Method and system for constructing seismic fracture fault model and co-seismic displacement field

Abstract

The invention provides a seismic fracture fault model and co-seismic displacement field construction method and system, and relates to the technical field of seismic data processing, and the method comprises the steps: obtaining GNSS data, strong-motion seismograph data and accelerometer data of a preset region, and carrying out the fusion processing of the data, and obtaining the multi-source seismic data of the preset region; performing seismic waveform inversion on the multi-source seismic data to obtain related earth crust parameters of the preset area; calculating a Green function by using a hierarchical plane dislocation model according to the multi-source seismic data in combination with the related earth crust parameters to obtain a seismic fracture fault model of the preset region; performing inversion on the seismic fracture fault model through a gradient algorithm to obtain a seismic fracture surface of the preset area; and analyzing the seismic fracture surface to obtain a co-seismic displacement field. According to the method, multi-source data fusion is combined with an algorithm, the influence of noise interference and data errors is effectively reduced, and an earthquake occurrence mechanism and earth surface deformation characteristics are accurately revealed.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data processing, and in particular, to a method and system for constructing a seismic rupture fault model and a coseismic displacement field. Background Art

[0002] Since seismic disasters have a huge impact on human society and the natural environment, establishing a seismic rupture fault model and a coseismic displacement field is of extremely important significance for understanding the earthquake occurrence mechanism, assessing seismic disasters, earthquake early warning, earthquake prevention and mitigation, etc. Among them, the seismic rupture fault model can reveal the spatial and temporal distribution of underground rock fractures during an earthquake, while the coseismic displacement field reflects the displacement of the ground surface during an earthquake.

[0003] In the related art, seismic rupture plane inversion methods mainly rely on single seismic wave data, such as body wave or surface wave data, which are easily affected by noise interference during the inversion process, resulting in low accuracy and reliability of the inversion results. In addition, most of the existing methods for constructing coseismic displacement fields are based on theoretical model calculations and lack effective combination with actual observation data, and cannot accurately reflect the true displacement of the ground surface during an earthquake. Summary of the Invention

[0004] The problem solved by the present invention is how to improve the accuracy of the seismic rupture fault model and the coseismic displacement field.

[0005] To solve the above problems, the present invention provides a method and system for constructing a seismic rupture fault model and a coseismic displacement field.

[0006] In a first aspect, the present invention provides a method for constructing a seismic rupture fault model and a coseismic displacement field, including: Obtaining GNSS data, strong motion instrument data, and accelerometer data of a preset area; Performing fusion processing on the GNSS data, the strong motion instrument data, and the accelerometer data to obtain multi-source seismic data of the preset area; Performing seismic waveform inversion on the multi-source seismic data to obtain relevant crustal parameters of the preset area; Combining the relevant crustal parameters, calculating Green's function using a layered planar dislocation model according to the multi-source seismic data to obtain a seismic rupture fault model of the preset area; Inverting the seismic rupture fault model through a gradient algorithm to obtain a seismic rupture surface of the preset area; Analyzing the seismic rupture surface to obtain a coseismic displacement field.

[0007] Optionally, the obtaining GNSS data, strong motion instrument data, and accelerometer data of a preset area includes: Determine the surface position and displacement information of the preset area through satellite positioning technology, and use the surface position and the displacement information as the GNSS data; Record data through a strong motion seismograph, determine the ground acceleration, ground velocity and ground displacement of the preset area, and use the ground acceleration, the ground velocity and the ground displacement as the strong motion seismograph data; Determine the acceleration change values of the ground and buildings within the preset area through an acceleration sensor, and use the acceleration change values as the accelerometer data.

[0008] Optionally, the fusion processing of the GNSS data, the strong motion seismograph data and the accelerometer data to obtain the multi-source seismic data of the preset area includes: Perform time alignment processing and spatial coordinate transformation processing on the GNSS data, the strong motion seismograph data and the accelerometer data respectively according to the same time reference and spatial reference to obtain the GNSS data, the strong motion seismograph data and the accelerometer data with eliminated spatio-temporal differences; Assign weights to the GNSS data, the strong motion seismograph data and the accelerometer data with eliminated spatio-temporal differences respectively through a weighted data fusion filtering algorithm; Fuse according to the weights corresponding to the GNSS data, the strong motion seismograph data and the accelerometer data with eliminated spatio-temporal differences to obtain the multi-source seismic data.

[0009] Optionally, the seismic waveform inversion of the multi-source seismic data to obtain the relevant crustal parameters of the preset area includes: Conduct forward simulation according to the multi-source seismic data and a preset underground medium model to generate the theoretical seismic waveform of the preset area; Compare the theoretical seismic waveform with the observed waveform of the preset area to determine the error between the theoretical seismic waveform and the observed waveform; Perform iterative optimization according to the error to obtain the relevant crustal parameters.

[0010] Optionally, combining the relevant crustal parameters, calculate the Green's function according to the multi-source seismic data using a layered planar dislocation model to obtain the seismic rupture fault model of the preset area, including: Input the relevant crustal parameters into the layered planar dislocation model, and set the earth internal layered model of the preset area according to the relevant crustal parameters; Discretize the earth internal layered model to obtain multiple sub-layers; Using the Thomson-Haskell propagation algorithm, calculate the propagation data of seismic waves in the sub-layer in sequence, and then obtain the Green's function values of each sub-layer through inverse Hankel transform; Determine the total deformation caused by the seismic waves in the crust through linear superposition; Obtain the seismic rupture fault model based on the total deformation in combination with the Green's function values;

[0011] Optionally, the inversion of the seismic rupture fault model through the gradient algorithm to obtain the seismic rupture surface in the preset area includes: Obtain model data based on the seismic rupture fault model; Perform geometric correction on the model data, and perform grid division on the faults in the preset area to obtain the fault structure in the preset area; Perform model calculation based on the fault structure, and then perform inversion based on the calculation results of the model calculation to obtain the seismic rupture surface.

[0012] Optionally, the performing model calculation based on the fault structure, and then performing inversion based on the calculation results of the model calculation to obtain the seismic rupture surface includes: Perform model calculation based on the fault structure to obtain the seismic fault dip angle, landslide gradient loss function, and slip gradient loss function in the preset area, and calculate the fault slip compensation point source, maximum gradient of the loss function, slip model displacement field, and sub-region slip gradient; Use the seismic fault dip angle, the landslide gradient loss function, the slip gradient loss function, the slip compensation point source, the maximum gradient of the loss function, the slip model displacement field, and the sub-region slip gradient as the calculation results, and perform inversion on the calculation results through an analytical algorithm for surface deformation caused by spatial internal shear and tensile faults to obtain the seismic rupture surface.

[0013] Optionally, the analysis of the seismic rupture surface to obtain the coseismic displacement field includes: Discretize the seismic rupture surface into multiple discrete point double-couple sources; Determine the coseismic displacement field through analysis based on the discrete point double-couple sources.

[0014] Optionally, the determining the coseismic displacement field through analysis based on the discrete point double-couple sources includes: Through Hankel custom transform, obtain the conventional equation in the wavenumber domain based on the distribution and parameters of the discrete point double-couple sources; Through the Thomson–Haskell propagator algorithm, determine the particular solution that satisfies the source and boundary conditions based on the conventional equation; Convert the special solution into a solution in the spatial domain through inverse Hankel; Perform a linear superposition operation based on the solution in the spatial domain to obtain seismic change data; Obtain the coseismic displacement field based on the seismic change data.

[0015] In a second aspect, the present invention provides a system for constructing a seismic rupture fault model and a coseismic displacement field, comprising: A data acquisition unit for acquiring GNSS data, strong motion seismograph data, and accelerometer data of a preset area; A data fusion unit for fusing the GNSS data, the strong motion seismograph data, and the accelerometer data to obtain multi-source seismic data of the preset area; A first inversion unit for performing seismic waveform inversion on the multi-source seismic data to obtain relevant crustal parameters of the preset area; A fault model construction unit for combining the relevant crustal parameters and calculating the Green's function according to the multi-source seismic data using a layered planar dislocation model to obtain a seismic rupture fault model of the preset area; A second inversion unit for inverting the seismic rupture fault model through a gradient algorithm to obtain a seismic rupture surface of the preset area; A displacement field construction unit for analyzing the seismic rupture surface to obtain a coseismic displacement field.

[0016] The method and system for constructing the earthquake rupture fault model and the co-seismic displacement field of the present invention eliminate the limitations of a single data source through the fusion processing of multi-source data. For example, the high-precision displacement information of GNSS data is combined with the high-frequency seismic waveform information of strong motion instruments and accelerometers, which can more comprehensively reflect the dynamic changes on the surface and underground during an earthquake, give full play to the advantages of each data source, make up for the deficiencies of a single data source, and thus more comprehensively reflect the dynamic changes on the surface and underground during the earthquake process. Secondly, the estimation of crustal parameters is further optimized through seismic waveform inversion technology, providing a more accurate physical basis for subsequent model calculations. Moreover, the Green's function calculation based on the layered plane dislocation model takes into account the complex layered structure of the crust, and can more realistically simulate the propagation and deformation effects of seismic waves in multi-layer media, thereby improving the accuracy of the earthquake rupture fault model. Finally, the earthquake rupture fault model is inverted through the gradient algorithm, and the co-seismic displacement field is calculated by combining the analytical algorithm, further improving the fitting accuracy and reliability of the model. The method of combining multi-source data fusion with advanced algorithms in the present invention can not only effectively reduce the influence of noise interference and data errors, but also more accurately reveal the earthquake occurrence mechanism and surface deformation characteristics. Furthermore, it can provide more accurate and reliable data support for various tasks such as seismological research, earthquake disaster assessment, and earthquake prevention and mitigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the method for constructing the earthquake rupture fault model and the co-seismic displacement field in an embodiment of the present invention; Figure 2 It is a schematic diagram of the working structure of a strong motion instrument in an embodiment of the present invention; Figure 3 It is a schematic diagram of the weighted data fusion filtering algorithm in an embodiment of the present invention; Figure 4 It is a schematic diagram of the principle information for obtaining relevant crustal parameters in an embodiment of the present invention; Figure 5 It is a flowchart of the customized Green's algorithm in an embodiment of the present invention; Figure 6 It is a schematic diagram of the principle for the customized Green's algorithm to solve the response generated by the earthquake source in the medium in an embodiment of the present invention; Figure 7 It is a flowchart of the work for the earthquake rupture surface inversion calculation in an embodiment of the present invention; Figure 8 It is a schematic diagram of the data processing of the formation model using a layered model in an embodiment of the present invention; Figure 9 It is a schematic diagram of the structure of the system for constructing the earthquake rupture fault model and the co-seismic displacement field in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. Instead, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0019] It should be understood that the various steps described in the method embodiments of the present invention can be executed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.

[0020] The term "including" and its variations used herein are open-ended, that is, "including but not limited to"; the term "based on" means "at least partially based on"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description.

[0021] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] Combined Figure 1 As shown, in view of the problems existing in the above related technologies, this embodiment provides a method for constructing an earthquake rupture fault model and a co-seismic displacement field, including: Obtain GNSS data, strong motion instrument data, and accelerometer data of a preset area.

[0024] Specifically, by deploying high-precision Global Navigation Satellite System (GNSS) monitoring stations, strong motion instruments, and accelerometers in a preset area, the ground motion data before and after an earthquake are collected in real time. GNSS data can provide high-precision ground displacement information, strong motion instrument data are used to record the propagation characteristics of seismic waves, and accelerometer data are used to capture the acceleration changes during an earthquake; these data are the basis for constructing an earthquake rupture fault model and a co-seismic displacement field, providing accuracy and reliability for subsequent analysis.

[0025] Fuse the GNSS data, the strong motion seismograph data, and the accelerometer data to obtain the multi-source seismic data of the preset area.

[0026] Specifically, fuse the acquired GNSS data, strong motion seismograph data, and accelerometer data. The fusion process includes steps such as data preprocessing (such as filtering and denoising), time alignment, and data format unification. Through the fusion process, different types of data are integrated into a complete multi-source seismic data set to more comprehensively reflect the ground motion characteristics during an earthquake. The data fusion method in this embodiment can give full play to the advantages of various types of data and improve the accuracy and reliability of subsequent analysis.

[0027] Perform seismic waveform inversion on the multi-source seismic data to obtain the relevant crustal parameters of the preset area.

[0028] Specifically, use the fused multi-source seismic data to calculate the relevant crustal parameters of the preset area through seismic waveform inversion technology. Among them, seismic waveform inversion is a method based on the theory of seismic wave propagation. By fitting the difference between the observed seismic waveform and the theoretical waveform, parameters such as the velocity structure and density distribution in the crust are inverted.

[0029] Combine the relevant crustal parameters and use the layered planar dislocation model to calculate the Green's function based on the multi-source seismic data to obtain the seismic rupture fault model of the preset area.

[0030] Specifically, combine the crustal parameters obtained by inversion and the multi-source seismic data, and use the layered planar dislocation model to calculate the Green's function; the Green's function is used to describe the influence of the dislocation on the fault plane during the earthquake rupture process on the ground motion. By calculating the Green's function, a seismic rupture fault model of the preset area can be constructed, which can describe in detail the dislocation distribution and rupture propagation characteristics on the fault plane during the earthquake rupture process.

[0031] Invert the seismic rupture fault model through the gradient algorithm to obtain the seismic rupture surface of the preset area.

[0032] Specifically, use the gradient algorithm to invert the seismic rupture fault model and optimize the model parameters to better fit the observed data. Among them, the gradient algorithm is an efficient optimization method that can gradually reduce the difference between the model prediction and the actual observation by iteratively adjusting the model parameters. The seismic rupture surface obtained by inversion can clearly show the dislocation distribution and rupture propagation path on the fault plane during the earthquake rupture process, providing key input for the subsequent calculation of the coseismic displacement field.

[0033] Analyze the seismic rupture surface to obtain the coseismic displacement field.

[0034] Specifically, the earthquake rupture surface obtained by inversion is analyzed to calculate the coseismic displacement field of the ground caused during the earthquake rupture process. The coseismic displacement field is used to reflect the instantaneous displacement changes of the ground during an earthquake and is an important basis for evaluating earthquake impacts and disaster risks. By analyzing the earthquake rupture surface, the ground displacement amounts and displacement directions at different positions can be accurately calculated, thereby obtaining a complete distribution map of the coseismic displacement field.

[0035] The earthquake rupture fault model and the construction method and system of the coseismic displacement field of the present invention eliminate the limitations of a single data source through the fusion processing of multi-source data. For example, the high-precision displacement information of GNSS data is combined with the high-frequency seismic waveform information of strong motion instruments and accelerometers, which can more comprehensively reflect the dynamic changes on the surface and underground during an earthquake, give full play to the advantages of each data source, make up for the deficiencies of a single data source, and thus more comprehensively reflect the dynamic changes on the surface and underground during the earthquake process. Secondly, the estimation of crustal parameters is further optimized through seismic waveform inversion technology, providing a more accurate physical basis for subsequent model calculations. Moreover, the Green's function calculation based on the layered planar dislocation model takes into account the complex layered structure of the crust and can more realistically simulate the propagation and deformation effects of seismic waves in multi-layer media, thereby improving the accuracy of the earthquake rupture fault model. Finally, the earthquake rupture fault model is inverted through the gradient algorithm, and the coseismic displacement field is calculated by combining the analytical algorithm, further improving the fitting accuracy and reliability of the model. The method of combining multi-source data fusion with advanced algorithms in the present invention can not only effectively reduce the influence of noise interference and data errors, but also more accurately reveal the earthquake occurrence mechanism and surface deformation characteristics. Furthermore, it can provide more accurate and reliable data support for various tasks such as seismological research, earthquake disaster assessment, and earthquake prevention and mitigation.

[0036] Optionally, the obtaining of the GNSS data, strong motion instrument data, and accelerometer data of the preset area includes: Through satellite positioning technology, determine the surface position and displacement information of the preset area, and use the surface position and the displacement information as the GNSS data; Through the strong motion instrument to record data, determine the ground acceleration, ground velocity, and ground displacement of the preset area, and use the ground acceleration, the ground velocity, and the ground displacement as the strong motion instrument data; Through the acceleration sensor, determine the acceleration change values of the ground and buildings within the preset area, and use the acceleration change values as the accelerometer data.

[0037] Specifically, when obtaining GNSS data, strong motion instrument data, and accelerometer data of a preset area, first, satellite positioning technology (such as GPS or Beidou system) is used to monitor the surface position and displacement information of the preset area in real time. Through a high-precision GNSS receiver, the precise position changes on the ground before and after an earthquake are obtained. These data include not only horizontal displacements but also vertical displacements, providing important ground motion information for the construction of earthquake rupture fault models. Among them, GNSS data is the surface position and displacement information obtained through satellite positioning technology, capable of providing high-precision surface deformation data. These data include the displacement observation values of a station in the east, north, and up directions, and can be used to monitor surface displacements caused by earthquakes, plate movements, and other crustal deformation phenomena. GNSS data has the characteristics of high precision, high spatio-temporal resolution, and all-weather observation, and is an indispensable important data source in seismological research and crustal deformation monitoring. For example, in earthquake monitoring, GNSS data can help determine the range and degree of surface deformation caused by an earthquake. Secondly, strong motion instruments record the acceleration, velocity, and displacement data of the ground during an earthquake. These data reflect the propagation characteristics of seismic waves on the surface, including the intensity, frequency, and duration of ground motion. Strong motion instrument data has a high sampling rate and wide-band characteristics, capable of recording high-frequency signals during an earthquake. Strong motion instruments are usually installed in earthquake-prone areas or key monitoring points, capable of capturing the dynamic changes during the propagation of seismic waves. These data are crucial for analyzing the propagation characteristics of seismic waves and the impact of earthquakes on ground structures.

[0038] In a preferred embodiment of the present invention, in combination with Figure 2As shown, the working structure of the strong motion seismograph, which is a key device for earthquake data acquisition in the present invention; the main function of the seismograph is to record various seismic waves generated during an earthquake, including body waves (P-waves and S-waves) and surface waves. The working structure of the seismograph is decomposed into multiple key parts, each with a clear function and data flow. The sensor part of the seismograph is responsible for detecting the vibrations of seismic waves. These sensors usually include geophones in multiple directions to ensure that three-dimensional information of seismic waves can be comprehensively captured. The raw data collected by the sensors is then transmitted to the data processing unit, which is responsible for amplifying, filtering, and digitizing the signals to improve the quality and usability of the data. The data processed by the data processing unit is stored in the data storage module. At the same time, this data can also be transmitted to the central processing system or remote monitoring center in real time through the data transmission module. The data storage module not only stores the original seismic waveform data but may also include preliminarily processed characteristic data, such as the arrival time, amplitude, and frequency of seismic waves. Among them is the power management module, which ensures that the seismograph can operate stably under various environmental conditions. The power management module includes batteries, solar panels, or other energy supply devices to adapt to different deployment scenarios and working requirements. Among them is the communication interface for data exchange and communication with external devices. Through the wireless communication module, the seismograph can send data to nearby base stations or satellites to achieve remote data transmission and real-time monitoring. This schematic diagram of the structure not only helps users and professionals understand the working mechanism of the seismograph but also provides important background information for multi-source data fusion and earthquake data processing in the present invention. Through efficient data acquisition and transmission, the seismograph provides high-quality raw data for the establishment of earthquake rupture fault models and the construction of coseismic displacement fields, ensuring the accuracy and reliability of the method of the present invention. Finally, acceleration sensors are used to monitor the acceleration change values of the ground and buildings in a preset area. Among them, accelerometer data is the acceleration change of the ground or building recorded by the acceleration sensor during an earthquake. These data can capture the inertial forces caused by an earthquake in real time and reflect the intensity and dynamic characteristics of ground motion. Accelerometers usually have high sensitivity and high sampling rates and can record seismic signals from low frequencies to high frequencies, which are important data sources for studying the characteristics of ground motion and structural responses. However, acceleration data is not intuitive. Generally, it will be simply numerically integrated twice to obtain the displacement change result caused by the earthquake. The errors of accelerometers are cumulative, and at the same time, integration will inevitably further amplify the influence of their noise. To reduce the degradation of the quality of the displacement change result caused by non-physical drift, it is necessary to perform fusion analysis with other data.

[0039] Specifically, the control module is the "brain" of the entire system, responsible for receiving and processing data from various sensors; receiving data (such as seismic signals and temperature information) from sensors (such as seismic detection sensors and temperature measurement modules), executing corresponding control strategies and sending instructions to other modules (such as communication modules and alarm modules). For example, when a seismic signal is triggered, it can quickly respond, activate the safety blocker to cut off the power supply, and prevent equipment damage or danger. The seismic detection sensor is mainly responsible for detecting the vibrations of seismic waves, including body waves (P-waves and S-waves) and surface waves, and transmitting the detected raw seismic data to the control module for processing. The temperature measurement module is used to monitor the temperature changes in the external or internal environment, provide temperature information to the control module; and send temperature data to the control module. When detecting a seismic signal, the safety blocker will perform operations to cut off or restore power according to the instructions of the control module to prevent equipment damage or danger, thereby protecting the safety of equipment and personnel. The communication module is responsible for data exchange and communication with external devices (such as base stations or satellites); through the wireless communication module, seismic data can be transmitted to the remote monitoring center in real time to achieve remote monitoring and data analysis. When detecting a seismic signal or other abnormal conditions, the alarm module will issue an alarm to remind relevant personnel to take emergency measures. The power supply module provides stable power support for the entire system. The power supply module can include batteries, solar panels or other energy devices to adapt to different deployment scenarios and working requirements. The load is the device or system to be monitored, which can be a building, a bridge or other important structures. The purpose of the seismograph is to monitor the performance of these structures during an earthquake to evaluate their safety. Each of the above modules works together to achieve the acquisition, processing, transmission and safety protection of seismic data.

[0040] In an alternative embodiment, through the comprehensive acquisition of multi-source data, the dynamic changes of the ground and buildings during an earthquake are comprehensively captured, providing rich information for earthquake research and disaster assessment. GNSS data provides high-precision surface displacement information, which helps to determine the range of surface deformation caused by an earthquake; strong motion seismograph data can detail the propagation characteristics of seismic waves and provide key inputs for seismic waveform inversion; while accelerometer data focuses on the seismic performance assessment of buildings and can help identify potential damage to buildings caused by an earthquake. The combined use of these data not only improves the accuracy and reliability of earthquake monitoring, but also provides a solid data foundation for the construction of earthquake rupture fault models and co-seismic displacement fields, thus providing a scientific basis for the rapid response to earthquake disasters and disaster reduction measures.

[0041] Optionally, the fusion processing of the GNSS data, the strong motion seismograph data and the accelerometer data to obtain the multi-source seismic data of the preset area includes: Perform time alignment processing and spatial coordinate transformation processing on the GNSS data, the strong motion seismograph data, and the accelerometer data respectively according to the same time reference and spatial reference to obtain the GNSS data, the strong motion seismograph data, and the accelerometer data with eliminated spatio-temporal differences; Through the weighted data fusion filtering algorithm, assign weights to the GNSS data, the strong motion seismograph data, and the accelerometer data with eliminated spatio-temporal differences respectively; Fuse according to the weights corresponding to the GNSS data, the strong motion seismograph data, and the accelerometer data with eliminated spatio-temporal differences to obtain the multi-source seismic data.

[0042] Specifically, when performing fusion processing on GNSS data, strong motion seismograph data, and accelerometer data, it is first necessary to perform time alignment and spatial coordinate transformation processing on these data according to the same time reference and spatial reference; this process not only ensures the consistency of data from different sources in time and space, but also eliminates the errors caused by the time synchronization differences and spatial position differences of data acquisition devices. Secondly, through the weighted data fusion filtering algorithm, assign weights to the GNSS data, the strong motion seismograph data, and the accelerometer data after eliminating spatio-temporal differences; among them, the basis for weight assignment is based on the accuracy, reliability of the data, and the degree of contribution to seismic analysis. For example, GNSS data has high precision in displacement measurement and may be given a higher weight; while strong motion seismograph data has more advantages in seismic waveform recording and will also be assigned corresponding weights according to its importance. Finally, fuse different data sources according to these weights to obtain multi-source seismic data. In the data fusion stage, the weighted data fusion filtering algorithm is used to fuse GNSS data, strong motion seismograph data, and accelerometer data.

[0043] This method of this embodiment comprehensively considers the accuracy and reliability of each data source by assigning appropriate weights to different data sources. Compared with the traditional accelerometer double integration method, the fused high-frequency displacement observation values and high-frequency velocity observation data have higher accuracy. This is because although accelerometer data can provide high-frequency information, its integration process is prone to error accumulation, while GNSS data can provide high-precision displacement information, and the fusion can effectively make up for the deficiencies of a single data source. In the stage of fusing to obtain the co-seismic displacement field of the region, the fusion algorithm automatically selects an appropriate calculation method and performs data fusion according to the type of input formation model to form the final surface deformation result. According to different formation models, there are mainly two calculation paths: when the formation model adopts a homogeneous model, the algorithm uses an analytical method to calculate the surface deformation caused by internal shear and tensile faults in space. This method is based on the homogeneous medium hypothesis, considers the interaction between the uniform seismic source and the surface, and calculates the surface deformation under different fault characteristics. This data fusion algorithm can provide accurate seismic deformation prediction results under different geological conditions by intelligently selecting appropriate models and methods and combining the complexity of the formation structure.

[0044] In a preferred embodiment of the present invention, time alignment is performed by the Timestamp Alignment method to read the timestamps in the data file and align the data of different sensors to a unified time reference. If the data sampling rates are different, alignment can be performed by interpolation methods. There is also the Time Delay Estimation - TDE method to calculate the time delay between two signals and align the data to the same time reference. Spatial coordinate transformation is performed through a coordinate transformation matrix, rotation matrix, and translation vector to transform the data from one coordinate system to another. This method is based on linear algebra and is suitable for simple coordinate transformations. Or use seven-parameter transformation to transform one coordinate system to another through seven parameters (three translation parameters, three rotation parameters, and one scale parameter). This method is suitable for precise transformation between different coordinate systems. Time synchronization and spatial coordinate transformation eliminate the spatio-temporal differences between the data, enabling all data to be analyzed under the same reference framework. This process not only improves the compatibility of the data but also lays a foundation for subsequent fusion processing. Time synchronization and spatial coordinate transformation eliminate the spatio-temporal differences between the data, enabling all data to be analyzed under the same reference framework. This process not only improves the compatibility of the data but also lays a foundation for subsequent fusion processing.

[0045] In another preferred embodiment of the present invention, in combination with Figure 3The weighted data fusion filtering algorithm shown processes the GNSS data, strong motion seismograph data, and accelerometer data. By combining the advantages of the two data sources, this algorithm significantly improves the reliability and accuracy of displacement change analysis in earthquake monitoring. The inputs of the algorithm include time-aligned GNSS displacement observation data and accelerometer data in the NEU (north, east, up) directions. The accelerometer data provides high-frequency acceleration information, but its integration process is prone to error accumulation, leading to a decrease in the accuracy of displacement data. Although GNSS data has high accuracy, its sampling frequency is low. Through the weighted data fusion filtering algorithm, these two types of data can be effectively fused to overcome their respective deficiencies. Specifically, the algorithm first makes a one-step prediction of displacement and velocity based on the output of the accelerometer. This one-step prediction utilizes the high-frequency characteristics of the accelerometer and can quickly respond to the changes in seismic waves. Subsequently, the predicted values are updated based on the GNSS observation results. This update process utilizes the high-accuracy characteristics of GNSS data to correct the predicted values, thereby obtaining more accurate displacement and velocity data. As Figure 3 shown in the iterative process of the algorithm, within each time step, the algorithm continuously makes predictions and updates until the next GNSS observation value appears. Among them, w(k + 1) represents the input signal, which is the external input at time step (k + 1). Γ(k + 1, k) represents the input transition matrix, which is used to convert the input signal w(k + 1) into an impact on the state vector. Φ(k + 1, k) represents the state transition matrix, which is used to convert the current state vector x(k) into the state vector x(k + 1) at the next time step. x(k) represents the current state vector, which is the system state at time step k. x(k + 1) represents the state vector at the next time step, which is calculated from the current state vector x(k) and the input signal w(k + 1) through the state transition matrix and the input transition matrix. H(k + 1) represents the output transition matrix, which is used to convert the state vector x(k + 1) at the next time step into the output signal z(k + 1). z(k + 1) represents the output signal, which is the system output at time step (k + 1). This iterative mechanism ensures the real-time and accuracy of the data, enabling the algorithm to dynamically adapt to the changes in seismic waves. The weighted data fusion filtering algorithm balances the high-frequency characteristics of the accelerometer data and the high-accuracy characteristics of GNSS data by adjusting the weights, and finally outputs the fused high-frequency displacement data and velocity data in the NEU directions. This algorithm not only improves the accuracy and reliability of the data but also provides high-quality displacement data support for the establishment of the earthquake rupture fault model and the construction of the coseismic displacement field in this embodiment.

[0046] In an alternative embodiment, spatio-temporal differences between different data sources are eliminated through time alignment and spatial coordinate transformation, ensuring data consistency and comparability, and providing a unified data framework for subsequent analysis. The use of the weighted data fusion filtering algorithm further optimizes the data integration process. By reasonably allocating weights, the advantages of each data source are fully utilized, improving the accuracy and reliability of the fused data.

[0047] Optionally, performing seismic waveform inversion on the multi-source seismic data to obtain relevant crustal parameters of the preset area includes: Performing forward modeling based on the multi-source seismic data and a preset subsurface medium model to generate a theoretical seismic waveform of the preset area; Comparing the theoretical seismic waveform with the observed waveform of the preset area to determine the error between the theoretical seismic waveform and the observed waveform; Performing iterative optimization based on the error to obtain the relevant crustal parameters.

[0048] Specifically, seismic waveform inversion technology is a geophysical method for inferring the subsurface medium structure and seismic source characteristics through seismic waveform data. The core of seismic waveform inversion is to utilize the propagation characteristics of seismic waves in the subsurface medium. Seismic waves start from the seismic source and propagate through different media to reach the seismographs on the ground. The waveform data recorded by the seismographs contains various information during the propagation of seismic waves, such as wave propagation time, amplitude, and frequency. Through these waveform data, the structure of the subsurface medium and the characteristics of the seismic source can be inverted. The goal of seismic waveform inversion is to find a subsurface medium model that makes the seismic waveform predicted by the model as consistent as possible with the actually observed seismic waveform. First, perform forward modeling based on multi-source seismic data and a preset subsurface medium model to generate a theoretical seismic waveform. Conduct error assessment, calculate the difference between the theoretical seismic waveform and the actually observed waveform, and usually use the least squares method or other optimization objective functions to quantify this difference. Then, perform parameter update. According to the error assessment results, adjust the subsurface medium model and seismic source parameters to reduce the difference between the theoretical waveform and the observed waveform. Conduct iterative optimization, repeat the above steps until an optimal subsurface medium model and seismic source parameters are found, minimizing the difference between the theoretical waveform and the observed waveform. Seismic waveform inversion technology can provide important parameters of the subsurface medium structure and seismic source characteristics by combining seismic waveform data and mathematical models. These parameters include crustal thickness, density, Poisson's ratio, P-wave velocity, and S-wave velocity, etc. Through seismic waveform inversion, these parameters can be estimated more accurately, thus optimizing the crustal velocity model. Further parameter optimization enables the model to more accurately reflect the seismic rupture process and surface deformation characteristics.

[0049] In a preferred embodiment of the present invention, in combination with Figure 4Schematic diagram of the principle information of obtaining relevant crustal parameters for the method of establishing a seismic rupture fault model and constructing a coseismic displacement field by integrating multi-source observation data, showing the principle and information flow of obtaining crustal parameters. Specifically, Figure 4 In the three-dimensional coordinate system (X, Y, Z), it is used to represent the spatial coordinate system of the seismic activity area. The X and Y axes define the horizontal direction, and the Z axis is vertically upward, representing the depth. The hypocenter represents the initial position where the earthquake occurs, which is located inside the crust; seismic waves propagate in all directions from the hypocenter. The epicenter represents the vertical projection point of the hypocenter on the earth's surface; it is usually also the "epicenter position" mentioned in earthquake monitoring and reports. The focal depth represents the vertical distance from the hypocenter to the earth's surface. The epicentral distance represents the straight-line distance from a certain point on the earth's surface (such as the location of an observation station or sensor) to the epicenter; it is used to evaluate the time and intensity of seismic waves reaching different positions. The crust is composed of different geological layers, and each layer has different physical properties (such as density, velocity, etc.); different strata will affect the propagation speed and path of seismic waves. The velocities of P-waves and S-waves; P-waves are the fastest propagating waves in seismic waves and can propagate in solids, liquids, and gases. S-waves propagate more slowly and can only propagate in solids. This part is the key part of the present invention for obtaining relevant crustal parameters, and its main function is to obtain the crustal physical property parameters of the area according to the input longitude and latitude of the center of the seismic area. Specifically, obtain the longitude and latitude of the center of the received seismic area, and through seismic waveform inversion technology, output the results of crustal parameters. These parameters include key data such as the thickness, density, Poisson's ratio, P-wave velocity, and S-wave velocity of the crust, providing necessary basic information for the subsequent establishment of the seismic rupture fault model and the calculation of the coseismic displacement field.

[0050] In an alternative embodiment, by generating theoretical seismic waveforms through forward simulation and comparing them with the actual observed waveforms, the differences between the model and the actual earthquake process can be quantitatively evaluated. The iterative optimization process based on errors further improves the accuracy of the inversion results, enabling the obtained crustal parameters to more accurately reflect the actual geological conditions. It not only provides reliable crustal parameters for the construction of the seismic rupture fault model but also enhances the understanding of the earthquake propagation mechanism, contributing to improving the accuracy of earthquake disaster prediction and assessment.

[0051] Optionally, combining the relevant crustal parameters, using a layered planar dislocation model to calculate the Green's function according to the multi-source seismic data, and obtaining the seismic rupture fault model of the preset area, including: Input the relevant crustal parameters into the layered planar dislocation model, and set the internal stratification model of the earth in the preset area according to the relevant crustal parameters; Discretize the internal stratification model of the earth to obtain multiple sub-layers; Using the Thomson-Haskell propagation algorithm, calculate the propagation data of seismic waves in the sub-layers in sequence, and then obtain the Green's function values of each sub-layer through inverse Hankel transform; Determine the total deformation caused by the seismic waves in the crust through linear superposition; Obtain the seismic rupture fault model based on the total deformation in combination with the Green's function values.

[0052] Specifically, first, use the multi-source data after fusion to calculate the Green's function based on the layered plane dislocation model. Set up the layered model of the Earth's interior, including information such as the focal depth, focal position, receiver depth, etc., and discretize the complex layered model into a series of uniform sub-layers for easy numerical calculation. Calculate the matrix of seismic wave propagation, which describes the propagation of waves between different medium layers. Conduct seismic wave propagation analysis to calculate the response functions of different seismic waves near the seismic source. Perform wavenumber integration to calculate the kernel function of seismic waves, which describes the response of a specific point in the crust under the action of a given seismic source. Execute the superposition of fundamental solutions to calculate the total deformation caused by the seismic source in the crust, and output the geometric model product of the seismic rupture fault. Combine Figure 5 As shown in the process of the customized Green's algorithm, this algorithm solves the problem of seismic wave propagation in complex crustal structures by converting the partial differential equation of motion into a conventional equation in the wavenumber domain, using Hankel transform and Thomson-Haskell propagation algorithm, improving the accuracy and stability of the calculation.

[0053] In a preferred embodiment of the present invention, combine Figure 8 As shown, when the formation model adopts a layered model, the data summary and process required for the customized Green's algorithm. Using the customized Green's function method based on the formation model requires: formation model information, including: the number of layers of the layered model and the thickness of each layer, physical properties (such as shear wave velocity, compressional wave velocity, density, etc.). Physical parameters between layers, that is, the interaction and transmission characteristics between different layers; seismic source information, that is, fault plane information, geometric characteristics of the fault, such as length, width, strike, dip angle, slip angle; slip amount, that is, the amount of slip that occurs on the fault; coordinates of the observation points, that is, the positions of the points that need to be observed within the specified seismic simulation area.

[0054] Specifically, when dealing with a layered model, formation model information is required, including the number of layers in the layered model, the thickness of each layer, physical properties (such as shear wave velocity, compressional wave velocity, density, etc.), and physical parameters between layers, that is, the interaction and transmission characteristics between different layers. At the same time, seismic source information needs to be input, including the geometric characteristics of the fault plane (such as length, width, strike, dip angle, slip angle) and the slip amount. The coordinates of the observation points are also crucial, which are used to specify the positions of the points to be observed within the seismic simulation area. Next, these data are fused through a customized Green's algorithm, integrating the formation model, seismic source, and observation point information, and using the customized Green's function to simulate the propagation of seismic waves and calculate the coseismic displacement field of the surface and subsurface structures caused by the earthquake. Finally, the regional coseismic displacement field is output, reflecting the displacement changes of each observation point within the region; the entire process realizes the accurate simulation of the earthquake rupture process and the coseismic displacement field by combining the formation model information, seismic source information, and observation point coordinates, providing important theoretical support and data basis for earthquake research and disaster prevention and control.

[0055] In another preferred embodiment of the present invention, specifically, it is divided into seven steps, which are respectively: Input the geometric model of the earthquake rupture fault and the parameters of the crustal velocity model. Specifically: The input of the algorithm includes the geometric model of the earthquake rupture fault and the parameters of the crustal velocity model. These parameters provide the location of the seismic source, the geometry of the fault, and the physical properties of the crust, such as thickness, density, Poisson's ratio, longitudinal wave velocity, and shear wave velocity, etc. Combining Figure 6 with the principle of the customized Green's algorithm to solve the response of the seismic source in the medium, Figure 6 which indicates the location and characteristics of the seismic source and the Cartesian coordinates of the reference point. These parameters define the basic characteristics of the seismic source and are the basis for calculating the propagation of seismic waves and the surface response. Through iterative or recursive methods, calculate the propagation of seismic waves in the multi-layer elastic crust, starting from the layer where the seismic source is located, propagating layer by layer upward or downward, and the wave field is updated by applying the corresponding propagation matrix until reaching the target layer or the calculated depth. Among them, the three-dimensional coordinate system is marked with the X, Y, and Z axes, and the origin is marked as "0". There are two points on the Z axis: ( , , ) and ( , , ), and there is also a point M(x,y,z) located at a certain position in space. ( , , ) represents the location of the seismic source, and its coordinates ( , , ) gives its specific location in the crust. ( , , ) represents the mirror point of the earthquake source on the Earth's surface and is used to model the reflected wave. M(x, y, z) is an observation point in space, the location where the seismic wave arrives, and its coordinates (x, y, z) represent its position relative to the earthquake source. The dashed lines connecting these points represent the propagation paths of the seismic waves, including the direct wave and the reflected wave. The "±" marks in the figure indicate the phase or propagation direction of the wave.

[0056] Set up the internal layered model. Specifically: Include setting up the layered model of the Earth's interior. Include information such as the source depth, source location, receiver depth, etc. Then calculate the layer thickness and depth. And discretize the complex layered model into a series of uniform sub-layers to facilitate numerical calculations. Make the subsequent seismic wave propagation calculations more feasible.

[0057] Calculate and store the table of Bessel functions. Specifically: Calculate and store the table of Bessel functions. The Bessel functions can discretize the complex medium model and enable more efficient numerical calculations.

[0058] Set the parameters of the earthquake source. Specifically: These parameters describe the type and intensity of the earthquake source. The subroutine determines the type of the earthquake source according to the values of the stress function array and accordingly sets the stress function values in the stress function derivative array. These stress functions are used to describe the moments of the earthquake source in various directions.

[0059] Calculate the matrix for seismic wave propagation. Specifically: Use two different model operators to calculate the response functions of different seismic waves near the earthquake source, calculate the matrix for seismic wave propagation, and these matrices are used to describe the propagation of waves between different medium layers. Use two different model operators to calculate the response functions of different seismic waves near the earthquake source.

[0060] Perform wavenumber integration to transform the seismic wave from the frequency domain to the spatial domain. Specifically: Use the Thomson-Haskell propagation algorithm to calculate the propagation of seismic waves in a multi-layer elastic crust. This algorithm simulates the process of seismic waves propagating from the source point to a distance through iteration or recursion, considering the physical properties of each layer of the medium. This step ensures the accuracy and stability of the calculation, especially when dealing with complex crustal structures. Transform the solution in the wavenumber domain back to the spatial domain. This step is achieved through the inverse Hankel transform, which transforms the calculation results from the wavenumber domain back to the actual spatial domain, generating results such as displacement vectors, strain tensors, stress tensors, and vertical tilts. Store the calculated Green's function values in an array, and these values are subsequently used to simulate the propagation of seismic waves in the Earth's interior.

[0061] Perform kernel function calculations. Specifically: describe the response of a specific point in the crust under the action of a given seismic source, solve a system of linear equations, and calculate the total deformation caused by the seismic source in the crust. Through linear superposition (convolution integral), calculate the total deformation caused by the seismic source in the crust. This step superimposes multiple fundamental solutions to generate the final deformation field, provides detailed information on the surface displacement, strain, and stress distributions caused by the earthquake, and outputs the customized Green's function of the corresponding model. The three models are the compensated linear vector dipole model, the dip-slip model, and the strike-slip model.

[0062] In an alternative embodiment, by inputting relevant crustal parameters into a layered planar dislocation model and performing discretization processing, the heterogeneity of the crust can be more accurately reflected, improving the accuracy of the model. The use of the Thomson-Haskell propagation algorithm and the inverse Hankel transform enables the efficient calculation of the propagation characteristics of seismic waves in complex media, providing key support for the simulation of the earthquake rupture process. The total deformation and the earthquake rupture fault model finally obtained through linear superposition clearly show the dislocation distribution and the rupture propagation path during the earthquake rupture process.

[0063] Optionally, the inversion of the earthquake rupture fault model by the gradient algorithm to obtain the earthquake rupture surface of the preset area includes: Obtain model data according to the earthquake rupture fault model; Perform geometric correction on the model data and grid the faults in the preset area to obtain the fault structure of the preset area; Perform model calculations according to the fault structure, and then perform inversion according to the calculation results of the model calculations to obtain the earthquake rupture surface.

[0064] Specifically, the process of obtaining the earthquake rupture surface by inversely calculating the earthquake rupture fault model is a complex step that combines numerical simulation and optimization algorithms. First, model data are obtained based on the constructed earthquake rupture fault model. These data include the geometric characteristics of the fault, dislocation distribution, and physical parameters related to seismic wave propagation. For example, in earthquake research, the model data may involve the strike, dip, slip angle of the fault, and the amount of dislocation at different depths. Secondly, geometric correction is performed on the model data to ensure that the geometric characteristics of the fault are consistent with the actual geological conditions. At the same time, the fault in the preset area is meshed, and the complex fault structure is discretized into multiple small units for more refined numerical calculations. The accuracy of the meshing directly affects the accuracy of the inversion result. Finally, model calculations are performed based on the meshed fault structure, and the gradient algorithm is used to inversely calculate the calculation results. The gradient algorithm iteratively optimizes the model parameters, gradually reducing the difference between the model prediction and the actual observed data, and finally obtaining the earthquake rupture surface. This process can clearly reveal the dislocation distribution and rupture propagation path on the fault plane during the earthquake rupture process, providing key information for earthquake mechanism research.

[0065] In a preferred embodiment of the present invention, in combination with Figure 7 As shown, the working process of the earthquake rupture surface inversion calculation is exemplarily given by the working flowchart of the inversion calculation: Obtain earthquake rupture fault model data; perform data geometric correction and mesh the fault; data geometric correction is to ensure the accuracy and consistency of the data, and fault meshing helps to more accurately describe the earthquake fault structure; calculate the dip angle of the earthquake fault, calculate the landslide gradient loss function and the slip gradient loss function; read the Green's function, calculate the fault slip compensated point source, calculate the maximum gradient of the loss function. Specifically, read the Green's function to provide the basic model parameters of the earthquake model; calculate the fault slip compensated point source to handle the complexity of the fault; calculate the maximum gradient of the loss function; calculate the slip model displacement field, calculate the sub-region slip gradient, and obtain the model fitting result. Specifically, calculate the slip model displacement field to provide the fitting result of the model; calculate the sub-region slip gradient; perform earthquake displacement processing to obtain the calculation model. Specifically, performing earthquake displacement processing can ensure that the earthquake displacement is properly processed. Calculate the slip gradient of the loss function to optimize the convergence of the process; perform inversion calculation based on the obtained model. Specifically, the program is responsible for performing inversion calculation according to the given data and model parameters, and uses the main inversion program to coordinate and call other subroutines to complete the entire inversion process, and outputs the fitting result and residual of the inversion.

[0066] In an alternative embodiment, by performing geometric correction and meshing on the seismic rupture fault model, it is possible to ensure that the geometric features of the model are highly consistent with the actual geological conditions, while improving the accuracy and efficiency of numerical calculations. The use of the gradient algorithm further optimizes the inversion process. By iteratively adjusting the model parameters, it is possible to more accurately fit the actual observed data, thereby obtaining a high-precision seismic rupture surface.

[0067] Optionally, the model calculation is performed according to the fault structure, and then the inversion is performed according to the calculation result of the model calculation to obtain the seismic rupture surface, including: Performing model calculation according to the fault structure to obtain the seismic fault dip angle, landslide gradient loss function, and sliding gradient loss function of the preset area, and calculating the fault sliding compensation point source, maximum gradient of the loss function, sliding model displacement field, and sub-region sliding gradient; Taking the seismic fault dip angle, the landslide gradient loss function, the sliding gradient loss function, the sliding compensation point source, the maximum gradient of the loss function, the sliding model displacement field, and the sub-region sliding gradient as the calculation result, and performing inversion on the calculation result through an analytical algorithm for surface deformation caused by internal shear and tensile faults in space to obtain the seismic rupture surface.

[0068] Specifically, a series of key parameters need to be obtained through model calculation first. These parameters include the seismic fault dip angle, landslide gradient loss function, sliding gradient loss function, fault sliding compensation point source, maximum gradient of the loss function, sliding model displacement field, and sub-region sliding gradient. These parameters reflect the geometric features of the fault and the physical properties during the seismic rupture process. Among them, the seismic fault dip angle describes the inclination degree of the fault, the landslide gradient loss function and the sliding gradient loss function are used to quantify the non-uniformity of fault sliding, and the fault sliding compensation point source is used to describe the distribution characteristics of fault sliding. Through the calculation of these parameters, the dynamic changes of the fault during the seismic rupture process can be comprehensively understood. Subsequently, taking these calculation results as input, an analytical algorithm for surface deformation caused by internal shear and tensile faults in space is used for inversion. This analytical algorithm can analyze the sliding characteristics of the fault and its influence on surface deformation during the seismic rupture process through a mathematical model, thereby obtaining the seismic rupture surface. This process not only considers the physical process inside the fault but also combines the surface observation data, making the inversion result more accurately reflect the actual seismic rupture situation.

[0069] In an alternative embodiment, by calculating key parameters such as the seismic fault dip angle and landslide gradient loss function, it is possible to comprehensively capture the complex physical phenomena during the seismic rupture process, providing rich information for inversion. The use of the analytical algorithm further improves the inversion accuracy, enabling the characteristics of the seismic rupture surface to be clearly revealed.

[0070] Optionally, parsing the earthquake rupture surface to obtain the coseismic displacement field includes: Discretizing the earthquake rupture surface into multiple discrete double-couple sources; Determining the coseismic displacement field by parsing according to the discrete double-couple sources.

[0071] Specifically, the earthquake rupture surface is discretized into multiple discrete double-couple sources. This process decomposes the complex earthquake rupture surface into a series of simple point sources. Each point source represents a tiny area on the rupture surface and is assumed to release energy in the form of a double-couple source. As a commonly used method for describing earthquake ruptures, the double-couple source model can effectively simulate the shear and tensile rupture characteristics on the fault plane during the earthquake rupture process. Through the discretization process, the complex rupture surface problem can be transformed into a superposition problem of multiple point sources, facilitating analytical calculations. Subsequently, analytical calculations are performed based on the discrete double-couple sources to determine the coseismic displacement field. The analytical calculations are based on the theory of seismic wave propagation and Green's function. By calculating the displacement responses caused by each discrete double-couple source on the ground surface and linearly superposing these responses, the coseismic displacement field caused by the entire earthquake rupture surface is finally obtained. This process can describe in detail the displacement changes on the ground surface during an earthquake, including the distribution of horizontal and vertical displacements. For example, in earthquake research, through this method, the deformation characteristics of the ground surface during the earthquake rupture process can be clearly demonstrated, providing key data support for earthquake disaster assessment and earthquake engineering.

[0072] In an alternative embodiment, by discretizing the earthquake rupture surface into multiple discrete double-couple sources, the complex rupture surface problem can be effectively simplified, making it easier to perform analytical calculations. The use of the double-couple source model further improves the description accuracy of earthquake rupture characteristics, making the calculation results of the coseismic displacement field more accurate.

[0073] Optionally, determining the coseismic displacement field by parsing according to the discrete double-couple sources includes: Obtaining a conventional equation in the wavenumber domain through Hankel custom transform according to the distribution and parameters of the discrete double-couple sources; Determining a particular solution that satisfies the source and boundary conditions according to the conventional equation through the Thomson–Haskell propagator algorithm; Converting the particular solution into a solution in the spatial domain through inverse Hankel; Performing a linear superposition operation according to the solution in the spatial domain to obtain seismic change data; Obtaining the coseismic displacement field according to the seismic change data.

[0074] Specifically, through the Hankel customized transform, according to the distribution and parameters of the discrete point double-couple sources, the problem is transformed from the spatial domain to the wavenumber domain to obtain the conventional equation. This transform utilizes the advantage of the Hankel transform in dealing with radially symmetric problems and can effectively simplify the mathematical description of seismic wave propagation. Subsequently, using the Thomson–Haskell propagator algorithm, a particular solution satisfying the source and boundary conditions is solved according to the conventional equation in the wavenumber domain. The Thomson–Haskell algorithm is a classic numerical method for seismic wave propagation, especially suitable for wave propagation problems in layered media, and can efficiently calculate the propagation path and response of seismic waves in complex media. Then, the particular solution in the wavenumber domain is transformed into a solution in the spatial domain through the inverse Hankel transform. This process restores the abstract wavenumber domain results to actual physical quantities, providing a basis for subsequent analysis. Finally, based on the solution in the spatial domain, a linear superposition operation is performed to obtain the seismic change data, and the coseismic displacement field is obtained therefrom. The application of the linear superposition principle here is based on the linear characteristics of seismic wave propagation. By superimposing the responses of multiple discrete point double-couple sources, the influence of the earthquake rupture process on the surface displacement can be completely described. For example, in seismic research, through this method, the displacement changes of each point on the surface during an earthquake can be accurately calculated, providing key data support for earthquake disaster assessment and earthquake engineering.

[0075] In a preferred embodiment of the present invention, a customized Green's algorithm or an analytical algorithm for surface deformation caused by internal shear and tensile faults in space is used to simulate the interaction between seismic waves and underground structures. The finite rectangular fault is discretized into multiple discrete point double-couple sources, and the partial differential equation of motion is transformed into a conventional equation in the wavenumber domain using the Hankel customized transform. A particular solution satisfying the source and boundary conditions is obtained through the Thomson–Haskell propagator algorithm. The whole process is processed by analyzing the solution, and numerical calculations are required when performing the inverse Hankel customized transform. And the deformation field caused by the earthquake is calculated through linear superposition (convolution integral), and results such as displacement vectors, strain tensors, stress tensors, and vertical tilts are output to obtain the coseismic displacement field on the surface.

[0076] In an alternative embodiment, by the combined use of the Hankel transform and the Thomson–Haskell algorithm, the propagation problem of seismic waves in complex media can be efficiently handled, ensuring the accuracy and reliability of the calculation results. The use of the inverse Hankel transform further converts the solution in the wavenumber domain into the actual physical quantities in the spatial domain, making the results more practically meaningful. The co-seismic displacement field finally obtained through linear superposition can clearly show the deformation distribution on the ground surface during an earthquake, providing an important theoretical basis for earthquake disaster assessment, earthquake engineering design, and earthquake emergency response. This method not only improves the scientific nature of earthquake research but also enhances the accuracy and effectiveness of earthquake disaster response, providing strong support for earthquake science and engineering applications.

[0077] Combined Figure 9 As shown, a system for constructing an earthquake rupture fault model and a co-seismic displacement field provided by an embodiment of the present invention includes: A data acquisition unit, configured to acquire GNSS data, strong motion seismograph data, and accelerometer data of a preset area; A data fusion unit, configured to perform fusion processing on the GNSS data, the strong motion seismograph data, and the accelerometer data to obtain multi-source seismic data of the preset area; A first inversion unit, configured to perform seismic waveform inversion on the multi-source seismic data to obtain relevant crustal parameters of the preset area; A fault model construction unit, configured to calculate a Green's function using a layered planar dislocation model according to the multi-source seismic data in combination with the relevant crustal parameters to obtain an earthquake rupture fault model of the preset area; A second inversion unit, configured to perform inversion on the earthquake rupture fault model through a gradient algorithm to obtain an earthquake rupture surface of the preset area; A displacement field construction unit, configured to analyze the earthquake rupture surface to obtain a co-seismic displacement field.

[0078] The earthquake rupture fault model and the co-seismic displacement field construction system of the present invention eliminate the limitations of a single data source through the fusion processing of multi-source data. For example, the high-precision displacement information of GNSS data is combined with the high-frequency seismic waveform information of strong motion instruments and accelerometers, which can more comprehensively reflect the dynamic changes on the surface and underground during an earthquake, give full play to the advantages of each data source, make up for the deficiencies of a single data source, and thus more comprehensively reflect the dynamic changes on the surface and underground during the earthquake process. Secondly, the application of seismic waveform inversion technology further optimizes the estimation of crustal parameters, providing a more accurate physical basis for subsequent model calculations. Furthermore, the Green's function calculation based on the layered planar dislocation model takes into account the complex layered structure of the crust, and can more realistically simulate the propagation and deformation effects of seismic waves in multi-layer media, thereby improving the accuracy of the earthquake rupture fault model. Finally, the earthquake rupture fault model is inverted through the gradient algorithm, and the co-seismic displacement field is calculated by combining the analytical algorithm, further improving the fitting accuracy and reliability of the model. The method of combining multi-source data fusion with advanced algorithms in the present invention can not only effectively reduce the influence of noise interference and data errors, but also more accurately reveal the earthquake occurrence mechanism and surface deformation characteristics, providing more accurate and reliable data support for various tasks such as seismological research, earthquake disaster assessment, and earthquake prevention and mitigation.

[0079] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will all fall within the protection scope of the present invention.

Claims

1. A method for constructing an earthquake rupture fault model and a co-seismic displacement field, characterized in that: include: Obtain GNSS data, strong motion data and accelerometer data for a preset area; fusing the GNSS data, the strong motion detector data and the accelerometer data to obtain multi-source seismic data of the preset area; Performing seismic waveform inversion on the multi-source seismic data to obtain relevant crustal parameters of the preset area; In combination with the relevant crustal parameters, Green's function is calculated according to the multi-source seismic data using a layered plane dislocation model to obtain an earthquake rupture fault model of the preset area; Inverting the earthquake rupture fault model by using a gradient algorithm to obtain an earthquake rupture surface in the preset area; The earthquake rupture surface is analyzed to obtain the co-seismic displacement field.

2. The method for constructing an earthquake rupture fault model and a co-seismic displacement field according to claim 1, characterized in that: The obtaining of GNSS data, strong motion detector data and accelerometer data of a preset area includes: Determine the surface position and displacement information of the preset area by satellite positioning technology, and use the surface position and the displacement information as the GNSS data; Determine the ground acceleration, ground velocity and ground displacement of the preset area by recording data of the strong motion seismograph, and use the ground acceleration, ground velocity and ground displacement as the strong motion seismograph data; The acceleration change values ​​of the ground and the building in the preset area are determined by the acceleration sensor, and the acceleration change values ​​are used as the accelerometer data.

3. The method for constructing an earthquake rupture fault model and a co-seismic displacement field according to claim 1, characterized in that: The fusing the GNSS data, the strong motion detector data and the accelerometer data to obtain the multi-source seismic data of the preset area includes: Performing time alignment processing and space coordinate conversion processing on the GNSS data, the strong seismometer data and the accelerometer data according to the same time reference and space reference, respectively, to obtain the GNSS data, the strong seismometer data and the accelerometer data with time and space differences eliminated; By using a weighted data fusion filtering algorithm, weights are respectively assigned to the GNSS data, the strong seismometer data and the accelerometer data to eliminate temporal and spatial differences; The multi-source seismic data is obtained by fusing the GNSS data, the strong seismograph data and the accelerometer data according to the weights corresponding to the data that eliminate the temporal and spatial differences.

4. The method for constructing an earthquake rupture fault model and a co-seismic displacement field according to claim 1, characterized in that: The performing seismic waveform inversion on the multi-source seismic data to obtain relevant crustal parameters of the preset area includes: Perform forward simulation based on the multi-source seismic data and a preset underground medium model to generate a theoretical seismic waveform for the preset area; By comparing the theoretical seismic waveform with the observed waveform of the preset area, an error between the theoretical seismic waveform and the observed waveform is determined; Iterative optimization is performed according to the error to obtain the relevant crustal parameters.

5. The method for constructing an earthquake rupture fault model and a co-seismic displacement field according to claim 1, characterized in that: The method of combining the relevant crustal parameters and calculating the Green's function using a layered plane dislocation model according to the multi-source seismic data to obtain an earthquake rupture fault model of the preset area includes: Inputting the relevant crustal parameters into the layered plane dislocation model, and setting the earth interior layered model of the preset area according to the relevant crustal parameters; Discretizing the Earth's interior layered model to obtain multiple sublayers; The propagation data of seismic waves in the sub-layers are calculated in sequence by using the Thomson-Haskell propagation algorithm, and then the Green function value of each sub-layer is obtained by using the inverse Hankel transform; Determine the total deformation caused by the seismic wave in the earth's crust by linear superposition; The earthquake rupture fault model is obtained according to the total deformation combined with the Green's function value.

6. The method for constructing an earthquake rupture fault model and a co-seismic displacement field according to claim 1, characterized in that: The inversion of the earthquake rupture fault model by a gradient algorithm to obtain the earthquake rupture surface of the preset area includes: Acquiring model data according to the earthquake rupture fault model; Performing geometric correction on the model data and meshing the faults in the preset area to obtain the fault structure of the preset area; Model calculation is performed according to the fault structure, and then inversion is performed according to the calculation results of the model calculation to obtain the earthquake rupture surface.

7. The method for constructing an earthquake rupture fault model and a co-seismic displacement field according to claim 6, characterized in that: The method of performing model calculation according to the fault structure and then performing inversion according to the calculation result of the model calculation to obtain the earthquake rupture surface includes: Performing model calculation according to the fault structure to obtain the earthquake fault dip angle, landslide gradient loss function and sliding gradient loss function of the preset area, and calculating the fault sliding compensation point source, the maximum gradient of the loss function, the sliding model displacement field and the sub-area sliding gradient; The earthquake fault dip, the landslide gradient loss function, the sliding gradient loss function, the sliding compensation point source, the maximum gradient of the loss function, the sliding model displacement field and the sub-region sliding gradient are taken as the calculation results, and the calculation results are inverted through an analytical algorithm of surface deformation caused by internal shear and tensile faults in space to obtain the earthquake rupture surface.

8. The method for constructing an earthquake rupture fault model and a co-seismic displacement field according to claim 1, characterized in that: The analyzing the earthquake rupture surface to obtain the co-seismic displacement field includes: discretizing the earthquake rupture surface into a plurality of discrete point double-even sources; The co-seismic displacement field is determined by analyzing the discrete point double-dual sources.

9. The method for constructing an earthquake rupture fault model and a co-seismic displacement field according to claim 8, characterized in that: The determining the co-seismic displacement field by analyzing the discrete point double-even sources includes: Through the Hankel custom transformation, according to the distribution and parameters of the discrete point double-even source, a conventional equation in the wave number domain is obtained; Determining a special solution satisfying source and boundary conditions from the general equations by a Thomson–Haskell propagator algorithm; By inverse Hankel, the special solution is converted into a solution in the space domain; Performing linear superposition operation according to the solution in the spatial domain to obtain seismic change data; The co-seismic displacement field is obtained according to the seismic change data.

10. A system for constructing an earthquake rupture fault model and a co-seismic displacement field, characterized in that: include: A data acquisition unit, used to acquire GNSS data, strong seismometer data and accelerometer data of a preset area; A data fusion unit, used for fusing the GNSS data, the strong motion detector data and the accelerometer data to obtain multi-source seismic data of the preset area; A first inversion unit is used to perform seismic waveform inversion on the multi-source seismic data to obtain relevant crustal parameters of the preset area; A fault model building unit, used to calculate the Green's function using a layered plane dislocation model according to the multi-source seismic data in combination with the relevant crustal parameters, to obtain an earthquake rupture fault model of the preset area; A second inversion unit is used to invert the earthquake rupture fault model by using a gradient algorithm to obtain an earthquake rupture surface in the preset area; The displacement field construction unit is used to analyze the earthquake rupture surface to obtain the co-seismic displacement field.

Images