multi-source sensing data to generate a source region amplitude conversion map; perform pre-earthquake potential seismogenic region positioning on the source region amplitude conversion map to generate pre-earthquake seismogenic region positioning data; The epicenter post-earthquake analysis module is used to record the epicenter seismic waveforms of the earthquake-prone area location data before the earthquake, and generate the epicenter seismic waveform data of the earthquake-prone area; conduct seismic instantaneous dynamic analysis on the epicenter seismic waveform data of the earthquake-prone area, and generate the instantaneous magnitude data of the epicenter; conduct seismic activity analysis based on the instantaneous magnitude data of the epicenter, so as to generate the epicenter seismic activity information data; conduct post-earthquake crustal structure change analysis based on the epicenter seismic activity information data, and generate the post-earthquake crustal structure change data; The scenario construction module is used to integrate the earthquake-prone area location data before the earthquake, the epicenter seismic activity information data, and the post-earthquake crustal structure change data, and generate the comprehensive earthquake acquisition data; conduct three-dimensional regional terrain modeling on the comprehensive earthquake acquisition data, and generate the earthquake regional terrain modeling data; conduct three-dimensional scenario construction on the earthquake regional terrain modeling data, and generate the earthquake three-dimensional scenario data; The disaster prediction module is used to conduct focal mechanism analysis on the earthquake three-dimensional scenario data, and generate the focal mechanism analysis data; conduct earthquake disaster prediction on the earthquake three-dimensional scenario data through the focal mechanism analysis data, and generate the earthquake disaster prediction data; construct a disaster data acquisition strategy based on the earthquake disaster prediction data, and generate the earthquake disaster data acquisition strategy to execute the three-dimensional earthquake disaster data acquisition operation.

[0023] The beneficial effects of the present invention are as follows: By obtaining data in the seismic source area, the timeliness and accuracy of the data are ensured, providing a reliable information basis for subsequent analysis. Converting the original data into an amplitude map can more clearly identify the seismic characteristics of the seismic source area, helping to locate potential earthquake-prone areas. By identifying earthquake-prone areas before an earthquake, high-risk areas can be identified in advance, providing an important basis for subsequent monitoring and emergency preparedness. Recording the waveform in the epicenter area can obtain detailed seismic activity information, providing the necessary data for analyzing the epicenter situation. By analyzing the instantaneous magnitude, the changing dynamics of seismic activities can be better understood, enhancing the real-time monitoring ability of earthquakes. Analyzing the epicenter activity information to generate post-earthquake crustal change data helps to evaluate the impact of the earthquake on the crustal structure and supports subsequent post-disaster recovery work. Integrating the data at each stage into comprehensive acquisition data can provide a global perspective, making seismic analysis more comprehensive and systematic. Through terrain modeling, a spatial understanding of the earthquake-affected area is provided, helping to identify potential geological risks. The generated three-dimensional scene data is conducive to visual analysis, enabling researchers to more intuitively understand the earthquake impact and propagation path. By conducting seismic source mechanism analysis on the three-dimensional scene data, the occurrence mechanism of earthquakes can be deeply understood, providing a scientific basis for predicting earthquake behavior. The prediction data generated based on the seismic source mechanism analysis can more accurately evaluate the potential impact of earthquakes and enhance the effectiveness of emergency response. By formulating a scientific acquisition strategy, effective data acquisition and analysis can be ensured after an earthquake, enhancing the ability of post-disaster recovery and emergency management. Therefore, the present invention improves the real-time performance and dynamic analysis ability of seismic data acquisition through real-time multi-source sensing data acquisition, dynamic epicenter analysis, and three-dimensional modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic flow chart of the steps of a three-dimensional seismic data acquisition method; Figure 2 is Figure 1 a detailed implementation step flow chart of step S2 in Figure 3 is Figure 1 a detailed implementation step flow chart of step S3 in Figure 4 is Figure 1 a detailed implementation step flow chart of step S4 in The realization, functional characteristics, and advantages of the object of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The technical method of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0028] To achieve the above object, please refer to Figures 1 to 4 , a three-dimensional seismic data acquisition method, the method comprising the following steps: Step S1: Obtain data of the seismic source area; perform real-time multi-source sensing data acquisition on the data of the seismic source area to obtain multi-source sensing data of the seismic source area; perform amplitude conversion on the multi-source sensing data of the seismic source area to generate an amplitude conversion map of the seismic source area; perform pre-earthquake potential seismically vulnerable area location on the amplitude conversion map of the seismic source area to generate pre-earthquake seismically vulnerable area location data; Step S2: Perform seismic waveform recording at the epicenter of the pre-earthquake seismically vulnerable area location data to generate seismic waveform data at the epicenter of the seismically vulnerable area; perform seismic instantaneous dynamic analysis on the seismic waveform data at the epicenter of the seismically vulnerable area to generate instantaneous magnitude data at the epicenter; perform seismic activity analysis based on the instantaneous magnitude data at the epicenter to generate seismic activity information data at the epicenter; perform post-earthquake crustal structure change analysis based on the seismic activity information data at the epicenter to generate post-earthquake crustal structure change data; Step S3: Integrate the pre-earthquake earthquake-prone area positioning data, epicenter seismic activity information data, and post-earthquake crustal structure change data to generate comprehensive earthquake acquisition data; perform three-dimensional regional terrain modeling on the comprehensive earthquake acquisition data to generate earthquake regional terrain modeling data; perform three-dimensional scene construction on the earthquake regional terrain modeling data to generate earthquake three-dimensional scene data; Step S4: Conduct focal mechanism analysis on the earthquake three-dimensional scene data to generate focal mechanism analysis data; perform earthquake disaster prediction on the earthquake three-dimensional scene data through the focal mechanism analysis data to generate earthquake disaster prediction data; construct a disaster data acquisition strategy based on the earthquake disaster prediction data to generate an earthquake disaster data acquisition strategy for executing three-dimensional earthquake disaster data acquisition operations.

[0029] Through the real-time data collection of multi-source sensors, the present invention can quickly obtain various environmental information of the seismic source area, ensuring the timeliness and comprehensiveness of the data. By performing amplitude conversion on the data of the seismic source area, potential seismic activities in the seismic source area can be effectively identified, providing an important basis for subsequent analysis. Through the pre-earthquake positioning of potentially seismogenic areas, high-risk areas can be identified in advance, providing a reference basis for earthquake early warning. By recording the seismic waveforms at the epicenter of seismogenic areas, accurate seismic waveform data can be obtained, providing a reliable basis for epicenter analysis. Conducting instantaneous dynamic analysis of earthquakes can quickly generate instantaneous magnitude data at the epicenter, thereby realizing real-time monitoring of seismic activities. By analyzing the instantaneous magnitude data at the epicenter, information data on seismic activities at the epicenter can be generated, providing a comprehensive information basis for post-earthquake analysis. The post-earthquake crustal structure change analysis based on the seismic activity information at the epicenter helps to understand the impact of earthquakes on the crust, providing important data for subsequent scientific research and post-disaster assessment. Integrating the pre-earthquake seismogenic area positioning data, the seismic activity information data at the epicenter, and the post-earthquake crustal structure change data to generate comprehensive seismic acquisition data provides a multi-dimensional analysis perspective. Conducting three-dimensional regional terrain modeling on the comprehensive seismic acquisition data can generate high-precision terrain data, providing a basis for subsequent seismic analysis and visualization. The generated three-dimensional seismic scene data helps researchers intuitively understand seismic activities and their impacts, providing visual support for scientific research and education. By performing seismic source mechanism analysis on the three-dimensional seismic scene data, the occurrence mechanism of earthquakes can be deeply understood, providing data support for scientific research. Using the seismic source mechanism analysis data for earthquake disaster prediction can provide more accurate prediction results based on scientific data, enhancing the early warning ability. The disaster data collection strategy constructed based on the earthquake disaster prediction data can systematically guide subsequent data collection work, optimize resource allocation, and improve data collection efficiency. Through the implementation of the above steps, the entire system can effectively enhance the society's disaster prevention and mitigation capabilities, improve the prediction and response capabilities to earthquakes, and reduce the losses caused by earthquakes. Therefore, through real-time multi-source sensing data collection, dynamic epicenter analysis, and three-dimensional modeling, the present invention improves the real-time performance and dynamic analysis ability of seismic data collection.

[0030] In the embodiment of the present invention, referring to Figure 1 as described, it is a schematic flowchart of the steps of a three-dimensional seismic data collection method of the present invention. In this example, the three-dimensional seismic data collection method includes the following steps: Step S1: Obtain the data of the seismic source area; perform real-time multi-source sensing data collection on the data of the seismic source area to obtain multi-source sensing data of the seismic source area; perform amplitude conversion on the multi-source sensing data of the seismic source area to generate an amplitude conversion map of the seismic source area; perform pre-earthquake positioning of potentially seismogenic areas on the amplitude conversion map of the seismic source area to generate pre-earthquake seismogenic area positioning data; In the embodiments of the present invention, the geographical scope and relevant geological features of the seismic source area are determined. Using seismic historical data, geological investigation reports, and existing seismic monitoring network data, a preliminary data set of the seismic source area is constructed. Seismic monitoring instruments (such as strong motion instruments, accelerometers, etc.) and remote sensing technologies (such as satellite images, UAV images) are used to obtain basic data of the seismic source area, including topography, soil characteristics, and underground structure information. A variety of sensors are deployed in the seismic source area, including seismic wave sensors, meteorological sensors, soil moisture sensors, etc., to form a multi-source sensor network to collect data of the seismic source area in real time. Wireless sensor network (WSN) or Internet of Things (IoT) technology is used to realize real-time monitoring and transmission of the data collected by the sensors to ensure the timeliness and accuracy of the data. The data from different sensors are integrated to generate a multi-source sensing data set of the seismic source area, including various environmental parameters and vibration signals. The amplitude conversion of the multi-source sensing data in the seismic source area is carried out. Usually, methods such as Fourier transform or wavelet transform are used to convert the time-domain signal into a frequency-domain signal, so as to extract the amplitude characteristics of the vibration signal. Through visual processing of the converted data, an amplitude conversion map of the seismic source area is generated, which shows the amplitude distribution at different frequencies and provides basic data for subsequent analysis. According to the amplitude conversion map of the seismic source area, the change characteristics of the amplitude are analyzed, and the areas with larger amplitudes are identified as potential seismically vulnerable areas. This step involves spatial clustering algorithms (such as K-means clustering, DBSCAN, etc.) to extract high-amplitude areas. According to the identified seismically vulnerable areas, pre-earthquake seismically vulnerable area location data are generated, which details the geographical coordinates and relevant characteristics of these areas and provides a basis for subsequent monitoring and early warning work.

[0031] Step S2: Record the seismic waveforms at the epicenter of the pre-earthquake seismically vulnerable area location data to generate seismic waveform data at the epicenter of the seismically vulnerable area; conduct seismic instantaneous dynamic analysis on the seismic waveform data at the epicenter of the seismically vulnerable area to generate instantaneous magnitude data at the epicenter; conduct seismic activity analysis based on the instantaneous magnitude data at the epicenter to generate seismic activity information data at the epicenter; conduct post-earthquake crustal structure change analysis based on the seismic activity information data at the epicenter to generate post-earthquake crustal structure change data; In the embodiments of the present invention, by deploying high-precision strong motion seismographs and accelerometers within the earthquake-prone areas before an earthquake, it is ensured that the seismic waveforms occurring at the epicenter can be effectively captured. When configuring the devices, the sensitivity and sampling frequency of the devices should be considered to meet the recording requirements of high-frequency seismic signals. During an earthquake, the seismic waveforms at the epicenter are recorded in real time to generate the seismic waveform data of the epicenter in the earthquake-prone areas. These data should include the records of P-waves, S-waves and subsequent surface waves to ensure that complete seismic waveform information is covered. Through the wireless communication network, the recorded waveform data is transmitted to the central database for storage and subsequent analysis to ensure the security and traceability of the data. The recorded seismic waveform data at the epicenter is denoised to eliminate environmental noise and other interference signals. Common denoising techniques include wavelet denoising, filtering, etc. The instantaneous magnitude analysis algorithm (such as Hilbert transform or short-time Fourier transform) is used to analyze the cleaned seismic waveform data to generate the instantaneous magnitude data at the epicenter, which can reflect the intensity change of seismic activities. According to the instantaneous magnitude data at the epicenter, seismic activity analysis is carried out, and this process includes determining parameters such as the maximum magnitude, epicenter location, epicenter depth, etc. The analysis results are integrated to form the seismic activity information data at the epicenter, including the epicenter magnitude, focal mechanism, epicenter coordinates and time information, etc., for subsequent use. Geological and seismic history data related to the seismic activity information data at the epicenter are collected to provide necessary background information for subsequent analysis. The crustal deformation monitoring technology (such as GPS monitoring, seismic inversion technology) is used to analyze the changes in the crustal structure after the earthquake. By comparing the data before and after the earthquake, the changes in the crust, including the uplift, displacement and strain of the crust, are identified and quantified. According to the analysis results, the data on the changes in the crustal structure after the earthquake are formed to describe the deformation characteristics of the crust after the earthquake and its impact on seismic activities.

[0032] Step S3: Integrate the pre-earthquake earthquake-prone area positioning data, the seismic activity information data at the epicenter, and the data on the changes in the crustal structure after the earthquake to generate comprehensive earthquake acquisition data; perform three-dimensional regional terrain modeling on the comprehensive earthquake acquisition data to generate earthquake regional terrain modeling data; perform three-dimensional scene construction on the earthquake regional terrain modeling data to generate earthquake three-dimensional scene data; In the embodiments of the present invention, by verifying the sources of the pre-earthquake seismically vulnerable area location data, epicenter seismic activity information data, and post-earthquake crustal structure change data, the accuracy and integrity of the data are ensured. These data come from different sensors and measurement devices. Standardize the data from different sources to ensure that all data have a consistent format and unit. For example, convert all coordinate data to a unified geographic coordinate system (such as WGS84) for subsequent integration. Use data integration algorithms (such as weighted average method, least squares method, etc.) to fuse the pre-earthquake, epicenter, and post-earthquake data to generate comprehensive seismic acquisition data. During the integration process, consider the importance and reliability of the data to ensure that the fused data can reflect the real seismic activity situation. According to the integrated comprehensive seismic acquisition data, select a suitable 3D modeling tool (such as ArcGIS, QGIS, SketchUp, etc.) for terrain modeling. The input data includes elevation data, geological data, and seismic activity data. Use terrain generation algorithms (such as Delaunay triangulation, Kriging interpolation, etc.) to generate a 3D regional terrain model. Ensure that the terrain model can reflect the actual terrain characteristics of the pre-earthquake seismically vulnerable area and the post-earthquake crustal changes. Perform detail enhancement processing on the generated terrain model, including adding surface features (such as mountains, rivers, buildings, etc.) and updating the terrain information according to the post-earthquake changes to improve the realism and visualization effect of the model. Select a suitable 3D scene construction software (such as Blender, Unity, Unreal Engine, etc.), and import the generated 3D regional terrain modeling data into the software for scene construction. Integrate other relevant elements in the 3D scene, such as the positions of seismic instruments, the epicenter position, the road network, etc., for visual display and analysis. Reasonably layout and label these elements for subsequent interaction and analysis. Add dynamic effects and interactive functions to the 3D scene, such as seismic wave propagation effects, crustal deformation animations, etc., for users to perform real-time scene observation and analysis. Ensure that the interactivity of the scene can support the needs of different users, such as professional analysis, public education, etc. Export the final 3D scene data in a format suitable for display and analysis (such as OBJ, FBX, GLTF, etc.). Ensure that the exported data can be displayed and interacted on multiple platforms. Utilize the generated 3D seismic scene data for subsequent analysis and applications, such as simulating earthquake impact assessment, public education and training, decision support, etc. Through real-time analysis of the 3D scene, enhance the understanding and response ability to seismic activities.

[0033] Step S4: Conduct focal mechanism analysis on the 3D seismic scene data to generate focal mechanism analysis data; perform earthquake disaster prediction on the 3D seismic scene data through the focal mechanism analysis data to generate earthquake disaster prediction data; construct a disaster data acquisition strategy based on the earthquake disaster prediction data to generate an earthquake disaster data acquisition strategy for performing 3D earthquake disaster data acquisition operations.

[0034] In the embodiments of the present invention, by collecting historical earthquake records, strain gauge data, accelerometer data and other relevant earthquake data in the seismic source area, these data will serve as the basis for seismic source mechanism analysis. Select a suitable seismic source mechanism analysis method (such as the inverse problem method, source mechanism inversion, etc.). According to the available data types and quantities, select the corresponding mathematical model to model the seismic source mechanism. Use the seismic source mechanism inversion algorithm (such as the Moment Tensor inversion method) to analyze the three-dimensional seismic scene data, identify the seismic source characteristics (such as seismic source depth, epicenter location, fault type, seismic source intensity, etc.), and generate seismic source mechanism analysis data, including reverse fault mechanism data and normal fault mechanism data. These data will provide important information for subsequent disaster prediction. Based on the seismic source mechanism analysis data, establish an earthquake disaster prediction model. Machine learning algorithms (such as random forest, support vector machine, etc.) or physical models (such as finite element analysis) can be used for disaster prediction. Use historical earthquake disaster data to train the prediction model and verify the accuracy and reliability of the model. The model should be able to predict the potential disaster impact range, intensity and consequences based on the seismic source mechanism analysis data. Apply the trained model to the three-dimensional seismic scene data for disaster prediction and generate earthquake disaster prediction data. This data should include information such as potential impact areas, affected population, and infrastructure damage assessment. Based on the earthquake disaster prediction data, analyze the data types and priorities that need to be collected after the disaster occurs. Considering the characteristics of different affected areas, determine the key points and methods of data collection. Develop a detailed disaster data collection strategy, including the objectives, time frame, collection methods (such as drone aerial photography, ground surveys, sensor monitoring, etc.) and resource allocation of data collection. Based on the earthquake disaster prediction data, optimize the data collection strategy to ensure that the most important data can be obtained in the shortest time to support post-disaster response and decision-making. Sort out and summarize the developed collection strategy to generate a formal earthquake disaster data collection strategy document. This document should include specific implementation steps, responsibility assignments and emergency contact methods for quick implementation after an earthquake occurs. Conduct implementation preparations before the earthquake disaster occurs, including personnel training, equipment commissioning, resource allocation, etc., to ensure that data collection work can be carried out quickly in case of an emergency. According to the developed disaster data collection strategy, initiate the three-dimensional earthquake disaster data collection operation. Use suitable equipment (such as remote sensing satellites, drones, ground sensors, etc.) for data collection. After the collection is completed, process and analyze the data to extract key information for subsequent earthquake disaster assessment, recovery planning and public safety management.

[0035] Preferably, step S1 includes the following steps: Step S11: Obtain the data in the seismic source area; Step S12: Arrange a seismic monitoring network for the data of the seismic source area to obtain a seismic monitoring network for the seismic source area; collect real-time multi-source sensing data from the seismic monitoring network for the seismic source area to obtain multi-source sensing data for the seismic source area. Step S13: Perform data preprocessing on the multi-source sensing data for the seismic source area to generate standard multi-source sensing data for the seismic source area, where the data preprocessing includes data cleaning, data denoising, filling of missing data values, and data standardization. Step S14: Perform amplitude conversion on the standard multi-source sensing data for the seismic source area to generate an amplitude conversion map for the seismic source area; identify the change in seismic wave frequency for the amplitude conversion map of the seismic source area to generate seismic wave frequency change data; locate the potentially seismically vulnerable area before an earthquake for the data of the seismic source area based on the seismic wave frequency change data to generate pre-earthquake seismically vulnerable area location data.

[0036] In the embodiment of the present invention, by determining the seismic source area to be monitored, geological, geographical, and historical earthquake activity data are collected. Relevant data of the seismic source area are collected, including: geological exploration reports, historical earthquake event records, relevant satellite images, and topographic data. All the obtained data of the seismic source area are integrated to form a complete data set for subsequent processing and analysis. According to the geological characteristics and historical earthquake data of the seismic source area, a layout plan for the seismic monitoring network is designed to determine the number, type, and layout of sensors. Multiple types of sensors are installed in the seismic source area, including: seismic sensors (such as accelerometers, velocimeters), ground settlement monitors, temperature and humidity sensors (monitoring environmental conditions). Configure the real-time data acquisition system of the monitoring network to ensure that multi-source sensor data can be collected in real time. Clean the multi-source sensing data for the seismic source area collected to remove duplicates, invalid values, and outliers. Apply filtering techniques (such as high-pass filtering, low-pass filtering) to remove noise in the signal to improve data quality. For missing data, use appropriate filling methods (such as mean filling, interpolation method, etc.) to supplement it to ensure data integrity. Standardize the data of all sensors to make them comparable for subsequent analysis. After the above processing, "standard multi-source sensing data for the seismic source area" is generated. Perform amplitude conversion on the standard multi-source sensing data for the seismic source area to extract the amplitude information of the seismic signal and generate an "amplitude conversion map for the seismic source area". Perform frequency analysis on the amplitude conversion map of the seismic source area to identify the change in seismic wave frequency and generate "seismic wave frequency change data". Methods such as the fast Fourier transform (FFT) can be used for frequency analysis. Based on the seismic wave frequency change data and combined with the historical earthquake activity pattern, locate the potentially seismically vulnerable area before an earthquake to generate "pre-earthquake seismically vulnerable area location data". By identifying the correlation between the change in seismic wave frequency and earthquake activity, the seismically vulnerable area can be determined.

[0037] Preferably, step S14 includes the following steps: Step S141: Calculate the root mean square value of the multi-source sensing data in the standard seismic source area to obtain the amplitude value of the seismic source area; map the amplitude value of the seismic source area to a preset GIS for amplitude conversion to generate an amplitude intensity map of the seismic source area; perform color gradient identification on the amplitude intensity map of the seismic source area to generate an amplitude conversion map of the seismic source area; Step S142: Identify the position feature points of the amplitude conversion map of the seismic source area to obtain the position feature points of the seismic source area; perform Fourier transform of the amplitude of the feature points on the amplitude conversion map of the seismic source area based on the position feature points of the seismic source area to generate the seismic wave frequency spectrum data of the regional feature points; Step S143: Analyze the frequency component changes of the seismic wave frequency spectrum data of the regional feature points to generate the rising curvature data of the seismic wave frequency of the regional feature points and the falling curvature data of the seismic wave frequency of the regional feature points, and eliminate the falling curvature data of the seismic wave frequency of the regional feature points; use the rising curvature data of the seismic wave frequency of the regional feature points to analyze the changing trend of the seismic wave frequency of the seismic wave frequency spectrum data of the regional feature points to generate the seismic wave frequency change data; Step S144: Locate the earthquake-prone area points of the position feature points of the seismic source area through the seismic wave frequency change data to generate the position data of the earthquake-prone area points; perform real-world mapping on the position data of the earthquake-prone area points to generate the position mapping data of the earthquake-prone area points; aggregate the data points of the position mapping data of the earthquake-prone area points to generate the pre-earthquake earthquake-prone area location data.

[0038] In the embodiment of the present invention, the amplitude value of the seismic source area is obtained by calculating the root mean square (RMS) value of the multi-source sensing data in the standard seismic source area. The formula is: Where are the respective data points of the multi-source sensing data in the seismic source area, is denoted as the total number of data points. The obtained amplitude values of the seismic source area are mapped into a preset Geographic Information System (GIS) for amplitude conversion to generate an amplitude intensity map of the seismic source area. A color gradient identifier is applied to the amplitude intensity map of the seismic source area, and different colors are assigned to different areas according to the magnitude of the amplitude intensity, thereby generating an amplitude conversion map of the seismic source area. Position feature points of the amplitude conversion map of the seismic source area are identified, and position feature points of the seismic source area are obtained using a feature point detection algorithm (such as Harris corner detection or SIFT algorithm). Based on the identified position feature points of the seismic source area, a Fourier transform of the amplitude of the feature points is performed on the amplitude conversion map to generate seismic wave frequency spectrum data of the regional feature points, and this process can be achieved through a Fast Fourier Transform (FFT). Frequency component change analysis is performed on the seismic wave frequency spectrum data of the regional feature points, and frequency rising curvature and falling curvature data are extracted. The calculation of the frequency rising curvature can be obtained by the method of the second derivative. The falling curvature data is eliminated, and only the rising curvature data is retained. Using the seismic wave frequency rising curvature data of the regional feature points, a seismic wave frequency change trend analysis is performed on the seismic wave frequency spectrum data to generate seismic wave frequency change data to capture the change trend of the seismic wave frequency. According to the seismic wave frequency change data, earthquake-prone area point positioning is performed on the position feature points of the seismic source area to generate position data of the earthquake-prone area points. The position data of the earthquake-prone area points is mapped to reality for visualization in the actual geographical environment to generate position mapping data of the earthquake-prone area points. The position mapping data of the earthquake-prone area points is aggregated, summarized, and processed, and finally earthquake-prone area positioning data before an earthquake is generated to provide a reference for subsequent earthquake early warning and monitoring.

[0039] As an example of the present invention, refer to Figure 2 shown, in this example, step S2 includes: Step S21: Deploy strong motion seismographs for the earthquake-prone area positioning data before an earthquake to obtain strong motion seismograph deployment data for the earthquake-prone area; record the seismic waveforms at the epicenter for the strong motion seismograph deployment data for the earthquake-prone area to generate seismic waveform data at the epicenter of the earthquake-prone area; Step S22: Perform seismic instantaneous dynamic analysis on the seismic waveform data at the epicenter of the earthquake-prone area to generate instantaneous magnitude data at the epicenter; compare the instantaneous magnitude data at the epicenter with a preset warning magnitude threshold. When the instantaneous magnitude data at the epicenter is greater than or equal to the preset warning magnitude threshold, use an unmanned aerial vehicle to perform seismic surface damage analysis on the earthquake-prone area positioning data before an earthquake to generate surface damage data at the epicenter; Step S23: When the instantaneous magnitude data at the epicenter is less than the preset warning magnitude threshold, perform seismic hidden layer damage analysis on the earthquake-prone area positioning data before an earthquake based on the instantaneous magnitude data at the epicenter to generate hidden layer influence data at the epicenter; integrate the surface damage data at the epicenter and the hidden layer influence data at the epicenter to generate seismic activity information data at the epicenter; Step S24: Conduct post-earthquake geological exploration based on the earthquake activity information data of the epicenter to generate post-earthquake geological exploration data; analyze the crustal structure changes of the post-earthquake geological exploration data to generate post-earthquake crustal structure change data.

[0040] In the embodiments of the present invention, based on the pre-earthquake earthquake-prone area positioning data in the early stage, a suitable geographical location is selected for the deployment of strong motion seismographs. In the earthquake-prone area, multiple strong motion seismographs are installed according to scientific layout principles to ensure coverage of all important points in the earthquake-prone area for comprehensive monitoring of earthquake activities. After the deployment of the strong motion seismographs is completed, the earthquake waveform data generated at the epicenter is recorded in real time to ensure the integrity and continuity of data collection. This can be achieved by storing the real-time waveform data in a database through a data acquisition system (such as a data recorder). The earthquake waveform data of the epicenter in the earthquake-prone area is dynamically analyzed, and the instantaneous magnitude data of the epicenter is extracted using time-frequency analysis methods (such as wavelet transform or short-time Fourier transform). The specific calculation can use the peak method of the epicenter waveform or other related algorithms. The analysis results are sorted and the instantaneous magnitude data of the epicenter is generated to reflect the intensity and dynamic changes of earthquake activities. A warning magnitude threshold is set according to past earthquake data and experience, usually combined with historical earthquake records or relevant specifications. The instantaneous magnitude data of the epicenter is compared with the preset warning magnitude threshold. When the instantaneous magnitude data of the epicenter is greater than or equal to the preset threshold, the earthquake surface damage analysis program is started. A drone is used to conduct surface damage analysis on the pre-earthquake earthquake-prone area, and the surface damage data of the epicenter is recorded, including cracks, ground settlement, and other significant damage characteristics. If the instantaneous magnitude data of the epicenter is less than the preset warning magnitude threshold, hidden layer damage analysis is performed. Using the instantaneous magnitude data of the epicenter and combining the propagation characteristics of seismic waves, the damage caused by the hidden layer at the epicenter is analyzed to generate the influence data of the hidden layer at the epicenter, which can be achieved through numerical simulation methods or model-based analysis. The surface damage data of the epicenter and the influence data of the hidden layer at the epicenter are integrated to form the earthquake activity information data of the epicenter, providing a basis for subsequent decision-making. Based on the earthquake activity information data of the epicenter, a post-earthquake geological exploration plan is formulated to determine the exploration area and methods (such as geological drilling, seismic exploration, etc.). The geological exploration is implemented, relevant data is collected, and post-earthquake geological exploration data is generated, including geological structure, groundwater level, soil properties, etc. The post-earthquake geological exploration data is analyzed to study the impact of the earthquake on the crustal structure and generate post-earthquake crustal structure change data.

[0041] Preferably, step S22 includes the following steps: Step S221: Conduct earthquake instantaneous dynamic analysis on the earthquake waveform data of the epicenter in the earthquake-prone area to generate the instantaneous magnitude data of the epicenter; compare the instantaneous magnitude data of the epicenter with the preset warning magnitude threshold. When the instantaneous magnitude data of the epicenter is greater than or equal to the preset warning magnitude threshold, a multi-view image collection is performed on the pre-earthquake earthquake-prone area positioning data using a drone to obtain a multi-view image set of the earthquake-prone area; Step S222: Identify the surface line shapes in the multi - perspective image set of the earthquake - prone area to obtain the crack identification data of the earthquake - prone area; segment the edge area of the multi - perspective image set of the earthquake - prone area according to the crack identification data of the earthquake - prone area to generate the crack area image of the earthquake - prone area; Step S223: Identify the surface texture features of the crack area image of the earthquake - prone area to obtain the texture feature data of the crack area; conduct surface subsidence analysis on the crack area image of the earthquake - prone area through the texture feature data of the crack area to generate the surface subsidence data of the crack area; Step S224: Quantify the damage level of the surface subsidence data of the crack area to generate the surface damage data of the earthquake epicenter.

[0042] In the embodiment of the present invention, through the instantaneous dynamic analysis of the recorded seismic waveform data at the earthquake epicenter of the earthquake - prone area. Time - frequency analysis techniques such as short - time Fourier transform or wavelet transform can be used to extract the instantaneous features of the seismic waveform. Based on the extracted instantaneous features, calculate the instantaneous magnitude data of the earthquake epicenter, which reflects the intensity and dynamic changes of the earthquake. Set the magnitude threshold for early warning, usually based on historical data and seismic risk assessment. Compare the calculated instantaneous magnitude data of the earthquake epicenter with the preset warning magnitude threshold. When the instantaneous magnitude data is greater than or equal to the warning magnitude threshold, start the subsequent UAV image acquisition. Use the UAV to perform multi - perspective image acquisition on the pre - earthquake positioning data of the earthquake - prone area to obtain a multi - dimensional image set of the earthquake - prone area. The image acquisition should cover different perspectives and heights to ensure the comprehensiveness of the data. Perform image processing on the multi - perspective image set of the earthquake - prone area, use edge detection algorithms (such as Canny algorithm) to identify the surface line shapes, extract crack information, generate the crack identification data of the earthquake - prone area, and mark the crack positions and shapes. Segment the edge area of the multi - perspective image set according to the crack identification data to highlight the crack area and generate the crack area image of the earthquake - prone area. This process can be achieved using the watershed algorithm or region - growing algorithm. Identify the surface texture features of the crack area image, use texture analysis methods (such as gray - level co - occurrence matrix or LBP features) to extract the texture feature data of the crack area, generate the texture feature data of the crack area, and describe the fine - structure features of the crack. Based on the texture feature data of the crack area, conduct surface subsidence analysis, which can be realized by comparing image data at different time periods or using a three - dimensional reconstruction model, generate the surface subsidence data of the crack area, and reflect the post - earthquake deformation of the surface. According to the surface subsidence data of the crack area, apply damage assessment criteria (such as earthquake damage level criteria) to quantitatively evaluate the damage, determine the damage level of the crack, generate the surface damage data of the earthquake epicenter, and record the damage degree and its impact on the surrounding environment.

[0043] Preferably, step S23 includes the following steps: Step S231: When the instantaneous epicenter magnitude data is less than the preset warning magnitude threshold, electromagnetic detection is performed on the pre-earthquake seismically vulnerable area location data based on the instantaneous epicenter magnitude data to generate an electromagnetic imaging map of the epicenter of the seismically vulnerable area; Step S232: Perform tomographic color system distribution analysis on the electromagnetic imaging map of the epicenter of the seismically vulnerable area to generate tomographic color system distribution data; perform hierarchical electromagnetic characteristic analysis on the electromagnetic imaging map of the epicenter of the seismically vulnerable area according to the tomographic color system distribution data to generate hierarchical electromagnetic characteristic analysis data; Step S233: Use the hierarchical electromagnetic characteristic analysis data to identify electromagnetic reflection bands in the tomographic color system distribution data to generate an electromagnetic reflection area at the epicenter of the seismically vulnerable area; perform electromagnetic anomaly band analysis on the electromagnetic reflection area at the epicenter of the seismically vulnerable area to generate influence data of the hidden layer at the epicenter; Step S234: Integrate the surface damage data at the epicenter and the influence data of the hidden layer at the epicenter to generate earthquake activity information data at the epicenter.

[0044] In the embodiment of the present invention, when the instantaneous epicenter magnitude data is less than the preset warning magnitude threshold, electromagnetic detection work is carried out based on the instantaneous epicenter magnitude data, which can be performed by setting electromagnetic detectors (such as earth resistivity meters, geomagnetic meters) in seismically vulnerable areas. The collected electromagnetic detection data is processed through signal processing and imaging algorithms (such as inversion algorithms) to generate an electromagnetic imaging map of the epicenter of the seismically vulnerable area, which reflects the distribution of electromagnetic characteristics of the strata. Analyze the electromagnetic imaging map of the epicenter of the seismically vulnerable area, extract the color system distribution information of the fault, and use image processing techniques (such as threshold segmentation, color clustering) to process the image to generate tomographic color system distribution data. According to the extracted tomographic color system distribution data, further perform hierarchical electromagnetic characteristic analysis on the electromagnetic imaging map of the epicenter to generate hierarchical electromagnetic characteristic analysis data, reflecting the electromagnetic response characteristics of different layers. Use the hierarchical electromagnetic characteristic analysis data to identify electromagnetic reflection bands in the tomographic color system distribution data to generate an electromagnetic reflection area at the epicenter of the seismically vulnerable area. This process can be carried out by finding the reflection peaks of electromagnetic signals. Perform electromagnetic anomaly band analysis on the identified electromagnetic reflection area to identify abnormal signal areas, and further analyze its influence on the hidden layer at the epicenter to generate influence data of the hidden layer at the epicenter. Integrate the surface damage data at the epicenter and the influence data of the hidden layer at the epicenter. Adopt data fusion technology to comprehensively combine the information of the two to fully reflect the earthquake activity in the epicenter area. The integrated data forms earthquake activity information data at the epicenter, including the damage situation and hidden layer influence in the epicenter area, providing an important basis for earthquake risk and impact assessment.

[0045] Preferably, step S24 includes the following steps: Based on the earthquake activity information data at the epicenter, confirm the steady time points of frequency change for the amplitude conversion map of the source region to obtain the gently changing time points; conduct post-earthquake geological exploration according to the gently changing time points to generate post-earthquake geological exploration echo data; Calculate the echo time for the post-earthquake geological exploration echo data to obtain echo time data; calculate the surface deformation amplitude for the post-earthquake geological exploration echo data according to the echo time data to generate a post-earthquake surface deformation difference map; Conduct time series difference analysis on the post-earthquake surface deformation difference to generate post-earthquake crustal structure change data.

[0046] In the embodiments of the present invention, by using the earthquake activity information data at the epicenter, analyze the frequency change of the amplitude conversion map of the source region. Adopt signal processing methods (such as Fourier transform or wavelet transform) to identify the steady time points of frequency change. By identifying the trend and fluctuation of the frequency change, determine the gently changing time points of post-earthquake geological exploration, and these time points indicate the stable state after the earthquake. According to the gently changing time points, conduct post-earthquake geological exploration on the source region, which can collect data by exciting ground or underground waves (such as seismic waves, ultrasonic waves, etc.). Record the echo signals generated during the exploration process to form post-earthquake geological exploration echo data, and these data contain the reflection information of the underground structure. Calculate the echo time for the post-earthquake geological exploration echo data, analyze the arrival time of the echo signals, and this process usually involves signal processing techniques such as peak detection and time delay estimation to determine the precise arrival moments of each echo. Organize the calculation results into echo time data, providing information about the reflection characteristics and layer depth of the underground structure. According to the echo time data, calculate the surface deformation amplitude for the post-earthquake geological exploration echo data, which usually involves calculating the displacement change of the surface by using the reflection wave amplitude and time difference to generate a post-earthquake surface deformation difference map. The formed deformation difference map provides the deformation amplitude distribution information of different regions on the post-earthquake surface, reflecting the situation of crustal movement. Conduct time series difference analysis on the post-earthquake surface deformation difference map to analyze the change of the deformation amplitude over time. This process can adopt interpolation techniques (such as linear interpolation, spline interpolation, etc.) to smooth the change and fill the gaps between data. Through time series difference analysis, generate post-earthquake crustal structure change data, which summarizes the trend and characteristics of crustal structure change and provides important reference for subsequent earthquake research and early warning.

[0047] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: Integrate the pre-earthquake earthquake-prone area location data, the earthquake activity information data at the epicenter, and the post-earthquake crustal structure change data to generate comprehensive earthquake acquisition data; Step S32: Perform three-dimensional coordinate transformation on the comprehensive seismic acquisition data to generate comprehensive seismic coordinate transformation data; perform regional terrain modeling on the comprehensive seismic coordinate transformation data to generate seismic regional terrain modeling data; perform terrain detail enhancement on the seismic regional terrain modeling data to generate seismic regional terrain enhancement data; Step S33: Spatialize the seismic regional terrain enhancement data to generate seismic regional vibration spatialization data, where data spatialization includes vibration data spatialization and displacement data visualization; Step S34: Based on three-dimensional modeling software, construct a three-dimensional scene for the seismic regional vibration spatialization data to generate seismic three-dimensional scene data.

[0048] In the embodiment of the present invention, the pre-earthquake seismically vulnerable area positioning data, the epicenter seismic activity information data, and the post-earthquake crustal structure change data are systematically integrated. This step can ensure the compatibility between different data sources through data cleaning, standardization, and format conversion. The integrated data forms comprehensive seismic acquisition data, covering multi-dimensional information before, during, and after the earthquake, providing a comprehensive basis for subsequent analysis. According to the required output format and the requirements of the Geographic Information System (GIS), a suitable three-dimensional coordinate system (such as WGS 84 or local coordinate system) is selected for data transformation. After completing the three-dimensional coordinate transformation, comprehensive seismic coordinate transformation data is generated to ensure that all data is analyzed under the same spatial reference. Use the comprehensive seismic coordinate transformation data for regional terrain modeling to construct a digital terrain model (DTM) of the area. Interpolation methods (such as Kriging interpolation, TIN model, etc.) can be used to generate elevation data. Complete seismic regional terrain modeling data is formed, containing the height information and terrain features of the region. Image processing techniques (such as sharpening filters, texture mapping, etc.) are used to enhance the details of the seismic regional terrain modeling data to highlight the terrain features. The finally generated seismic regional terrain enhancement data can display more real and delicate terrain information, facilitating further analysis. Spatialize the seismic regional terrain enhancement data. This process includes: through three-dimensional visualization technology, map the vibration data into three-dimensional space to present the changes in vibration amplitude and direction. Use graphic tools to visualize the displacement data in the form of color diagrams, vector arrows, etc. to help observe the direction and range of crustal movement. Combine the processed vibration data and displacement data to generate complete seismic regional vibration spatialization data, facilitating analysis and display. Based on three-dimensional modeling software (such as Blender, Unity, Unreal Engine, etc.), import the seismic regional vibration spatialization data for scene construction. Use the modeling, material, and lighting settings of the software to reconstruct the three-dimensional scene of the epicenter and its surrounding areas. Finally, seismic three-dimensional scene data is formed, which can intuitively display the regional characteristics, surface deformation conditions, and seismic wave propagation patterns affected by the earthquake.

[0049] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes: Step S41: Conduct a focal mechanism analysis on the 3D seismic scene data to generate focal mechanism analysis data, where the focal mechanism analysis data includes reverse fault mechanism data and normal fault mechanism data; Step S42: Perform seismic pattern recognition on the 3D seismic scene data through the reverse fault mechanism data and the normal fault mechanism data to obtain seismic pattern recognition data; extract seismic pattern feature data from the seismic pattern recognition data; Step S43: Divide the seismic pattern feature data into a data set to generate a model training set and a model test set; use the long short-term memory neural network algorithm to train the model training set to generate a preliminary earthquake disaster prediction model; optimize and iterate the preliminary earthquake disaster prediction model according to the model test set to generate an earthquake disaster prediction model; Step S44: Import the 3D seismic scene data into the earthquake disaster prediction model to conduct earthquake disaster prediction and generate earthquake disaster prediction data; construct a disaster data collection strategy based on the earthquake disaster prediction data to generate an earthquake disaster data collection strategy for performing 3D earthquake disaster data collection operations.

[0050] In the embodiments of the present invention, by utilizing three-dimensional seismic scene data and combining with seismic activity information at the epicenter, seismic waveform feature data related to the focal mechanism are collected, including the amplitude, frequency, and propagation direction of the waveform. An inversion algorithm (such as the seismic wave inversion method) is applied for focal mechanism analysis to identify reverse fault and normal fault mechanisms. This step involves time-domain and frequency-domain analysis of seismic waveforms to determine the slip direction and slip plane of the fault, generating focal mechanism analysis data including reverse fault mechanism data and normal fault mechanism data, providing a basis for subsequent seismic pattern recognition. Based on the reverse fault mechanism data and normal fault mechanism data, pattern recognition algorithms (such as support vector machines, random forests, etc.) are used to analyze the three-dimensional seismic scene data to identify different seismic patterns. Feature extraction is performed on the identified seismic patterns, including time-frequency features, spatial features, etc. of the patterns, to generate detailed seismic pattern feature data. This step can use methods such as wavelet transform and Fourier transform to extract signal features. Finally, seismic pattern recognition data is formed to describe the characteristics of different seismic patterns and their corresponding mechanisms. The seismic pattern feature data is divided into a data set, usually 70% is used for model training and 30% is used for model testing to ensure the effectiveness of model evaluation. The structure and characteristics of the training set and the test set are clarified to facilitate subsequent model training and verification. The long short-term memory neural network (LSTM) algorithm is used for model training because of its advantage in processing time series data, which is suitable for the time series characteristics of seismic data. The model training set is trained, and the network weights are adjusted through the backpropagation algorithm to reduce the prediction error, generating a preliminary seismic disaster prediction pre-model. The seismic disaster prediction pre-model is verified using the model test set to evaluate the accuracy and robustness of the model. According to the evaluation results, hyperparameter adjustment and model architecture improvement are performed, and the final seismic disaster prediction model is iteratively generated. The three-dimensional seismic scene data is input into the generated seismic disaster prediction model for actual seismic disaster prediction analysis. Seismic disaster prediction data is generated according to the output results of the model, describing the damage degree, damage range, and expected impact in the epicenter area. Based on the generated seismic disaster prediction data, potential disaster impact areas are analyzed, and corresponding data collection strategies are formulated to ensure rapid response and effective evaluation after the disaster. Finally, a systematic seismic disaster data collection strategy is formed to support three-dimensional seismic disaster data collection operations, providing a decision-making basis for post-disaster recovery and emergency management.

[0051] In this specification, a three-dimensional seismic data acquisition system is provided for performing the above-mentioned three-dimensional seismic data acquisition method. The three-dimensional seismic data acquisition system includes: Pre-earthquake regional positioning module, which is used to obtain data of the earthquake source area; perform real-time multi-source sensing data acquisition on the earthquake source area data to obtain multi-source sensing data of the earthquake source area; perform amplitude conversion on the multi-source sensing data of the earthquake source area to generate an amplitude conversion map of the earthquake source area; perform pre-earthquake potential earthquake-prone area positioning on the amplitude conversion map of the earthquake source area to generate pre-earthquake earthquake-prone area positioning data; Epicenter pre- and post-earthquake analysis module, which is used to record the epicenter earthquake waveform of the pre-earthquake earthquake-prone area positioning data to generate epicenter earthquake waveform data of the earthquake-prone area; perform earthquake instantaneous dynamic analysis on the epicenter earthquake waveform data of the earthquake-prone area to generate epicenter instantaneous magnitude data; perform earthquake activity analysis based on the epicenter instantaneous magnitude data to generate epicenter earthquake activity information data; perform post-earthquake crustal structure change analysis based on the epicenter earthquake activity information data to generate post-earthquake crustal structure change data; Scene construction module, which is used to integrate the pre-earthquake earthquake-prone area positioning data, epicenter earthquake activity information data, and post-earthquake crustal structure change data to generate comprehensive earthquake acquisition data; perform three-dimensional regional terrain modeling on the comprehensive earthquake acquisition data to generate earthquake regional terrain modeling data; perform three-dimensional scene construction on the earthquake regional terrain modeling data to generate earthquake three-dimensional scene data; Disaster prediction module, which is used to perform earthquake source mechanism analysis on the earthquake three-dimensional scene data to generate earthquake source mechanism analysis data; perform earthquake disaster prediction on the earthquake three-dimensional scene data through the earthquake source mechanism analysis data to generate earthquake disaster prediction data; construct a disaster data acquisition strategy based on the earthquake disaster prediction data to generate an earthquake disaster data acquisition strategy to execute three-dimensional earthquake disaster data acquisition operations.

[0052] The beneficial effects of the present invention are as follows: By obtaining data in the seismic source area, the timeliness and accuracy of the data are ensured, providing a reliable information basis for subsequent analysis. Converting the original data into an amplitude map can more clearly identify the seismic characteristics of the seismic source area, helping to locate potential earthquake-prone areas. By identifying earthquake-prone areas before an earthquake, high-risk areas can be identified in advance, providing an important basis for subsequent monitoring and emergency preparedness. Recording the waveforms in the epicenter area can obtain detailed seismic activity information, providing the necessary data for analyzing the epicenter situation. By analyzing the instantaneous magnitude, the changing dynamics of seismic activities can be better understood, enhancing the real-time monitoring ability of earthquakes. Analyzing the epicenter activity information to generate post-earthquake crustal change data helps to evaluate the impact of the earthquake on the crustal structure and supports subsequent post-disaster recovery work. Integrating the data at each stage into comprehensive acquisition data can provide a global perspective, making seismic analysis more comprehensive and systematic. Through terrain modeling, a spatial understanding of the earthquake-affected area is provided, helping to identify potential geological risks. The generated three-dimensional scene data is conducive to visual analysis, enabling researchers to more intuitively understand the earthquake impact and propagation path. By conducting seismic source mechanism analysis on the three-dimensional scene data, the occurrence mechanism of the earthquake can be deeply understood, providing a scientific basis for predicting earthquake behavior. The prediction data generated based on the seismic source mechanism analysis can more accurately evaluate the potential impact of the earthquake and enhance the effectiveness of emergency response. By formulating a scientific acquisition strategy, effective data acquisition and analysis can be ensured after an earthquake, enhancing the ability of post-disaster recovery and emergency management. Therefore, through real-time multi-source sensing data acquisition, dynamic epicenter analysis, and three-dimensional modeling, the present invention improves the timeliness of seismic data acquisition and the dynamic analysis ability.

[0053] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0054] The above are only the specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A three-dimensional seismic data acquisition method, characterized in that: The following steps are involved: Step S1: Acquire the earthquake source area data; perform real-time multi-source sensing data acquisition on the earthquake source area data to obtain the earthquake source area multi-source sensing data; Perform amplitude conversion on multi-source sensing data in the earthquake source area to generate an amplitude conversion map of the earthquake source area; perform positioning of potential earthquake-prone areas before an earthquake on the amplitude conversion map of the earthquake source area to generate positioning data of earthquake-prone areas before an earthquake; Step S2: Record the epicenter earthquake waveform of the earthquake-prone area positioning data before the earthquake to generate epicenter earthquake waveform data of the earthquake-prone area; Conduct earthquake instantaneous dynamic analysis on earthquake waveform data at the epicenter of earthquake-prone areas to generate instantaneous magnitude data at the epicenter; conduct earthquake activity analysis based on the instantaneous magnitude data at the epicenter to generate earthquake activity information data at the epicenter; conduct post-earthquake crustal structure change analysis based on the earthquake activity information data at the epicenter to generate post-earthquake crustal structure change data; Step S3: Integrate the pre-earthquake prone area location data, epicenter earthquake activity information data, and post-earthquake crust structure change data to generate comprehensive earthquake acquisition data; Conduct three-dimensional regional terrain modeling on comprehensive seismic acquisition data to generate seismic regional terrain modeling data; Construct three-dimensional scenes based on earthquake area terrain modeling data to generate earthquake three-dimensional scene data; Step S4: Perform source mechanism analysis on the earthquake three-dimensional scene data to generate source mechanism analysis data; perform earthquake disaster prediction on the earthquake three-dimensional scene data through the source mechanism analysis data to generate earthquake disaster prediction data; construct a disaster data collection strategy based on the earthquake disaster prediction data to generate an earthquake disaster data collection strategy to execute three-dimensional earthquake disaster data collection operations.

2. The three-dimensional seismic data acquisition method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire earthquake source area data; Step S12: deploying a seismic monitoring network for the source area data to obtain a seismic monitoring network for the source area; collecting real-time multi-source sensing data for the seismic monitoring network for the source area to obtain multi-source sensing data for the source area; Step S13: performing data preprocessing on the multi-source sensing data in the earthquake source area to generate standard multi-source sensing data in the earthquake source area, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S14: Perform amplitude conversion on the multi-source sensing data of the standard earthquake source area to generate an amplitude conversion map of the earthquake source area; identify the frequency change of seismic waves on the amplitude conversion map of the earthquake source area to generate seismic wave frequency change data; locate the potential earthquake-prone area before the earthquake based on the seismic wave frequency change data to generate the positioning data of the earthquake-prone area before the earthquake.

3. The three-dimensional seismic data acquisition method according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: Calculate the root mean square value of the multi-source sensing data of the standard earthquake source area to obtain the amplitude value of the earthquake source area; map the amplitude value of the earthquake source area to the preset GIS for amplitude conversion to generate an amplitude intensity map of the earthquake source area; perform color gradient marking on the amplitude intensity map of the earthquake source area to generate an amplitude conversion map of the earthquake source area; Step S142: performing position feature point identification on the earthquake source region amplitude conversion diagram to obtain the earthquake source region position feature points; performing feature point amplitude Fourier transform on the earthquake source region amplitude conversion diagram based on the earthquake source region position feature points to generate regional feature point seismic wave spectrum data; Step S143: performing frequency component change analysis on the seismic wave spectrum data of the regional feature points, generating seismic wave frequency rising curvature data and seismic wave frequency falling curvature data of the regional feature points, and removing the seismic wave frequency falling curvature data of the regional feature points; performing seismic wave frequency change trend analysis on the seismic wave spectrum data of the regional feature points using the seismic wave frequency rising curvature data of the regional feature points, generating seismic wave frequency change data; Step S144: locate the earthquake-prone area points by using the seismic wave frequency change data to locate the earthquake-prone area points; perform reality mapping on the earthquake-prone area points to generate earthquake-prone area points location mapping data; aggregate the earthquake-prone area points location mapping data to generate earthquake-prone area location data before the earthquake.

4. The three-dimensional seismic data acquisition method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: deploying strong motion seismographs on the pre-earthquake location data of the earthquake-prone area to obtain strong motion seismograph deployment data for the earthquake-prone area; recording the epicenter earthquake waveform on the strong motion seismograph deployment data for the earthquake-prone area to generate epicenter earthquake waveform data for the earthquake-prone area; Step S22: Perform earthquake instantaneous dynamic analysis on the earthquake waveform data at the epicenter of the earthquake-prone area to generate instantaneous magnitude data at the epicenter; compare the instantaneous magnitude data at the epicenter with a preset warning magnitude threshold, and when the instantaneous magnitude data at the epicenter is greater than or equal to the preset warning magnitude threshold, use a drone to perform earthquake surface damage analysis on the positioning data of the earthquake-prone area before the earthquake to generate surface damage data at the epicenter; Step S23: when the instantaneous magnitude data of the epicenter is less than the preset warning magnitude threshold, the earthquake hidden layer damage analysis is performed on the pre-earthquake prone area location data based on the instantaneous magnitude data of the epicenter to generate the epicenter hidden layer impact data; the epicenter surface layer damage data and the epicenter hidden layer impact data are integrated to generate the epicenter earthquake activity information data; Step S24: Conduct post-earthquake geological exploration based on the epicenter seismic activity information data to generate post-earthquake geological exploration data; conduct crustal structure change analysis on the post-earthquake geological exploration data to generate post-earthquake crustal structure change data.

5. The three-dimensional seismic data acquisition method according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: Perform earthquake instantaneous dynamic analysis on the earthquake waveform data at the epicenter of the earthquake-prone area to generate instantaneous magnitude data at the epicenter; compare the instantaneous magnitude data at the epicenter with a preset warning magnitude threshold, and when the instantaneous magnitude data at the epicenter is greater than or equal to the preset warning magnitude threshold, use a drone to perform multi-view image acquisition on the positioning data of the earthquake-prone area before the earthquake to obtain a multi-view image set of the earthquake-prone area; Step S222: performing surface line shape recognition on the multi-view image set of the earthquake-prone area to obtain crack recognition data of the earthquake-prone area; performing edge region segmentation on the multi-view image set of the earthquake-prone area according to the crack recognition data of the earthquake-prone area to generate a crack region image of the earthquake-prone area; Step S223: performing surface texture feature recognition on the crack region image in the earthquake-prone area to obtain texture feature data of the crack region; performing surface settlement analysis on the crack region image in the earthquake-prone area using the texture feature data of the crack region to generate surface settlement data of the crack region; Step S224: quantify the damage level of the surface settlement data in the fracture area to generate epicenter surface damage data.

6. The three-dimensional seismic data acquisition method according to claim 4, characterized in that: Step S23 includes the following steps: Step S231: When the instantaneous magnitude data of the epicenter is less than the preset warning magnitude threshold, electromagnetic detection is performed on the pre-earthquake prone area positioning data based on the instantaneous magnitude data of the epicenter to generate an electromagnetic imaging map of the epicenter of the earthquake prone area; Step S232: performing fault color distribution analysis on the epicenter electromagnetic imaging image of the earthquake-prone region to generate fault color distribution data; performing hierarchical electromagnetic characteristic analysis on the epicenter electromagnetic imaging image of the earthquake-prone region according to the fault color distribution data to generate hierarchical electromagnetic characteristic analysis data; Step S233: using the hierarchical electromagnetic characteristic analysis data to identify the electromagnetic reflection band of the fault color distribution data, and generate the electromagnetic reflection area of ​​the epicenter of the earthquake-prone area; performing electromagnetic anomaly band analysis on the electromagnetic reflection area of ​​the epicenter of the earthquake-prone area, and thus generating the epicenter hidden layer impact data; Step S234: Integrate the epicenter surface damage data and the epicenter hidden layer impact data to generate epicenter earthquake activity information data.

7. The three-dimensional seismic data acquisition method according to claim 4, characterized in that: Step S24 includes the following steps: Based on the epicenter seismic activity information data, the amplitude conversion diagram of the source area is used to confirm the stable time point of frequency change, and the gentle change time point is obtained; post-earthquake geological detection is carried out according to the gentle change time point to generate post-earthquake geological detection echo data; Calculate the echo time of the post-earthquake geological detection echo data to obtain the echo time data; calculate the surface deformation amplitude of the post-earthquake geological detection echo data according to the echo time data to generate a post-earthquake surface deformation difference map; The post-earthquake surface deformation differences are analyzed by time series difference to generate the post-earthquake crust structure change data.

8. The three-dimensional seismic data acquisition method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Integrate the data of earthquake-prone area location before the earthquake, the information data of earthquake activity at the epicenter, and the data of crustal structure change after the earthquake to generate comprehensive earthquake acquisition data; Step S32: performing three-dimensional coordinate conversion on the comprehensive seismic acquisition data to generate comprehensive seismic coordinate conversion data; performing regional terrain modeling on the comprehensive seismic coordinate conversion data to generate regional seismic terrain modeling data; performing terrain detail enhancement on the regional seismic terrain modeling data to generate regional seismic terrain enhancement data; Step S33: spatializing the earthquake region terrain enhancement data to generate earthquake region vibration spatialization data, wherein the data spatialization includes vibration data spatialization and displacement data visualization; Step S34: construct a three-dimensional scene for the earthquake region vibration spatialization data based on three-dimensional modeling software to generate earthquake three-dimensional scene data.

9. The three-dimensional seismic data acquisition method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing a focal mechanism analysis on the earthquake three-dimensional scene data to generate focal mechanism analysis data, wherein the focal mechanism analysis data includes reverse fault mechanism data and normal fault mechanism data; Step S42: performing earthquake pattern recognition on the earthquake three-dimensional scene data through the reverse fault mechanism data and the normal fault mechanism data to obtain earthquake pattern recognition data; performing earthquake pattern feature extraction on the earthquake pattern recognition data to obtain earthquake pattern feature data; Step S43: dividing the earthquake pattern feature data into data sets to generate a model training set and a model test set; using a long short-term memory neural network algorithm to perform model training on the model training set to generate an earthquake disaster prediction pre-model; performing model optimization iteration on the earthquake disaster prediction pre-model according to the model test set to generate an earthquake disaster prediction model; Step S44: Import the three-dimensional earthquake scene data into the earthquake disaster prediction model to perform earthquake disaster prediction and generate earthquake disaster prediction data; construct a disaster data collection strategy based on the earthquake disaster prediction data and generate an earthquake disaster data collection strategy to perform three-dimensional earthquake disaster data collection operations.

10. A three-dimensional seismic data acquisition system, characterized in that: For executing the three-dimensional seismic data acquisition method according to claim 1, the three-dimensional seismic data acquisition system comprises: The pre-earthquake regional positioning module is used to obtain the source area data; perform real-time multi-source sensing data acquisition on the source area data to obtain the source area multi-source sensing data; perform amplitude conversion on the source area multi-source sensing data to generate the source area amplitude conversion map; perform pre-earthquake potential earthquake-prone area positioning on the source area amplitude conversion map to generate pre-earthquake earthquake-prone area positioning data; The epicenter and post-seismic analysis module is used to record the epicenter seismic waveform of the earthquake-prone area positioning data before the earthquake, and generate the epicenter seismic waveform data of the earthquake-prone area; perform earthquake instantaneous dynamic analysis on the epicenter seismic waveform data of the earthquake-prone area, and generate the epicenter instantaneous magnitude data; perform earthquake activity analysis based on the epicenter instantaneous magnitude data, and thus generate epicenter seismic activity information data; perform post-seismic crustal structure change analysis based on the epicenter seismic activity information data, and generate post-seismic crustal structure change data; The scene construction module is used to integrate the data of earthquake-prone area location before the earthquake, the information data of earthquake activity in the epicenter, and the data of crustal structure change after the earthquake to generate comprehensive earthquake acquisition data; to perform three-dimensional regional terrain modeling on the comprehensive earthquake acquisition data to generate earthquake regional terrain modeling data; to perform three-dimensional scene construction on the earthquake regional terrain modeling data to generate earthquake three-dimensional scene data; The disaster prediction module is used to perform source mechanism analysis on earthquake three-dimensional scene data and generate source mechanism analysis data; to predict earthquake disasters on earthquake three-dimensional scene data through source mechanism analysis data and generate earthquake disaster prediction data; to construct disaster data collection strategy based on earthquake disaster prediction data and generate earthquake disaster data collection strategy to execute three-dimensional earthquake disaster data collection operations.

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