Ground surface deformation risk assessment system and method based on InSAR and multi-source data

Abstract

The invention discloses an earth surface deformation risk assessment system and method based on InSAR and multi-source data, and the system comprises an SBAS-InSAR module which is used for extracting earth surface deformation information from a synthetic aperture radar SAR image obtained by a satellite, and generating a deformation rate diagram and an accumulative settlement diagram of a time sequence; the transient electromagnetic method module is used for detecting an underground structure, in particular for identifying a goaf and analyzing the internal condition of the goaf; the data analysis module is used for integrating data from the SBAS-InSAR module and the transient electromagnetic method module, and analyzing and predicting the spatial change rule of the settlement body by adopting a standard deviation ellipse and a long short-term memory (LSTM) network model; and the user interface is used for providing a visual tool, so that a user can intuitively view the monitoring result and the risk assessment report. The invention aims to realize high-precision earth surface deformation monitoring and underground structure detection in geological complex areas such as mining areas and the like by comprehensively applying an SBAS-InSAR technology and a transient electromagnetic method (TEM), so as to carry out comprehensive risk assessment.

Description

Technical Field

[0001] The present invention relates to the field of surface observation technology, and in particular to a surface deformation risk assessment system and method based on InSAR and multi-source data. Background Art

[0002] Surface deformation risk assessment plays a vital role in disaster prevention and mitigation work in geologically complex areas such as mining areas. Traditional surface deformation monitoring technologies, such as GPS and leveling, mainly rely on data collection at discrete points, which makes it difficult to fully reflect the sliding and subsidence laws of the entire mining area, resulting in limitations in monitoring large-area surface deformation. In addition, these traditional methods can usually only provide limited time series data, cannot capture the trend of changes over a long time scale, and have weak information acquisition capabilities for underground structures, making it difficult to deeply explore the intrinsic causes of surface deformation. For example, underground factors such as the existence of goafs and their water-richness are often the main causes of surface subsidence, but traditional monitoring methods have limited detection capabilities for such deep geological information.

[0003] In recent years, synthetic aperture radar interferometry (InSAR) technology, especially small baseline set interferometry (SBAS-InSAR), has gradually become an effective means of monitoring surface deformation in mining areas because it can obtain high-precision surface deformation information by differential interferometry processing of multi-period satellite image data. However, although SBAS-InSAR technology can provide large-scale, high-resolution surface deformation maps, it is still insufficient for the detection of underground structures and it is difficult to reveal the deep-seated causes of surface deformation. On the other hand, transient electromagnetic method (TEM), as a time-domain electromagnetic detection technology, can effectively identify underground goafs and their internal conditions, providing key data for analyzing the causes of subsidence. However, when transient electromagnetic method is used alone, it lacks the combination with surface deformation monitoring data, and cannot achieve coordinated monitoring of the surface and underground, which limits its application effect in surface deformation risk assessment.

[0004] The defect of the existing technology is that a single technical means is difficult to meet the needs of high-precision surface deformation monitoring and deep underground structure detection at the same time, resulting in incomplete and inaccurate surface deformation risk assessment. Specifically, it is manifested as follows: 1) Traditional monitoring technology can only provide data from discrete points, which is difficult to reflect the overall deformation law; 2) Although SBAS-InSAR technology can monitor surface deformation, it does not obtain enough information about underground structures; 3) Although transient electromagnetic method can detect underground structures, it lacks fusion with surface deformation data and cannot achieve comprehensive assessment. Therefore, there is an urgent need for a surface deformation risk assessment system that can integrate the advantages of multiple technologies and realize coordinated monitoring of the surface and underground, so as to improve the comprehensiveness and accuracy of monitoring and provide a scientific basis for disaster prevention and mitigation. Summary of the invention

[0005] 1. Technical Problems Solved

[0006] The technical problems to be solved by the present invention are the various problems mentioned in the above background technology, and a surface deformation risk assessment system and method based on InSAR and multi-source data are provided.

[0007] 2. Technical Solution

[0008] In order to solve the above technical problems, the present invention provides a technical solution: a surface deformation risk assessment system based on InSAR and multi-source data, comprising:

[0009] SBAS-InSAR module: used to extract surface deformation information from synthetic aperture radar SAR images acquired by satellites and generate time series deformation rate maps and cumulative settlement maps;

[0010] Transient electromagnetic method module: used for the detection of underground structures, especially for the identification of goafs and the analysis of their internal conditions;

[0011] Data analysis module: used to integrate the data from the SBAS-InSAR module and the transient electromagnetic method module, and use the standard deviation ellipse and long short-term memory network LSTM model to analyze and predict the spatial variation law of the sedimentation body;

[0012] User interface: Provides visualization tools so that users can intuitively view monitoring results and risk assessment reports.

[0013] As an improvement, the SBAS-InSAR module further comprises:

[0014] Connection map generation unit: used to generate a time baseline connection map and a space baseline connection map to reflect the pairing status of the data;

[0015] Differential interferometry unit: used to perform differential interferometry processing, remove flat ground effects, and provide guidance for connection diagram editing;

[0016] Connection diagram editing unit: used to select the best interference pairs and eliminate the interference pairs with poor effects;

[0017] GCP control point production unit: used to produce ground control point GCP files to ensure the accuracy of the inversion process;

[0018] Deformation series graph generation unit: used to form the final deformation series graph, including metadata and time series.

[0019] As an improvement, the transient electromagnetic method module further comprises:

[0020] Filtering unit: used to filter the collected data and eliminate noise;

[0021] Time-depth conversion unit: used to transform the observed data into the change of resistivity with depth;

[0022] Terrain geometry correction unit: used to eliminate the influence of terrain and convert the depth value of each measuring point data into actual elevation;

[0023] Section drawing unit: used to draw section views of underground structures.

[0024] As an improvement, an assessment method for a surface deformation risk assessment system based on InSAR and multi-source data includes the following steps:

[0025] S1. Data collection steps: Collect Sentinel-1A SAR image data and transient electromagnetic detection data in the study area;

[0026] S2. Comprehensive analysis step: Combine the surface deformation information obtained by SBAS-InSAR with the underground detection results of transient electromagnetic method to analyze the cause of subsidence; use standard deviation ellipse and LSTM model to predict the trend of future surface deformation;

[0027] S3. Risk assessment steps: Based on the analysis results, assess the risk level of surface deformation; provide disaster prevention and mitigation recommendations to assist in decision making.

[0028] As an improvement, the SBAS-InSAR processing step further includes:

[0029] A1. Connection map generation sub-step: by setting parameters, data with high coherence are formed into data pairs, and the main image and the auxiliary image are selected for pairing;

[0030] A2, differential interferometry sub-step: select SRTM1 data with a resolution of 30 meters as the elevation reference to keep the results highly consistent with the satellite system;

[0031] A3, connection diagram editing sub-step: according to the selection principle of coherence diagram and disentanglement diagram, the interference pairs with poor effect are eliminated;

[0032] A4. GCP control point preparation sub-step: Using the method combining PS-InSAR point selection and interferogram, select PS points with a coherence coefficient greater than 0.75 and a large amplitude deviation index as GCP preliminary points, and eliminate points with deformable fringes to obtain the final GCP control point file.

[0033] As an improvement, the transient electromagnetic method processing step also includes:

[0034] B1, filtering sub-step: before data processing, the collected data are first filtered to eliminate human noise;

[0035] B2, time-depth conversion sub-step: converting the data of the secondary field potential variation with time observed by the transient electromagnetic instrument in the field into the variation of resistivity with depth;

[0036] B3, terrain geometry time correction sub-step: the time-depth converted data is matched with the point line number of the measured data one by one, and the depth value of each measuring point data is converted into the actual elevation, thereby eliminating certain terrain effects;

[0037] B4. Cross-section drawing sub-step: Draw the cross-section of the underground structure based on the processed data.

[0038] As an improvement, the comprehensive analysis step specifically includes:

[0039] C1, data fusion sub-step: the surface deformation information obtained by SBAS-InSAR is integrated with the underground detection results of transient electromagnetic method to construct a three-dimensional geological model;

[0040] C2. Cause analysis sub-step: Based on the three-dimensional geological model, analyze the internal causes of surface subsidence, such as the existence of goaf and its water-richness;

[0041] C3, trend prediction sub-step: Use the LSTM model to predict the trend of future surface deformation and provide early warning information.

[0042] As an improvement, the risk assessment step specifically includes:

[0043] D1. Risk classification sub-step: Classify the risk into three levels: low, medium and high according to the magnitude and rate of surface deformation and the stability of underground structures;

[0044] D2. Provide suggestions Sub-step: Provide corresponding disaster prevention and mitigation measures and suggestions according to different risk levels, such as strengthening monitoring, implementing reinforcement projects or evacuating personnel.

[0045] 3. Beneficial Effects

[0046] The advantages of the present invention compared with the prior art are:

[0047] 1. The surface deformation risk assessment system and method based on InSAR and multi-source data of the present invention realizes high-precision monitoring of surface deformation and fine detection of underground structures by integrating SBAS-InSAR module and transient electromagnetic method module. The SBAS-InSAR module can extract detailed surface deformation information from synthetic aperture radar (SAR) images acquired by satellites, generate time series deformation rate diagrams and cumulative settlement diagrams, while the transient electromagnetic method module focuses on identifying underground goafs and their internal conditions, providing key data for analyzing the causes of settlement. Through the data analysis module, the system can integrate data from the two main modules, and use advanced standard deviation ellipse and long short-term memory network (LSTM) models to conduct in-depth analysis and prediction of the spatial variation law of the settlement body, thereby realizing accurate prediction of the surface deformation trend. In addition, the user interface provides intuitive visualization tools, allowing users to easily view monitoring results and risk assessment reports, greatly improving decision-making efficiency.

[0048] 2. The surface deformation risk assessment system and method based on InSAR and multi-source data of the present invention not only covers the whole process from data acquisition, processing to comprehensive analysis and risk assessment, but also pays special attention to the optimization of details in each step, such as connection map generation, differential interference, filtering, time-depth conversion, terrain geometry correction, etc., to ensure the accuracy and reliability of data processing. In the risk assessment stage, through comprehensive consideration of the amplitude and rate of surface deformation and the stability of underground structures, the risks are divided into three levels: low, medium and high, and corresponding disaster prevention and mitigation measures are proposed according to different risk levels, which effectively assists disaster prevention and emergency response. Therefore, the system and its method greatly improve the scientificity and practicality of surface deformation monitoring and risk assessment, and have significant beneficial effects on ensuring public safety and reducing losses caused by natural disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a structural diagram of a surface deformation risk assessment system based on InSAR and multi-source data of the present invention.

[0050] Figure 2 It is a flow chart of the steps of a surface deformation risk assessment system based on InSAR and multi-source data of the present invention. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0052] Embodiment 1

[0053] like Figure 1 As shown, a surface deformation risk assessment system based on InSAR and multi-source data includes:

[0054] SBAS-InSAR module: used to extract surface deformation information from synthetic aperture radar SAR images acquired by satellites, and generate time series deformation rate maps and cumulative settlement maps. The SBAS-InSAR module further includes:

[0055] Connection map generation unit: used to generate a time baseline connection map and a space baseline connection map to reflect the pairing status of the data;

[0056] Differential interferometry unit: used to perform differential interferometry processing, remove flat ground effects, and provide guidance for connection diagram editing;

[0057] Connection diagram editing unit: used to select the best interference pairs and eliminate the interference pairs with poor effects;

[0058] GCP control point production unit: used to produce ground control point GCP files to ensure the accuracy of the inversion process;

[0059] Deformation series graph generation unit: used to form the final deformation series graph, including metadata and time series;

[0060] Transient electromagnetic method module: used for the detection of underground structures, especially for the identification of goafs and the analysis of their internal conditions. The transient electromagnetic method module further includes:

[0061] Filtering unit: used to filter the collected data and eliminate noise;

[0062] Time-depth conversion unit: used to transform the observed data into the change of resistivity with depth;

[0063] Terrain geometry correction unit: used to eliminate the influence of terrain and convert the depth value of each measuring point data into actual elevation;

[0064] Section drawing unit: used to draw the section of underground structure;

[0065] Data analysis module: used to integrate the data from the SBAS-InSAR module and the transient electromagnetic method module, and use the standard deviation ellipse and long short-term memory network LSTM model to analyze and predict the spatial variation law of the sedimentation body;

[0066] User interface: Provides visualization tools so that users can intuitively view monitoring results and risk assessment reports.

[0067] like Figure 1As shown, an evaluation method for a surface deformation risk evaluation system based on InSAR and multi-source data includes the following steps:

[0068] S1. Data collection steps: Collect Sentinel-1A SAR image data and transient electromagnetic detection data in the study area;

[0069] The SBAS-InSAR processing steps also include:

[0070] A1. Connection map generation sub-step: by setting parameters, data with high coherence are formed into data pairs, and the main image and the auxiliary image are selected for pairing;

[0071] A2, differential interferometry sub-step: select SRTM1 data with a resolution of 30 meters as the elevation reference to keep the results highly consistent with the satellite system;

[0072] A3, connection diagram editing sub-step: according to the selection principle of coherence diagram and disentanglement diagram, the interference pairs with poor effect are eliminated;

[0073] A4, GCP control point preparation sub-step: using the method of combining PS-InSAR point selection with interferogram, select PS points with a coherence coefficient greater than 0.75 and a large amplitude deviation index as GCP preliminary points, and remove points with deformed fringes to obtain the final GCP control point file;

[0074] The transient electromagnetic method processing step also includes:

[0075] B1, filtering sub-step: before data processing, the collected data are first filtered to eliminate human noise;

[0076] B2, time-depth conversion sub-step: converting the data of the secondary field potential variation with time observed by the transient electromagnetic instrument in the field into the variation of resistivity with depth;

[0077] B3, terrain geometry time correction sub-step: the time-depth converted data is matched with the point line number of the measured data one by one, and the depth value of each measuring point data is converted into the actual elevation, thereby eliminating certain terrain effects;

[0078] B4, cross-section drawing sub-step: draw a cross-section of the underground structure based on the processed data;

[0079] S2. Comprehensive analysis step: combining the surface deformation information obtained by SBAS-InSAR with the underground detection results of transient electromagnetic method to analyze the cause of subsidence; using standard deviation ellipse and LSTM model to predict the trend of future surface deformation; the comprehensive analysis step specifically includes:

[0080] C1, data fusion sub-step: the surface deformation information obtained by SBAS-InSAR is integrated with the underground detection results of transient electromagnetic method to construct a three-dimensional geological model;

[0081] C2. Cause analysis sub-step: Based on the three-dimensional geological model, analyze the internal causes of surface subsidence, such as the existence of goaf and its water-richness;

[0082] C3, trend prediction sub-step: Use the LSTM model to predict the trend of future surface deformation and provide early warning information;

[0083] S3. Risk assessment step: Based on the analysis results, assess the risk level of surface deformation; provide disaster prevention and mitigation suggestions to assist decision-making; the risk assessment step specifically includes:

[0084] D1. Risk classification sub-step: Classify the risk into three levels: low, medium and high according to the magnitude and rate of surface deformation and the stability of underground structures;

[0085] D2. Provide suggestions Sub-step: Provide corresponding disaster prevention and mitigation measures and suggestions according to different risk levels, such as strengthening monitoring, implementing reinforcement projects or evacuating personnel.

[0086] The working principle of the present invention is as follows:

[0087] 1. System composition and functional modules

[0088] The system mainly consists of four core modules: SBAS-InSAR module, transient electromagnetic method module, data analysis module and user interface.

[0089] SBAS-InSAR module: responsible for extracting surface deformation information from synthetic aperture radar (SAR) images acquired by satellites and generating time series deformation rate maps and cumulative settlement maps. This module further includes a connection map generation unit, a differential interferometry unit, a connection map editing unit, a GCP control point production unit, and a deformation series map generation unit.

[0090] Transient electromagnetic method module: used for underground structure detection, especially for the identification of goaf and analysis of its internal conditions. This module includes filtering unit, time-depth conversion unit, terrain geometry correction unit and cross-section drawing unit.

[0091] Data analysis module: used to integrate data from the SBAS-InSAR module and the transient electromagnetic method module, and use standard deviation ellipse and long short-term memory network (LSTM) model to analyze and predict the spatial variation law of the sedimentation body.

[0092] User interface: Provides visualization tools that allow users to intuitively view monitoring results and risk assessment reports to assist in decision making.

[0093] 2. Data Collection

[0094] First, Sentinel-1A SAR image data and transient electromagnetic detection data were collected in the study area. The SAR image data has a temporal resolution of 12 days, an ascending orbit, an orbit number of 55, a C-band, a ground resolution of 5m×20m, and a single-look complex (SLC) image with a polarization mode of VV. A total of 21 images were collected. In addition, 30-meter resolution SRTM1 data was used as an elevation reference to eliminate the influence of terrain phase and satellite orbit errors. Transient electromagnetic data are transmitted by a large fixed source internal loop device, and the V8 dedicated probe receives the signal to ensure high data quality.

[0095] 3.SBAS-InSAR Processing

[0096] Connection map generation: By setting parameters, data with high coherence are formed into data pairs, and the main image and auxiliary image are selected for pairing to generate the time baseline connection map and the space baseline connection map to reflect the pairing status of the data.

[0097] Differential interferometry: SRTM1 data with a resolution of 30 meters is selected as the elevation reference, and differential interferometry processing is performed to remove the flat ground effect and keep the results highly consistent with the satellite system.

[0098] Connection map editing: According to the selection principles of coherence map and disentanglement map, interference pairs with poor effects are eliminated to ensure the accuracy of subsequent inversion.

[0099] Production of GCP control points: Using the method combining PS-InSAR point selection and interferogram, PS points with a coherence coefficient greater than 0.75 and a large amplitude deviation index are selected as preliminary GCP points, and points with deformable fringes are eliminated to obtain the final GCP control point file.

[0100] Deformation series map generation: After orbital refinement, re-flattening, two inversions, geocoding and other steps, the final deformation series map is formed, including metadata and time series.

[0101] 4. Transient electromagnetic method processing

[0102] Filtering: Before data processing, the collected data is first filtered to eliminate human noise and ensure the purity of the data.

[0103] Time-to-depth conversion: The data of the secondary field potential variation with time observed by the transient electromagnetic instrument in the field is converted into the variation of resistivity with depth in order to better understand the underground structure.

[0104] Terrain geometry correction: The time-depth converted data is matched one by one with the point and line numbers of the measured data, and the depth value of each measuring point data is converted into the actual elevation to eliminate the influence of terrain.

[0105] Cross-section diagram drawing: Based on the processed data, draw the cross-section diagram of the underground structure to intuitively display the underground geological structure.

[0106] 5. Comprehensive analysis

[0107] Data fusion: The surface deformation information obtained by SBAS-InSAR is integrated with the underground detection results of transient electromagnetic method to construct a three-dimensional geological model to fully display the relationship between the surface and underground structures.

[0108] Cause analysis: Based on the three-dimensional geological model, the internal causes of surface subsidence are analyzed, such as the existence of goaf and its water-richness, to reveal the root causes of surface deformation.

[0109] Trend prediction: Use the LSTM model to predict the trend of future surface deformation, provide early warning information, and help relevant departments take measures in advance.

[0110] 6. Risk Assessment

[0111] Risk classification: Based on the magnitude and rate of surface deformation and the stability of underground structures, the risk is divided into three levels: low, medium and high. Low risk means that the surface deformation is small and stable, medium risk means that there is some deformation but it is controllable, and high risk means that the deformation is serious and there is potential danger.

[0112] Recommendations Provide suggestions: According to different risk levels, provide corresponding disaster prevention and mitigation measures and suggestions, such as strengthening monitoring, implementing reinforcement projects or evacuating personnel, to ensure public safety.

[0113] 7. User Interface

[0114] The user interface provides intuitive visualization tools, allowing users to view monitoring results and risk assessment reports in the form of maps, charts, etc. The interface also supports interactive operations, allowing users to adjust display content and export reports as needed, making it easier for decision makers to quickly understand the situation and take action.

[0115] Through the above-mentioned specific implementation methods, the system realizes high-precision monitoring of surface deformation and fine detection of underground structures. It combines the advantages of SBAS-InSAR and transient electromagnetic method, provides a comprehensive surface deformation risk assessment solution, significantly improves the scientificity and practicality of monitoring, and provides strong support for disaster prevention and mitigation work.

[0116] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0117] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0118] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A surface deformation risk assessment system based on InSAR and multi-source data, characterized in that: include: SBAS-InSAR module: used to extract surface deformation information from synthetic aperture radar SAR images acquired by satellites and generate time series deformation rate maps and cumulative settlement maps; Transient electromagnetic method module: used for the detection of underground structures, especially for the identification of goafs and the analysis of their internal conditions; Data analysis module: used to integrate the data from the SBAS-InSAR module and the transient electromagnetic method module, and use the standard deviation ellipse and long short-term memory network LSTM model to analyze and predict the spatial variation law of the sedimentation body; User interface: Provides visualization tools so that users can intuitively view monitoring results and risk assessment reports.

2. The surface deformation risk assessment system based on InSAR and multi-source data according to claim 1 is characterized in that: The SBAS-InSAR module further comprises: Connection map generation unit: used to generate a time baseline connection map and a space baseline connection map to reflect the pairing status of the data; Differential interferometry unit: used to perform differential interferometry processing, remove flat ground effects, and provide guidance for connection diagram editing; Connection diagram editing unit: used to select the best interference pairs and eliminate the interference pairs with poor effects; GCP control point production unit: used to produce ground control point GCP files to ensure the accuracy of the inversion process; Deformation series graph generation unit: used to form the final deformation series graph, including metadata and time series.

3. The surface deformation risk assessment system based on InSAR and multi-source data according to claim 1 is characterized in that: The transient electromagnetic method module further comprises: Filtering unit: used to filter the collected data and eliminate noise; Time-depth conversion unit: used to transform the observed data into the change of resistivity with depth; Terrain geometry correction unit: used to eliminate the influence of terrain and convert the depth value of each measuring point data into actual elevation; Section drawing unit: used to draw section views of underground structures.

4. The method for evaluating a surface deformation risk evaluation system based on InSAR and multi-source data according to claim 1, characterized in that: The following steps are involved: S1. Data collection steps: Collect Sentinel-1A SAR image data and transient electromagnetic detection data in the study area; S2. Comprehensive analysis step: Combine the surface deformation information obtained by SBAS-InSAR with the underground detection results of transient electromagnetic method to analyze the cause of subsidence; use standard deviation ellipse and LSTM model to predict the trend of future surface deformation; S3, risk assessment step: assess the risk level of surface deformation based on the analysis results; Provide disaster prevention and mitigation advice and assist in decision-making.

5. The method for evaluating a surface deformation risk evaluation system based on InSAR and multi-source data according to claim 4, characterized in that: The SBAS-InSAR processing steps also include: A1. Connection map generation sub-step: by setting parameters, data with high coherence are formed into data pairs, and the main image and the auxiliary image are selected for pairing; A2, differential interferometry sub-step: select 30-meter resolution SRTM1 data as elevation reference to keep the results highly consistent with the satellite system; A3, connection diagram editing sub-step: according to the selection principle of coherence diagram and disentanglement diagram, the interference pairs with poor effect are eliminated; A4. GCP control point preparation sub-step: Using the method combining PS-InSAR point selection and interferogram, select PS points with a coherence coefficient greater than 0.75 and a large amplitude deviation index as GCP preliminary points, and eliminate points with deformable fringes to obtain the final GCP control point file.

6. The method for evaluating a surface deformation risk evaluation system based on InSAR and multi-source data according to claim 4, characterized in that: The transient electromagnetic method processing step also includes: B1, filtering sub-step: before data processing, the collected data are first filtered to eliminate human noise; B2, time-depth conversion sub-step: converting the data of the secondary field potential variation with time observed by the transient electromagnetic instrument in the field into the variation of resistivity with depth; B3, terrain geometry time correction sub-step: the time-depth converted data is matched with the point line number of the measured data one by one, and the depth value of each measuring point data is converted into the actual elevation, thereby eliminating certain terrain effects; B4. Cross-section drawing sub-step: Draw the cross-section of the underground structure based on the processed data.

7. The method for evaluating a surface deformation risk evaluation system based on InSAR and multi-source data according to claim 4, characterized in that: The comprehensive analysis step specifically includes: C1, data fusion sub-step: the surface deformation information obtained by SBAS-InSAR is integrated with the underground detection results of transient electromagnetic method to construct a three-dimensional geological model; C2. Cause analysis sub-step: Based on the three-dimensional geological model, analyze the internal causes of surface subsidence, such as the existence of goaf and its water-richness; C3, trend prediction sub-step: Use the LSTM model to predict the trend of future surface deformation and provide early warning information.

8. The method for evaluating a surface deformation risk evaluation system based on InSAR and multi-source data according to claim 4, characterized in that: The risk assessment steps specifically include: D1. Risk classification sub-step: Classify the risk into three levels: low, medium and high according to the magnitude and rate of surface deformation and the stability of underground structures; D2. Provide suggestions Sub-step: Provide corresponding disaster prevention and mitigation measures and suggestions according to different risk levels, such as strengthening monitoring, implementing reinforcement projects or evacuating personnel.

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