An open-pit mining subsidence whole-basin monitoring method based on space-air-ground cooperation observation

Abstract

The application discloses a kind of exploitation subsidence whole basin monitoring methods of space-time cooperation observation, belong to mining exploitation subsidence monitoring technical field.This method first collects target area time series SAR image, airborne LiDAR-DEM data and ground precision leveling measured data, obtains corresponding air-space-ground multi-source subsidence monitoring result after processing;Second, based on InSAR maximum deformation gradient theory, small-middle magnitude subsidence area boundary is divided, and ground measured data accuracy evaluation is combined to determine middle-large magnitude subsidence area boundary;Finally, according to the boundary, the data is fused regionally, the InSAR result is retained in small magnitude subsidence area, the LiDAR result is retained in large magnitude subsidence area, and the data of both is fused in middle magnitude subsidence area using priori fixed weight, and complete mining subsidence basin is obtained by spatial interpolation.The application effectively overcomes the shortcomings of single monitoring technology, can obtain continuous and accurate subsidence data, and provides scientific basis for ecological environment restoration and geological disaster prevention in mining area.

Description

Technical Field

[0001] This invention relates to the field of mining subsidence monitoring technology, and in particular to a method for monitoring mining subsidence across the entire basin using a combined air-space-ground observation approach. Background Technology

[0002] Large-scale coal mining activities have damaged buildings, roads, transportation facilities, and farmland above the mining subsidence areas, severely damaging the ecological environment of the mining areas. Therefore, in order to detect and understand the surface damage in the mining areas in a timely manner and grasp the patterns of ground subsidence, it is necessary to continuously monitor mining subsidence in the mining areas so as to provide relevant scientific basis for the ecological environment restoration and management of the mining areas, the rational exploitation of resources, and the prevention and control of geological disasters.

[0003] Traditional methods for monitoring mining subsidence mainly involve deploying ground-based observation stations and using leveling, GNSS, and other measurement methods to periodically monitor the movement of rock strata above the working face. However, these methods are costly, have limited observation points, are susceptible to human damage, and yield discrete point-based monitoring results, making it difficult to achieve continuous, full-range "area-like" monitoring of mining subsidence. Light Detection and Ranging (LiDAR) is a low-altitude laser remote sensing technology. By collecting laser point cloud data within the flight area, it can quickly and accurately acquire a Digital Elevation Model (DEM) of the coal mining subsidence area. By overlaying and subtracting multiple DEMs, surface elevation change data can be generated, yielding "area-like" mining subsidence results. However, due to limitations in point cloud data processing, such as filtering, interpolation, and modeling errors, the monitoring accuracy in areas with minor deformations at the edge of the subsidence basin is slightly insufficient.

[0004] Interferometric Synthetic Aperture Radar (InSAR), a newly developed space-based Earth observation technology, often achieves millimeter-level accuracy in its monitoring results. Leveraging its advantages of all-weather, high precision, and wide-area coverage, it has been successfully applied to the monitoring of surface subsidence from mining operations. However, due to the large-scale and nonlinear characteristics of mining subsidence, this technology is prone to interferometric decoherence in the large-scale deformation region at the center of the subsidence basin. This leads to phase aliasing, making it impossible to recover the true deformation, resulting in "holes" in the InSAR monitoring results and failing to capture the complete mining subsidence basin.

[0005] In summary, InSAR technology can obtain high-precision monitoring results at the edge of mining subsidence basins, but it cannot obtain effective information in the large-scale deformation areas at the center of the subsidence basin. LiDAR technology can obtain large-scale deformation at the center of the subsidence basin, but its accuracy is slightly lower in the edge areas. The two technologies can compensate for each other's shortcomings. Therefore, how to comprehensively utilize multiple technologies such as InSAR, LiDAR, and ground monitoring to obtain complete and accurate information on mining subsidence basins is a technical problem that needs to be solved in this field. Summary of the Invention

[0006] The purpose of this invention is to provide a method for monitoring mining subsidence basins using a combined air-space-ground observation approach. This method uses InSAR technology to obtain high-precision monitoring results at the edge of the mining subsidence basin, uses LiDAR technology to obtain large-scale deformation at the center of the subsidence basin, and uses data fusion methods to fuse multi-source monitoring results from air, space, and ground to achieve complete and accurate acquisition of mining subsidence information.

[0007] To achieve the above objectives, this invention provides a method for monitoring mining subsidence across an entire basin using a combined air-space-ground observation system, comprising the following steps: S1. Acquisition and processing of multi-source observation data from air, space, and ground: Acquire time-series SAR images of the target area and perform time-series D-InSAR data processing to obtain the cumulative time-series InSAR settlement in the vertical direction of the target area; acquire airborne LiDAR digital elevation model (DEM) data before and after settlement, and obtain the LiDAR settlement results by differential subtraction; set up ground observation points on the surface above the working face and carry out precise leveling measurements simultaneously to obtain the measured settlement at each ground observation point. S2. Delineation of subsidence area boundaries of different magnitudes: Based on the InSAR maximum deformation gradient theory, the boundaries of small to medium magnitude subsidence areas are calculated and determined; using ground leveling data, the accuracy of InSAR subsidence results and LiDAR-DEM data is evaluated and errors are calculated to determine the boundaries of medium to large magnitude subsidence areas. S3. Fusion of multi-source subsidence observation results: Based on the delineated boundaries, InSAR subsidence results are retained for small-scale subsidence areas, LiDAR subsidence results are retained for large-scale subsidence areas, and InSAR and LiDAR subsidence results are fused using a priori weighting method for medium-scale subsidence areas, ultimately obtaining a complete mining subsidence basin.

[0008] Preferably, in S1, the time-series D-InSAR data processing specifically involves: performing pairwise differential interferometry on the SAR images according to the time series to form... N A differential interferometric image pair; after filtering, phase unwrapping, phase transformation, geocoding, and time-series accumulation processing, the time-series InSAR cumulative settlement in the vertical direction of the target area is obtained; The LiDAR subsidence results are obtained by solving, filtering, and interpolating airborne LiDAR point cloud data to generate two DEM data before and after subsidence. The differential DEM surface elevation change model is then obtained by subtracting the earlier DEM from the later DEM, which is the LiDAR subsidence result.

[0009] Preferably, in S2, the determination of the boundary of the small- to medium-sized subsidence area includes the following: S201, Calculation N Average coherence value of each differential interference image pair : ; in, for N The coherence coefficient values ​​for each differential interference image pair range from [0,1]. S202. Calculate the actual maximum deformation gradient of InSAR based on the deformation gradient function model with coherence as the independent variable. D max : ; in , For SAR satellite data wavelength, SAR image pixel resolution; S203, Calculation N The maximum subsidence detected by each interferometer pair is used to determine the fusion boundary of the InSAR monitoring results, i.e., the boundary of the small to medium-sized subsidence area: .

[0010] Preferably, in S2, the mean square error of the InSAR settlement results The calculation formula is: ; in, For the first i InSAR monitoring subsidence values ​​corresponding to each ground monitoring point For the first i The measured leveling subsidence values ​​at each ground monitoring point n This represents the total number of ground monitoring points.

[0011] Preferably, in S2, the determination of the boundary of the medium-to-large-scale subsidence area includes the following: S211, based on n Based on the results from several ground monitoring points, the accuracy of the two DEM data sets acquired by the airborne LiDAR was statistically analyzed, and the error was calculated. and : , ; in For the first i The first phase DEM elevation values ​​corresponding to each ground monitoring point For the first i The first phase of measured leveling elevation values ​​at each ground monitoring point; For the first i The second-phase DEM elevation values ​​corresponding to each ground monitoring point For the first i The second phase of measured leveling elevation values ​​at each ground monitoring point; S212. Based on the law of error propagation, calculate the mean square error of the surface elevation change model after subtracting the two DEMs. for: ; Using three times the mean square error as the fusion boundary after subtracting the two LiDAR DEM phases, i.e., the boundary of the medium- to large-scale subsidence region, is represented as follows: .

[0012] Preferably, S3 specifically includes the following: S31. Resample the LiDAR settlement results to the InSAR result resolution and unify the spatial reference of InSAR, LiDAR and leveling monitoring results; S32. Based on the obtained InSAR and LiDAR data fusion boundary, establish a data fusion model: ; in Based on InSAR monitoring results, For LiDAR monitoring results, weights , Determined by prior weighting: , ; S33. Spatial interpolation is performed on the small-scale, medium-scale, and large-scale subsidence information obtained according to the data fusion model to obtain complete and accurate mining subsidence basins.

[0013] Therefore, this invention adopts the above-mentioned method for monitoring mining subsidence across the entire basin through integrated air-space-ground observation. By combining multi-source data from air, space, and ground with precise regional fusion, it overcomes the problem that single monitoring technologies cannot obtain complete and accurate information on mining subsidence. It also solves the technical defects of central incoherence in InSAR monitoring results and insufficient edge precision in LiDAR monitoring results. Ultimately, it obtains complete and accurate data on mining subsidence across the entire basin, which can provide a reliable basis for ecological restoration and disaster prevention in mining areas.

[0014] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of data fusion according to an embodiment of the present invention; Figure 2 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0017] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Example 1 This embodiment is combined with the appendix Figure 1 (Data fusion diagram), Appendix Figure 2 (Workflow diagram) This document details the implementation process of a comprehensive basin monitoring method for mining subsidence using a combined air-space-ground observation approach. The specific steps are as follows: S1. Acquisition and processing of multi-source observation data from air, space, and ground.

[0019] S11, InSAR Data Acquisition and Processing: Obtaining data covering the entire mining cycle of this working face. N +1 scene of temporal SAR imagery, processed by pairwise differential interferometry according to the time series, to form N A differential interferometric image pair is used to sequentially perform filtering, phase unwrapping, phase deformation conversion, geocoding, and time-series accumulation processing to obtain the time-series InSAR cumulative subsidence in the vertical direction of the working face, accurately acquiring small deformation data at the edge of the subsidence basin.

[0020] S12, LiDAR Data Acquisition and Processing: According to the first and second scenes of SAR imagery N+1 scene transit time, respectively carried out airborne LiDAR flight scanning; the laser point cloud data was solved, filtered and interpolated to generate two DEM data before and after mining; the difference between the later DEM and the earlier DEM was subtracted to obtain the LiDAR subsidence results, effectively obtaining a large amount of deformation data in the center of the subsidence basin.

[0021] S13. Ground leveling data collection: Before mining begins, leveling data will be collected along the dip and strike of the ground surface. n A total of 1,000 ground observation points were established. SAR image transit time and LiDAR flight time were matched, and precise leveling measurements were carried out simultaneously to calculate the measured settlement at each observation point, which served as the benchmark data for subsequent accuracy evaluation and boundary delineation.

[0022] S2. Boundary delineation of subsidence areas of different magnitudes: Based on actual ground leveling data, the graded boundaries of subsidence areas are determined using formulas, as detailed below: S21, Boundary of small to medium-sized subsidence areas (effective InSAR monitoring boundary): S201, Calculation N Average coherence value of each differential interference image pair : ; in, for N The coherence coefficient values ​​of the differential interference image pairs range from [0,1].

[0023] S202. Calculate the actual maximum deformation gradient of InSAR based on the deformation gradient function model with coherence as the independent variable. D max : ; in , For SAR satellite data wavelength, This represents the pixel resolution of the SAR image.

[0024] S203, Calculation N The maximum subsidence detected by each interferometer pair is used to determine the fusion boundary of the InSAR monitoring results, i.e., the boundary of the small to medium-sized subsidence area: .

[0025] InSAR sedimentation results mean error The calculation formula is: ; in, For the first i InSAR monitoring subsidence values ​​corresponding to each ground monitoring point For the first i The measured leveling subsidence values ​​at each ground monitoring point n This represents the total number of ground monitoring points.

[0026] S22, Boundary of Medium to Large-Scale Subsidence Areas (LiDAR Effective Monitoring Boundary): S211, based on n Based on the results from several ground monitoring points, the accuracy of the two DEM data sets acquired by the airborne LiDAR was statistically analyzed, and the error was calculated. and : , ; in For the first i The first phase DEM elevation values ​​corresponding to each ground monitoring point For the first i The first phase of measured leveling elevation values ​​at each ground monitoring point; For the first i The second-phase DEM elevation values ​​corresponding to each ground monitoring point For the first i The second phase of measured leveling elevation values ​​at each ground monitoring point.

[0027] S212. Based on the law of error propagation, calculate the mean square error of the surface elevation change model after subtracting the two DEMs. for: ; Using three times the mean square error as the fusion boundary after subtracting the two LiDAR DEM phases, i.e., the boundary of the medium- to large-scale subsidence region, is represented as follows: .

[0028] S3. Fusion of Multi-Source Subsidence Observation Results. This step achieves complete stitching of subsidence data across the entire basin through regional preservation and medium-level prior weighting. The specific fusion method is as follows: S31. Pre-processing before fusion: S311, Resolution unification: Resample the LiDAR settling results to the pixel resolution of the InSAR settling results to ensure data spatial matching; S312. Spatial benchmark unification: Unify InSAR, LiDAR, and leveling monitoring results to the same geographic coordinate system and projected coordinate system to eliminate spatial bias.

[0029] S32. Assignment of subsidence data by region: S321, Small-scale subsidence areas (basin edges): InSAR subsidence results are directly retained, and its millimeter-level high precision advantage is utilized to ensure the accuracy of monitoring small deformations at the edge. S322, Large-scale subsidence area (basin center): Directly retain LiDAR subsidence results, solve the InSAR decoherence problem, and obtain real data on large deformation in the center; S323, Medium-sized subsidence area (transition zone): A priori weighted fusion model is used to weight and fuse InSAR and LiDAR data to ensure smooth and continuous data in the transition zone. The specific process is as follows: Based on the obtained InSAR and LiDAR data fusion boundary, a data fusion model is established: ; in Based on InSAR monitoring results, For LiDAR monitoring results, weights , Determined by prior weighting: , .

[0030] S33. Spatial interpolation is performed on the small-scale, medium-scale, and large-scale subsidence information obtained according to the data fusion model to obtain complete and accurate mining subsidence basins.

[0031] Therefore, this invention adopts the above-mentioned method for monitoring mining subsidence across the entire basin through integrated air-space-ground observation. By combining multi-source data from air, space, and ground with precise regional fusion, it overcomes the problem that single monitoring technologies cannot obtain complete and accurate information on mining subsidence. It also solves the technical defects of central incoherence in InSAR monitoring results and insufficient edge precision in LiDAR monitoring results. Ultimately, it obtains complete and accurate data on mining subsidence across the entire basin, which can provide a reliable basis for ecological restoration and disaster prevention in mining areas.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for monitoring mining subsidence across an entire basin using integrated air-space-ground observation, characterized in that, Includes the following steps: S1. Acquisition and processing of multi-source observation data from air, space, and ground: Acquire time-series SAR images of the target area and perform time-series D-InSAR data processing to obtain the cumulative time-series InSAR settlement in the vertical direction of the target area; acquire airborne LiDAR digital elevation model (DEM) data before and after settlement, and obtain the LiDAR settlement results by differential subtraction; set up ground observation points on the surface above the working face and carry out precise leveling measurements simultaneously to obtain the measured settlement at each ground observation point. S2. Delineation of subsidence area boundaries of different magnitudes: Based on the InSAR maximum deformation gradient theory, the boundaries of small to medium magnitude subsidence areas are calculated and determined; using ground leveling data, the accuracy of InSAR subsidence results and LiDAR-DEM data is evaluated and errors are calculated to determine the boundaries of medium to large magnitude subsidence areas. S3. Fusion of multi-source subsidence observation results: Based on the delineated boundaries, InSAR subsidence results are retained for small-scale subsidence areas, LiDAR subsidence results are retained for large-scale subsidence areas, and InSAR and LiDAR subsidence results are fused using a priori weighting method for medium-scale subsidence areas, ultimately obtaining a complete mining subsidence basin.

2. The method for monitoring mining subsidence across the entire basin using integrated air-space-ground observation as described in claim 1, characterized in that, In S1, the time-series D-InSAR data processing specifically involves: performing pairwise differential interferometry on the SAR images according to the time series to form... N A differential interferometric image pair; after filtering, phase unwrapping, phase transformation, geocoding, and time-series accumulation processing, the time-series InSAR cumulative settlement in the vertical direction of the target area is obtained; The LiDAR subsidence results are obtained by solving, filtering, and interpolating airborne LiDAR point cloud data to generate two DEM data before and after subsidence. The differential DEM surface elevation change model is then obtained by subtracting the earlier DEM from the later DEM, which is the LiDAR subsidence result.

3. The method for monitoring mining subsidence across the entire basin using integrated air-space-ground observation as described in claim 2, characterized in that, In S2, the determination of the boundaries of small- to medium-sized subsidence areas includes the following: S201, Calculation N Average coherence value of each differential interference image pair : ； in, for N The coherence coefficient values ​​for each differential interference image pair range from [0,1]. S202. Calculate the actual maximum deformation gradient of InSAR based on the deformation gradient function model with coherence as the independent variable. D max : ； in , For SAR satellite data wavelength, SAR image pixel resolution; S203, Calculation N The maximum subsidence detected by each interferometer pair is used to determine the fusion boundary of the InSAR monitoring results, i.e., the boundary of the small to medium-sized subsidence area: 。 4. The method for monitoring mining subsidence across the entire basin using integrated air-space-ground observation as described in claim 3, characterized in that, In S2, the mean square error of InSAR settlement results The calculation formula is: ； in, For the first i InSAR monitoring subsidence values ​​corresponding to each ground monitoring point For the first i The measured leveling subsidence values ​​at each ground monitoring point n This represents the total number of ground monitoring points.

5. The method for monitoring mining subsidence across the entire basin using integrated air-space-ground observation as described in claim 4, characterized in that, In S2, the determination of the boundaries of medium- to large-scale subsidence areas includes the following: S211, based on n Based on the results from several ground monitoring points, the accuracy of the two DEM data sets acquired by the airborne LiDAR was statistically analyzed, and the error was calculated. and : ， ； in For the first i The first phase DEM elevation values ​​corresponding to each ground monitoring point For the first i The first phase of measured leveling elevation values ​​at each ground monitoring point; For the first i The second-phase DEM elevation values ​​corresponding to each ground monitoring point For the first i The second phase of measured leveling elevation values ​​at each ground monitoring point; S212. Based on the law of error propagation, calculate the mean square error of the surface elevation change model after subtracting the two DEMs. for: ； Using three times the mean square error as the fusion boundary after subtracting the two LiDAR DEM phases, i.e., the boundary of the medium- to large-scale subsidence region, is represented as follows: .

6. The method for monitoring mining subsidence across the entire basin using integrated air-space-ground observation as described in claim 5, characterized in that, S3 specifically includes the following: S31. Resample the LiDAR settlement results to the InSAR result resolution and unify the spatial reference of InSAR, LiDAR, and leveling monitoring results; S32. Based on the obtained InSAR and LiDAR data fusion boundary, establish a data fusion model: ； in Based on InSAR monitoring results, For LiDAR monitoring results, weights , Determined by prior weighting: ， ； S33. Spatial interpolation is performed on the small-scale, medium-scale, and large-scale subsidence information obtained according to the data fusion model to obtain complete and accurate mining subsidence basins.

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