A method and system for monitoring seismic faults based on Beidou

Abstract

The application provides a Beidou-based earthquake fault monitoring method and system, and relates to the technical field of earthquake fault monitoring.The method comprises the following steps: acquiring Beidou satellite data of multiple monitoring points of an earthquake fault within a preset sampling time; preprocessing each Beidou satellite data, and inputting each preprocessed Beidou satellite data into a preset prediction model to predict a displacement amount; calculating the sliding amount of each monitoring point at each time step in the future based on each predicted displacement amount at each time step; and performing risk early warning of the earthquake fault based on the change of the sliding amount of each monitoring point at each time step in the future, to obtain a risk early warning result.The application can more timely and accurately monitor the earthquake fault by predicting the sliding amount of multiple monitoring positions of the earthquake fault in the future multiple time steps based on the Beidou satellite data, and evaluating the risk of the earthquake fault according to the change of the predicted sliding amount in the future multiple time steps.

Description

Technical Field

[0001] This invention relates to the field of earthquake fault monitoring technology, and more specifically, to a method and system for earthquake fault monitoring based on the BeiDou Navigation Satellite System. Background Technology

[0002] Earthquake fault monitoring is a crucial means of early detection of fault activity and potential earthquake risks. The results of earthquake fault monitoring are used in earthquake early warning, disaster assessment, and scientific research, with the ultimate goal of minimizing the impact of earthquake disasters on human society. Through long-term, real-time monitoring and analysis of earthquake fault data, researchers can better understand the mechanisms of earthquake occurrence, improve earthquake prediction and emergency response capabilities, and thus provide strong support for safeguarding people's lives and property and maintaining social stability.

[0003] Traditional earthquake fault monitoring requires the installation of a large number of ground devices at specific locations and the collection and processing of data by humans. This not only has problems such as limited monitoring range and high dependence on ground facilities and manual maintenance, but also the real-time performance and accuracy of the acquired data are not ideal.

[0004] The global coverage and all-weather capability of the BeiDou system enable a wider monitoring range for earthquake faults, especially in remote or hard-to-reach areas, thus filling the blind spots of traditional ground-based monitoring. Furthermore, the millimeter-level positioning accuracy and near-zero latency of real-time data provided by BeiDou satellites significantly improve monitoring accuracy and response speed, thereby enhancing earthquake early warning and fault activity detection capabilities. At the same time, the cost of data acquisition and processing based on the BeiDou satellite system is relatively low, reducing reliance on ground facilities and manual maintenance.

[0005] Therefore, there is an urgent need for a method and system for earthquake fault monitoring based on BeiDou data, in order to improve the accuracy and real-time performance of earthquake fault monitoring while reducing operating costs. Summary of the Invention

[0006] The purpose of this invention is to provide a seismic fault monitoring method and system based on BeiDou navigation satellite system to improve the aforementioned problems. To achieve this objective, the technical solution adopted by this invention is as follows:

[0007] Firstly, this application provides a BeiDou-based method for earthquake fault monitoring, including:

[0008] Acquire BeiDou satellite data from multiple monitoring points along the earthquake fault within a preset sampling time.

[0009] Each BeiDou satellite data is preprocessed, and the preprocessed BeiDou satellite data is input into a preset prediction model to predict the displacement, resulting in multiple predicted displacements. Each predicted displacement includes the displacement of a monitoring point over multiple future time steps.

[0010] Based on each predicted displacement at each time step, calculate the slip amount of each monitoring point at each future time step to obtain multiple slip amounts;

[0011] Based on the change in the slip at each of the monitoring points at each future time step, a risk warning of the earthquake fault is generated, and a risk warning result is obtained.

[0012] Secondly, this application also provides a BeiDou-based earthquake fault monitoring system, including:

[0013] The acquisition module is used to acquire BeiDou satellite data from multiple monitoring points of the earthquake fault within a preset sampling time.

[0014] The first processing module is used to preprocess each Beidou satellite data and input the preprocessed Beidou satellite data into a preset prediction model to predict the displacement, thereby obtaining multiple predicted displacements. Each predicted displacement includes the displacement of a monitoring point over multiple future time steps.

[0015] The second processing module is used to calculate the sliding amount of each monitoring point in each future time step based on each predicted displacement at each time step, and obtain multiple sliding amounts.

[0016] The third processing module is used to perform risk warning of earthquake faults based on the change of slip at each time step in the future for each of the monitoring points, and to obtain risk warning results.

[0017] The beneficial effects of this invention are as follows:

[0018] This invention preprocesses BeiDou satellite data and then predicts the slip volume of multiple monitoring locations of earthquake faults at multiple time steps based on the preprocessed BeiDou satellite data. The risk of earthquake faults is assessed based on the changes in the predicted slip volume at multiple time steps, enabling more real-time and accurate monitoring of earthquake faults.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of a BeiDou-based earthquake fault monitoring method as described in an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of a BeiDou-based earthquake fault monitoring system as described in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0024] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0025] Example 1:

[0026] This embodiment provides a method for earthquake fault monitoring based on BeiDou.

[0027] See Figure 1 The figure shows that the method includes steps S1, S2, S3 and S4.

[0028] S1. Obtain BeiDou satellite data from multiple monitoring points along the earthquake fault within a preset sampling time.

[0029] Understandably, the BeiDou satellite system possesses high-precision positioning capabilities, providing centimeter-level accuracy, which is crucial for the accurate monitoring and prediction of earthquake fault displacement. Simultaneously, the BeiDou system can perform real-time observations, instantly acquiring information about crustal movement before and after an earthquake, improving the real-time nature of overall earthquake fault displacement prediction, and thus more quickly identifying the activity status and changing trends of faults. Furthermore, based on the high spatial resolution of satellite signal receivers, it can more accurately monitor the minute impacts of seismic activity on crustal changes, thereby providing more precise data for subsequent prediction of earthquake fault displacement.

[0030] Specifically, in this embodiment, BeiDou satellite signal receivers are deployed every 10 km along the earthquake fault to be monitored. Each BeiDou satellite signal receiver location is used as a monitoring point, and the sampling frequency is set to 1 Hz with a time window of 24 hours, meaning each sampling period lasts 24 hours, for collecting BeiDou satellite data. It is understood that the deployment interval, sampling frequency, and time window of the BeiDou satellite signal receivers can be adjusted to adapt to different monitoring needs.

[0031] S2. Preprocess each BeiDou satellite data, and input the preprocessed BeiDou satellite data into a preset prediction model to predict the displacement, thereby obtaining multiple predicted displacements. Each predicted displacement includes the displacement of a monitoring point over multiple future time steps.

[0032] Understandably, raw BeiDou satellite data mainly includes carrier phase, pseudorange, Doppler shift, timestamp, and satellite ephemeris. This data can be used to calculate the real-time geographical location of the BeiDou satellite receiver, thereby inferring the surface changes at the receiver's location based on the changes in the receiver's geographical location at different time steps. However, raw BeiDou satellite data cannot be directly used to analyze surface deformation at monitoring points. Data preprocessing is required to convert the raw BeiDou satellite data into a time series, facilitating the calculation and description of the monitoring point's location changes over time. Therefore, preprocessing is necessary for each BeiDou satellite data set. Specific steps include:

[0033] S211. Cycle slip detection and repair are performed on the carrier phase data of each BeiDou satellite data set, and error correction is applied to each carrier phase-repaired BeiDou satellite data set to obtain multiple BeiDou satellite data sets with error correction. By fitting the carrier phase data, residual abnormalities are detected, and cycle slips are repaired through interpolation or reinitialization. After cycle slip repair, systematic errors in the BeiDou satellite data need to be eliminated. In this embodiment, ionospheric delay is calculated and eliminated using dual-frequency observations, then tropospheric calibration is performed using the existing Saastamoinen model, and satellite orbital errors and clock errors are repaired using precise ephemeris and clock bias products. Finally, multipath interference is reduced through filtering. It is understood that the above-described cycle slip detection, repair, and error correction of the original BeiDou satellite data are commonly used methods by those skilled in the art.

[0034] S212. Position calculation is performed on each of the error-corrected BeiDou satellite data sets, and a surface deformation time series for each monitoring point is generated based on the position calculation results, resulting in multiple surface deformation time series. Each surface deformation time series includes the position, velocity, and acceleration of a monitoring point at each time step of the sampling time. It is understood that when generating the surface deformation time series for each monitoring point, the corrected BeiDou satellite data is first converted into the receiver's three-dimensional position, i.e., the longitude, latitude, and elevation of each monitoring point where the receiver is located; then, the initial position of the receiver is calculated using pseudorange observations; then, differential processing is performed using carrier phase data from the base station and rover to eliminate common errors; finally, the precise position of the receiver is calculated using Kalman filtering to obtain the receiver's three-dimensional coordinates at each time step.

[0035] After obtaining the three-dimensional coordinates of the receiver at each time step, the velocity and acceleration of the receiver at each time step are calculated by the time difference method. In this way, the three-dimensional coordinates, three-dimensional velocity and three-dimensional acceleration of each receiver at each time step can be timestamped to obtain the surface deformation time series of each receiver.

[0036] S213. Generate a spatiotemporal matrix based on multiple surface deformation time series, thus obtaining the spatiotemporal matrix. It is understood that after obtaining the surface deformation sequences from each receiver, since these data will be subsequently input into a preset neural network for processing, it is necessary to further construct a spatiotemporal matrix based on all the obtained surface deformation time series. Specific steps include:

[0037] First, the receiver data is resampled according to a unified timestamp to ensure that the time points of all surface deformation time series are aligned. Then, an adjacency matrix is ​​generated based on the geographical location of the receiver. The generated adjacency matrix is ​​used to extract the spatial features of the spatiotemporal matrix. Finally, the sequences from all receivers are stacked into a three-dimensional matrix to obtain the spatiotemporal matrix, which contains the deformation features of all monitoring points within the time window.

[0038] After data preprocessing is completed, the resulting spatiotemporal matrix will be used as input to a preset prediction model, which will predict the displacement of each monitoring point over multiple future time steps.

[0039] S3. Based on each predicted displacement at each time step, calculate the slip volume of each monitoring point at each future time step to obtain multiple slip volumes. It is understood that the slip volume of a seismic fault is a necessary basis for assessing its activity. This invention, after predicting the displacement of multiple monitoring points of a seismic fault at multiple future time steps, further converts the displacement into slip volume. By observing the slip volume and its changing trends at multiple time steps, it not only more intuitively displays the activity of various regions of the seismic fault but also provides a data foundation for more comprehensive risk warning, achieving better monitoring of the seismic fault.

[0040] Specifically, step S3 includes:

[0041] S31. Obtain first information, the first information including the strike angle, dip angle and locking depth of the earthquake fault;

[0042] S32. Construct a local coordinate system with a preset reference point as the origin. It is understood that a reference point on the fault is usually chosen as the center of the locked segment or a known rupture point. The X-axis of the local coordinate system is the horizontal direction along the fault strike, the Y-axis is perpendicular to the fault strike and points towards the footwall, and the Z-axis is perpendicular to the fault plane, determined by the right-hand rule. After establishing the local coordinate system, construct a coordinate transformation matrix using the unit vectors in each direction of the local coordinate system for subsequent coordinate transformations.

[0043] S33. Based on the first information and the local coordinate system, a fault geometric model is established to obtain the fault geometric model. It is understood that the fault geometric model includes strike angle, dip angle, locking depth, and coordinate transformation matrix, providing a spatial framework for fault activity. Fault slip is movement along the fault plane; therefore, a fault geometric model needs to be established to clarify the three-dimensional geometric parameters of the fault in order to project surface displacement onto the fault plane.

[0044] S34. Calculate the slip amount based on the fault geometry model and the displacement of each monitoring point at each future time step to obtain the slip amount of each monitoring point at each future time step.

[0045] Specifically, after establishing the local coordinate system and the fault geometric model, the receiver displacement needs to be projected onto the fault's local coordinate system first. Then, the tangential slip is calculated based on the displacement projected onto the local coordinate system. The calculation formula is as follows:

[0046]

[0047] Where, Δs i Let be the slip value of the i-th receiver. Let be the displacement of the i-th receiver projected onto the X-axis in the local coordinate system. The displacement projection of the i-th receiver onto the Y-axis direction of the local coordinate system.

[0048] S4. Based on the change in the slip amount of each monitoring point at each future time step, conduct a risk warning of the earthquake fault and obtain the risk warning result.

[0049] Example 2:

[0050] The only difference between this embodiment and Embodiment 1 is that, further, in step S2, inputting each preprocessed BeiDou satellite data into a preset prediction model for displacement prediction includes the following steps:

[0051] S221. Based on a preset one-dimensional convolutional neural network, local features are extracted from the spatiotemporal matrix to obtain the first output feature. The one-dimensional convolutional neural network extracts local features of each surface deformation time series along the time dimension, such as acceleration abrupt changes.

[0052] S222. Based on a preset bidirectional long short-term memory network, the long-range time dependency of the first output feature is extracted to obtain a time feature matrix. The bidirectional long short-term memory network extracts the long-range time dependency of the output of the one-dimensional convolutional neural network, thereby outputting a time feature matrix and completing the extraction of the time features of the spatiotemporal matrix.

[0053] S223. Generate a spatial adjacency matrix based on the location of the monitoring points, and input the spatial adjacency matrix and the spatiotemporal matrix into a preset graph convolutional network for feature aggregation to obtain a spatial feature matrix. The graph convolutional network aggregates the features of adjacent receivers based on the spatiotemporal matrix and the adjacency matrix containing the geographical location information of the receivers, thereby extracting the spatial features in the spatiotemporal matrix.

[0054] S224. The temporal feature matrix and spatial feature matrix are weighted and fused based on an attention mechanism to obtain a spatiotemporal feature matrix. After obtaining the temporal and spatial features, the spatiotemporal feature weights are calculated using an attention mechanism and then weighted and fused to obtain the fused spatiotemporal feature matrix.

[0055] S225. The spatiotemporal feature matrix is ​​input into a preset temporal convolutional network to predict the displacement of each monitoring point at multiple future time steps, resulting in multiple predicted displacements. The weighted and fused spatiotemporal feature matrix is ​​then input into the temporal convolutional network to output the displacements at multiple future time steps. It is understood that temporal convolutional networks can efficiently capture long-term dependencies, making them suitable for the real-time prediction of monitoring point displacements required by this invention, and improving the real-time performance of earthquake fault monitoring.

[0056] Example 3:

[0057] The only difference between this embodiment and embodiment 1 or 2 is that step S4 includes:

[0058] S41. A gridded fault surface is generated based on preset grid parameters and the fault geometry model. The slip amount of each grid point on the gridded fault surface at each time step is calculated based on each slip amount at each time step, resulting in multiple grid point slip amounts. It can be understood that first, grid parameters are defined, including strike length, dip length, and grid resolution. Then, the fault is divided into grids along its strike and dip, and the coordinates of each grid point are generated, thereby discretizing the fault surface into a regular grid, facilitating subsequent slip amount interpolation and locked segment identification.

[0059] Specifically, the step of calculating the slip amount of each grid point on the gridded fault surface at each time step based on each slip amount at each time step includes:

[0060] S411. Obtain the geographic coordinates of each monitoring point, and convert the geographic coordinates of each monitoring point into first coordinates in a local coordinate system to obtain multiple first coordinates;

[0061] S412. Based on the Kriging interpolation method and the first coordinate and the slip amount of each monitoring point at each time step, calculate the grid point slip amount of each grid point at each time step to obtain multiple grid point slip amounts.

[0062] Understandably, after obtaining the gridded fault surface and the local coordinates and slip of each monitoring point, the fault slip distribution matrix S is generated through kriging interpolation. map The calculation formula is:

[0063]

[0064] Among them, S map (m,n) represents the sliding amount of the grid point with coordinates (m,n), N is the number of monitoring points, and Δs i Let ω be the sliding amount at the i-th monitoring point. i Let be the Kriging weight corresponding to the i-th monitoring point.

[0065] S42. Based on the slip amount of each grid point at each time step, fault-locked segments are identified, resulting in multiple fault-locked segments at each time step. It is understood that a locked segment represents a region where slip has not occurred for a long time; stress gradually accumulates in these areas, potentially leading to severe slip. Therefore, by dividing the area into grids and calculating the grid point slip amount of each grid point, the locked segments can be accurately identified based on the grid point slip amount within each region. This allows for the prediction of the locked segment region and its activity, effectively monitoring the activity of earthquake faults and enabling early risk assessment and warning.

[0066] Specifically, step S42 includes:

[0067] S421. Based on a preset locking threshold and the slip amount of each grid point at each time step, lock points are selected at each time step to obtain multiple lock points. It is understood that the locking threshold set in this example is 20% of the long-term average slip amount of the seismic fault. Through binarization, the values ​​of points exceeding the locking threshold in the slip distribution matrix are set to 0, and the values ​​of points below the locking threshold are set to 1, thus obtaining a binary matrix used to mark the lock points in the grid.

[0068] S422. Based on a preset clustering algorithm, each closure point at each time step is clustered, and the neighboring closure points after clustering at each time step are merged to obtain multiple fault closure segments at each time step. It can be understood that this embodiment first sets a neighborhood radius to define the maximum distance between adjacent points, then sets the minimum number of grid points to form a closure segment, and then uses the DBSCAN clustering algorithm to merge the closure points in the binary matrix obtained in step S421 to obtain multiple closure segments. Each obtained closure segment includes the coordinate range of the grid points and the area of ​​the closure segment, where the area of ​​the closure segment is the product of the number of grid points and the resolution.

[0069] S43. Calculate the rupture probability of each fault-locked segment based on its stress state, obtaining the rupture probability of each fault-locked segment at each time step. It is understood that over time, plate movement and crustal deformation in the area surrounding the locked segment will continuously exert pressure on it, causing the stress in these areas to gradually increase. Once the stress accumulates to a critical point, the locked segment may rupture and release energy, causing an earthquake. Therefore, assessing the rupture probability of locked segments helps to understand the likelihood of future earthquakes in these areas, making the monitoring results of seismic faults more intuitive and accurate.

[0070] Specifically, step S43 includes:

[0071] S431. Obtain second information and third information. The second information includes the friction coefficient of the earthquake fault, the elastic modulus of the rock, Poisson's ratio and rock density. The third information includes the average recurrence period of historical earthquakes corresponding to the earthquake fault and the time of the most recent earthquake.

[0072] S432. Calculate the average slip of each fault-locked segment based on the grid point slip of each of the locking points of each fault-locked segment;

[0073] S433. Calculate the Coulomb fracture potential of each of the fault-locked segments based on the average slip value of each segment and the second information. It is understood that calculating the Coulomb fracture potential of a locked segment requires first calculating the shear stress and normal stress of the locked segment. The formula for calculating the shear stress is:

[0074]

[0075] Where, τ k Let E be the shear stress of the k-th locking segment, v be the elastic modulus, v be the Poisson's ratio, D be the locking depth, and Δs be the shear stress of the k-th locking segment. avg,k It represents the average slip of the k-th locking segment.

[0076] The formula for calculating normal stress is:

[0077] σ k =ρgh·sinφ;

[0078] Where, σ k Let ρ be the normal stress of the k locked segments, ρ be the rock density, g be the gravitational acceleration, h be the fault depth, and φ be the fault dip angle.

[0079] After calculating the shear stress and normal stress, the Coulomb fracture potential is calculated according to the Coulomb fracture criterion. The calculation formula is as follows:

[0080] CFF k =τ k -μσk ;

[0081] Among them, CFF k Let τ be the Coulomb fracture potential of the k-th locked segment. k Let σ be the shear stress of the k-th locked segment. k Let be the normal stress of the k locked segments, and μ be the friction coefficient.

[0082] S434. Based on the Brownian process time model, third-party information, and the Coulomb rupture potential of each fault-locked segment, the rupture probability of each segment is calculated, resulting in multiple rupture probabilities. It is understood that historical earthquake data needs to be considered when calculating the rupture probability of a locked segment. The calculation formula is as follows:

[0083]

[0084] Among them, P k Let T be the rupture probability of the k-th locked segment, T be the average recurrence period of historical earthquakes, t be the time interval between the current time and the last earthquake, and CFF be the probability of rupture. k For the Coulomb fracture potential of the k-th locked segment, CFF crit Critical Coulomb stress.

[0085] S44. Based on each of the fault-locked segments and each of the rupture probabilities at each time step, perform a risk warning for the earthquake fault and obtain the risk warning result.

[0086] Specifically, step S44 includes:

[0087] S441. Obtain fourth information, the fourth information including the area of ​​each of the fault-locked segments at each time step;

[0088] S442. Calculate the spatial overlap area of ​​each fault-locked segment in a continuous time step based on the fourth information to obtain multiple spatial overlap areas;

[0089] S443. Generate first risk warning information based on the overlapping area of ​​each space, thus obtaining the first risk warning information. It is understood that if the overlapping area of ​​a certain locking segment reaches 80% in multiple time steps, it indicates that the area corresponding to that locking segment has been in a state of low sliding volume for a long time, which will result in a large amount of stress accumulation and pose a significant risk. Therefore, this invention determines the duration of the locking segment by the overlapping area of ​​the locking segment and provides a risk warning for the locking segment based on the duration.

[0090] Furthermore, each blocking segment may present three scenarios: persistent blocking, intermittent blocking, and newly formed blocking. Persistent blocking refers to a region being identified as a blocking segment throughout all predicted time steps, posing a high risk. Intermittent blocking refers to a region meeting the blocking conditions only in certain time steps, requiring continuous monitoring and supplementary judgment. Newly formed blocking refers to a sudden shift to a blocking segment in the later stages of the predicted time step, requiring an extension of the predicted time step or comprehensive assessment of other information related to the blocking segment.

[0091] S444. Generate second risk warning information based on the rupture probability of each fault-locked segment at each time step, thus obtaining the second risk warning information. It is understood that the rupture probability directly reflects the risk level of the locked segment; therefore, this embodiment classifies warning levels and corresponding thresholds. Warning levels include yellow, orange, and red warnings. If the rupture probability exceeds 0.6, it indicates that the locked segment is of medium risk, issuing a yellow warning and continuously monitoring the locked segment; if the rupture probability exceeds 0.8, it indicates that the locked segment is of high risk, issuing an orange warning and verifying it in conjunction with other relevant data; if the rupture probability exceeds 0.9, it indicates that the locked segment is of extremely high risk, issuing a red warning and immediately responding with emergency measures.

[0092] S445. Generate third risk warning information based on the change in the rupture probability of each fault-locked segment at each time step, thus obtaining the third risk warning information. It is understood that the change trend of the rupture probability of the locked segment is analyzed by examining the change in the rupture probability of each fault-locked segment at each time step. If the trend is upward, more detailed investigation and analysis of the area is required. If the trend is downward, monitoring of the area can be simplified, and more focus can be allocated to high-risk areas.

[0093] Example 4:

[0094] like Figure 2 As shown, this embodiment provides a BeiDou-based earthquake fault monitoring system, which includes an acquisition module 901, a first processing module 902, a second processing module 903, and a third processing module 904.

[0095] The acquisition module 901 is used to acquire BeiDou satellite data from multiple monitoring points of the earthquake fault within a preset sampling time.

[0096] The first processing module 902 is used to preprocess each Beidou satellite data, and input the preprocessed Beidou satellite data into a preset prediction model to predict the displacement, thereby obtaining multiple predicted displacements. Each predicted displacement includes the displacement of a monitoring point in multiple future time steps.

[0097] The second processing module 903 is used to calculate the sliding amount of each monitoring point in each future time step based on each predicted displacement amount at each time step, and obtain multiple sliding amounts;

[0098] The third processing module 904 is used to perform risk warning of earthquake faults based on the change of slip at each time step in the future for each of the monitoring points, and obtain risk warning results.

[0099] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0100] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0101] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A seismic fault monitoring method based on BeiDou, characterized in that, include: Acquire BeiDou satellite data from multiple monitoring points along the earthquake fault within a preset sampling time. Each BeiDou satellite data is preprocessed, and the preprocessed BeiDou satellite data is input into a preset prediction model to predict the displacement, resulting in multiple predicted displacements. Each predicted displacement includes the displacement of a monitoring point over multiple future time steps. Based on each predicted displacement at each time step, calculate the slip amount of each monitoring point at each future time step to obtain multiple slip amounts; Based on the change in the slip at each of the monitoring points in each future time step, a risk warning of earthquake faults is generated, and a risk warning result is obtained. The step of calculating the slip of each monitoring point in each future time step based on each predicted displacement at each time step includes: Obtain first information, which includes the strike angle, dip angle, and locking depth of the earthquake fault; A local coordinate system is constructed with a preset reference point as the origin, and the local coordinate system is obtained. Based on the first information and the local coordinate system, a fault geometric model is established to obtain the fault geometric model; The slip amount is calculated based on the fault geometry model and the displacement of each monitoring point at each future time step, thus obtaining the slip amount of each monitoring point at each future time step.

2. The seismic fault monitoring method based on BeiDou as described in claim 1, characterized in that... The preprocessing of each BeiDou satellite data includes: Cycle slip detection and repair are performed on the carrier phase data of each BeiDou satellite data, and error correction is performed on the BeiDou satellite data after carrier phase repair to obtain multiple BeiDou satellite data after error correction; Position calculation is performed on each BeiDou satellite data after error correction, and a land deformation time series for each monitoring point is generated based on the position calculation results, resulting in multiple land deformation time series. Each land deformation time series includes the position, velocity, and acceleration of a monitoring point at each time step of the sampling time. A spatiotemporal matrix is ​​generated based on multiple time series of surface deformation, and the spatiotemporal matrix is ​​obtained.

3. The seismic fault monitoring method based on BeiDou according to claim 2, characterized in that... The step of inputting each preprocessed BeiDou satellite data into a preset prediction model for displacement prediction includes: The spatiotemporal matrix is ​​used to extract local features based on a pre-defined one-dimensional convolutional neural network to obtain the first output feature; Based on a pre-defined bidirectional long short-term memory network, the long-range time dependence of the first output feature is extracted to obtain a time feature matrix; A spatial adjacency matrix is ​​generated based on the location of the monitoring points, and the spatial adjacency matrix and the spatiotemporal matrix are input into a preset graph convolutional network for feature aggregation to obtain a spatial feature matrix. The temporal and spatial feature matrices are weighted and fused based on an attention mechanism to obtain a spatiotemporal feature matrix. The spatiotemporal feature matrix is ​​input into a preset temporal convolutional network to predict the displacement of each monitoring point in the future multiple time steps, thereby obtaining multiple predicted displacements.

4. The seismic fault monitoring method based on BeiDou as described in claim 1, characterized in that... Based on the change in the slip at each of the monitoring points at each future time step, a risk warning for earthquake faults is provided, including: A gridded fault surface is generated based on preset grid parameters and the fault geometry model. The slip amount of each grid point on the gridded fault surface at each time step is calculated based on each slip amount at each time step, resulting in multiple grid point slip amounts. Based on the slip amount of each grid point at each time step, the fault-locked segment is identified, resulting in multiple fault-locked segments at each time step; The rupture probability of each fault-locked segment is calculated based on the stress state of each fault-locked segment, thus obtaining the rupture probability of each fault-locked segment at each time step. Risk warning of earthquake faults is performed based on each fault-locked segment and each rupture probability at each time step, and the risk warning result is obtained.

5. The seismic fault monitoring method based on BeiDou according to claim 4, characterized in that... The calculation of the slip amount of each grid point on the gridded fault surface at each time step based on the slip amount at each time step includes: Obtain the geographic coordinates of each monitoring point, and convert the geographic coordinates of each monitoring point into first coordinates in a local coordinate system to obtain multiple first coordinates; Based on the Kriging interpolation method and the first coordinate and the slip amount of each monitoring point at each time step, the grid point slip amount of each grid point at each time step is calculated to obtain multiple grid point slip amounts.

6. The seismic fault monitoring method based on BeiDou according to claim 5, characterized in that... The identification of fault-locked segments based on the slip volume of each monitoring point at each future time step includes: Based on the preset locking threshold and the sliding amount of each grid point at each time step, the locking point at each time step is selected to obtain multiple locking points; Based on a preset clustering algorithm, each of the closure points at each time step is clustered, and the neighboring closure points after clustering at each time step are merged to obtain multiple fault closure segments at each time step.

7. A seismic fault monitoring method based on BeiDou as described in claim 6, characterized in that... The calculation of the rupture probability of each fault-locked segment based on the stress state of each fault-locked segment includes: Acquire second and third information, the second information including the friction coefficient of the earthquake fault, the elastic modulus of the rock, Poisson's ratio and rock density, and the third information including the average recurrence period of historical earthquakes corresponding to the earthquake fault and the time of the most recent earthquake; The average slip of each fault-locked segment is calculated based on the grid point slip of each of the locking points of each fault-locked segment; The Coulomb fracture potential of each fault-locked segment is calculated based on the average slip of each fault-locked segment and the second information. The rupture probability of each fault-locked segment is calculated based on the Brownian process time model, third information, and each Coulomb rupture potential, resulting in multiple rupture probabilities.

8. A seismic fault monitoring method based on BeiDou as described in claim 5, characterized in that... The risk warning for earthquake faults based on each fault-locked segment and each rupture probability at each time step includes: Obtain fourth information, which includes the area of ​​each of the fault-locked segments at each time step; Based on the fourth information, the spatial overlap area of ​​each fault-locked segment within a continuous time step is calculated to obtain multiple spatial overlap areas. First risk warning information is generated based on the overlapping area of ​​each space, and the first risk warning information is obtained. Based on the rupture probability of each fault-locked segment at each time step, a second risk warning information is generated, and the second risk warning information is obtained. A third risk warning is generated based on the change in the rupture probability of each fault-locked segment at each time step, and the third risk warning is obtained.

9. A seismic fault monitoring system based on BeiDou, characterized in that, include: The acquisition module is used to acquire BeiDou satellite data from multiple monitoring points of the earthquake fault within a preset sampling time. The first processing module is used to preprocess each Beidou satellite data and input the preprocessed Beidou satellite data into a preset prediction model to predict the displacement, thereby obtaining multiple predicted displacements. Each predicted displacement includes the displacement of a monitoring point over multiple future time steps. The second processing module is used to calculate the sliding amount of each monitoring point in each future time step based on each predicted displacement at each time step, and obtain multiple sliding amounts. The third processing module is used to perform risk warning of earthquake faults based on the change of slip at each time step of each monitoring point in the future, and to obtain risk warning results. The second processing module includes: Obtain first information, which includes the strike angle, dip angle, and locking depth of the earthquake fault; A local coordinate system is constructed with a preset reference point as the origin, and the local coordinate system is obtained. Based on the first information and the local coordinate system, a fault geometric model is established to obtain the fault geometric model; The slip amount is calculated based on the fault geometry model and the displacement of each monitoring point at each future time step, thus obtaining the slip amount of each monitoring point at each future time step.

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