Measurement Method and Device for the Boundary of the Subsidence Basin in the Goaf of Coal Mines

Abstract

This application relates to the field of measurement technology, and particularly to a method and device for measuring the boundary of a subsidence basin in a coal mine goaf. The method includes: obtaining synthetic aperture radar (SAR) image data of the boundary of the subsidence basin in the coal mine goaf and preprocessing it to generate a registered SAR image dataset; establishing a first multi-master image interference phase dataset and inversing the mining process according to the center position of the differential interference fringe pattern; performing stacking processing on the differential interference map to obtain an average rate map of surface deformation affected by mining to delineate the range of surface deformation affected by mining; generating a single-master image differential interference phase dataset and a second multi-master image interference phase dataset, and establishing a coupled image interference phase dataset to achieve high-precision measurement of the boundary of the subsidence basin in the coal mine goaf. Thus, the mining process can be reconstructed in the absence of historical data of the mining area, high-precision measurement of the boundary range and settlement magnitude of the subsidence basin in the coal mine goaf can be achieved, and the refinement level of the monitoring results can be improved.

Description

Technical Field

[0001] The present application relates to the field of measurement technology, and particularly to a method and device for measuring the boundary of a subsidence basin in a coal mine goaf. Background Art

[0002] The edge of the surface subsidence basin caused by underground coal mining is prone to tensile fracture damage, resulting in uneven ground settlement and seriously damaging the safety of buildings (structures). Manual field measurement is difficult to provide surface observation data covering the entire subsidence basin, and it is impossible to accurately delineate the boundary of the subsidence basin.

[0003] In related technologies, InSAR (Interferometric Synthetic Aperture Radar) has rapidly developed into an emerging space-to-ground observation technology due to its advantages of all-weather, high-precision, wide coverage, etc., and plays an important role in detecting the boundary of the surface subsidence basin in mining areas. Currently, the commonly used InSAR technologies include DInSAR (Differential Interferometric Synthetic Aperture Radar), PS-InSAR (Permanent Scatterer Interferometry), and SBAS-InSAR (Small Baseline Subset Interferometry), etc.

[0004] However, the DInSAR method has the ability to monitor large-scale surface deformation, but factors such as spatio-temporal decorrelation, atmospheric delay, and DEM (Digital Elevation Model) error limit the accuracy of surface deformation monitoring in mining areas. Although the time-series InSAR technologies represented by PS-InSAR and SBAS-InSAR can weaken spatio-temporal decorrelation and atmospheric noise to obtain millimeter-level deformation monitoring results, it is difficult to obtain a sufficient number of monitoring point targets. Therefore, the related technologies cannot meet the high-precision measurement of the boundary of the subsidence basin in the coal mine goaf and need to be improved urgently. Summary of the Invention

[0005] The present application provides a method and device for measuring the boundary of a subsidence basin in a coal mine goaf to solve the problems in related technologies that due to the influence of factors such as spatio-temporal decorrelation, atmospheric delay, and DEM error, the accuracy of surface deformation monitoring in mining areas is limited, and due to the difficulty of PS-InSAR and SBAS-InSAR in obtaining a sufficient number of monitoring point targets, it may not be possible to achieve high-precision measurement of the boundary of the subsidence basin in the coal mine goaf, etc.

[0006] The first aspect of the present application provides a method for measuring the boundary of a coal mine goaf subsidence basin, including the following steps: obtaining SAR (Synthetic Aperture Radar) image data covering the coal mine goaf subsidence basin, and preprocessing the SAR image data to generate a registered SAR image dataset; establishing a first multi-master image interferometric phase dataset based on the SAR image dataset to obtain a differential interferogram corresponding to the first multi-master image interferometric phase dataset, and reconstructing the mining process according to the differential interferogram; performing stacking processing on the differential interferogram to obtain an average rate map of surface deformation affected by mining, so as to delineate the range of surface deformation affected by mining according to the average rate map; generating a single-master image differential interferometric phase dataset and a second multi-master image interferometric phase dataset based on the SAR image dataset, and establishing a coupled image interferometric phase dataset based on the single-master image differential interferometric phase dataset and the second multi-master image interferometric phase dataset, so as to analyze the coupled image interferometric phase dataset to obtain time-series deformation data of the boundary of the coal mine goaf subsidence basin; combining the mining process, the range of surface deformation affected by mining, and the time-series deformation data to obtain the measurement result of the boundary of the coal mine goaf subsidence basin.

[0007] Optionally, in an embodiment of the present application, the reconstructing the mining process according to the differential interferogram includes: processing the first multi-master image interferometric phase dataset to obtain an average coherence map of the first multi-master image interferometric phase dataset; determining at least one unwrapping reference point that meets a preset condition based on the average coherence map, so as to perform phase unwrapping on the interferogram according to the at least one unwrapping reference point to obtain a differential interference fringe map; reconstructing the mining process according to the center position of the differential interference fringe map.

[0008] Optionally, in an embodiment of the present application, the first multi-master image interferometric phase dataset is (n - 4)×7 - 1 pairs of interferograms generated from n SAR images, where n is a positive integer.

[0009] Optionally, in an embodiment of the present application, the process of stacking the differential interferograms to obtain the average rate map of surface deformation affected by mining includes: stacking the differential interferograms to obtain a first set of differential interferograms, and estimating and removing the quadratic phase change rate from the first set of differential interferograms to obtain a second set of differential interferograms; calculating the phase change rate, the standard deviation of the phase change rate, and the standard deviation of the residual phase for each pixel in the second set of differential interferograms, and performing linear regression on the atmospheric components related to elevation based on the phase change rate, the standard deviation of the phase change rate, and the standard deviation of the residual phase of each pixel to obtain the atmospheric phase delay, and filtering the atmospheric phase delay using a large window to obtain a target set of differential interferograms; generating a phase unwrapping mask map based on the intensity and coherence of the pixels in the target set of differential interferograms, and determining the target measurement points based on the mask map, so as to obtain the average rate map of surface deformation affected by mining based on the target measurement points.

[0010] Optionally, in an embodiment of the present application, the expression of the average rate map of surface deformation affected by mining is:

[0011]

[0012] where V mean is the average rate of surface deformation, φ i is the unwrapped phase of N interferograms, and ΔT i is the time interval.

[0013] Optionally, in an embodiment of the present application, the process of establishing a coupled image interferometric phase dataset based on the single-master-image differential interferometric phase dataset and the second multi-master-image interferometric phase dataset includes: selecting permanent scatterers based on the SAR image dataset, and selecting master images according to the spatio-temporal baseline principle, so as to combine the permanent scatterers and the master images to establish the single-master-image differential interferometric phase dataset; calculating a baseline file based on the SAR image dataset, and establishing the second multi-master-image interferometric phase dataset based on the baseline file using the short baseline principle and the multi-master-image principle; performing compatibility processing on the single-master-image differential interferometric phase dataset and the second multi-master-image interferometric phase dataset, and converting each pixel value in the single-master-image differential interferometric phase dataset and the second multi-master-image interferometric phase dataset into a corresponding complex value, so as to superimpose the complex values at the corresponding positions to obtain the coupled image interferometric phase dataset.

[0014] Optionally, in an embodiment of the present application, the second multi-master-image interferometric phase dataset is (n - 2)×3(n - 2)*3 pairs of interferograms generated from n SAR images, where n is a positive integer.

[0015] The second aspect of the present application provides a measuring device for the boundary of a coal mine goaf subsidence basin, including: an acquisition module, configured to acquire synthetic aperture radar (SAR) image data covering the coal mine goaf subsidence basin and preprocess the SAR image data to generate a registered SAR image data set; a construction module, configured to establish a first multi-master image interference phase data set based on the SAR image data set to obtain a differential interferogram corresponding to the first multi-master image interference phase data set, and reconstruct the mining process according to the differential interferogram; a processing module, configured to perform stacking processing on the differential interferogram to obtain an average rate map of surface deformation affected by mining, so as to demarcate the range of surface deformation affected by mining according to the average rate map; a generation module, configured to generate a single-master image differential interference phase data set and a second multi-master image interference phase data set based on the SAR image data set, and establish a coupled image interference phase data set based on the single-master image differential interference phase data set and the second multi-master image interference phase data set, so as to analyze the coupled image interference phase data set to obtain time-series deformation data of the boundary of the coal mine goaf subsidence basin; a measurement module, configured to combine the mining process, the range of surface deformation affected by mining, and the time-series deformation data to obtain a measurement result of the boundary of the coal mine goaf subsidence basin.

[0016] Optionally, in an embodiment of the present application, the construction module includes: a first processing unit, configured to process the first multi-master image interference phase data set to obtain an average coherence map of the first multi-master image interference phase data set; a first generation unit, configured to determine at least one unwrapping reference point that meets a preset condition based on the average coherence map, so as to perform phase unwrapping on the interferogram according to the at least one unwrapping reference point to obtain a differential interference fringe map; a reconstruction unit, configured to reconstruct the mining process according to the center position of the differential interference fringe map.

[0017] Optionally, in an embodiment of the present application, the first multi-master image interference phase data set is (n - 4)×7 - 1 pairs of interferograms generated from n SAR images, where n is a positive integer.

[0018] Optionally, in an embodiment of the present application, the processing module includes: a second processing unit configured to stack the differential interferograms to obtain a first set of differential interferograms, and estimate and remove the quadratic phase change rate from the first set of differential interferograms to obtain a second set of differential interferograms; a calculation unit configured to calculate the phase change rate, the standard deviation of the phase change rate, and the standard deviation of the residual phase for each pixel point in the second set of differential interferograms, and perform linear regression on the atmospheric components related to elevation based on the phase change rate, the standard deviation of the phase change rate, and the standard deviation of the residual phase for each pixel point to obtain the atmospheric phase delay, and filter the atmospheric phase delay using a large window to obtain a target set of differential interferograms; a second generation unit configured to generate a phase unwrapping mask map based on the intensity and coherence of the pixel points in the target set of differential interferograms, and determine a target measurement point based on the mask map, so as to obtain an average rate map of the surface deformation affected by mining based on the target measurement point.

[0019] Optionally, in an embodiment of the present application, the expression of the average rate map of the surface deformation affected by mining is:

[0020]

[0021] where V mean is the average rate of surface deformation, φ i is the unwrapped phase of N interferograms, and ΔT i is the time interval.

[0022] Optionally, in an embodiment of the present application, the generation module includes: a first construction unit configured to select permanent scatterers based on the SAR image dataset, and select a master image according to the spatio-temporal baseline principle, so as to establish the single-master-image differential interferometric phase dataset by combining the permanent scatterers and the master image; a second construction unit configured to calculate a baseline file according to the SAR image dataset, and establish the second multi-master-image interferometric phase dataset based on the baseline file using the short baseline principle and the multi-master-image principle; a third processing unit configured to perform compatibility processing on the single-master-image differential interferometric phase dataset and the second multi-master-image interferometric phase dataset, and convert each pixel value in the single-master-image differential interferometric phase dataset and the second multi-master-image interferometric phase dataset into a corresponding complex value, so as to superimpose the complex values at the corresponding positions to obtain the coupled image interferometric phase dataset.

[0023] Optionally, in an embodiment of the present application, the second multi-master-image interferometric phase dataset is (n - 2)×3(n - 2)*3 pairs of interferograms generated from n SAR images, where n is a positive integer.

[0024] A third aspect embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the method for measuring the boundary of the coal mine goaf subsidence basin as described in the above embodiments.

[0025] A fourth aspect embodiment of the present application provides a computer-readable storage medium storing a computer program, and when the program is executed by a processor, it implements the method for measuring the boundary of the coal mine goaf subsidence basin as described above.

[0026] A fifth aspect embodiment of the present application provides a computer program product, and when the computer program is executed, it is used to implement the method for measuring the boundary of the coal mine goaf subsidence basin as described above.

[0027] The embodiments of the present application can obtain SAR image data of the boundary of the coal mine goaf subsidence basin, generate a registered SAR image data set, and then can establish a first multi-master image interference phase data set, invert the mining process according to the center position of the differential interference fringe map, and perform stacking processing on the differential interference map to obtain an average rate map of the surface deformation affected by mining to delineate the range of the surface deformation affected by mining. Thus, a single-master image differential interference phase data set and a second multi-master image interference phase data set can be generated to establish a coupled image interference phase data set, perform interference processing and time series inversion analysis on the coherent target points with stable scattering characteristics, and obtain deformation data of the boundary of the coal mine goaf subsidence basin, which can effectively improve the density and spatial coverage rate of monitoring points, help obtain time series deformation data of the boundary of the coal mine goaf subsidence basin, and achieve millimeter-level high-precision measurement. Therefore, it solves the problems in the related art that due to the possible influence of factors such as spatio-temporal decorrelation, atmospheric delay, and DEM error on DInSAR, the accuracy of surface deformation monitoring in mining areas is limited, and due to the difficulty of PS-InSAR and SBAS-InSAR in obtaining a sufficient number of monitoring point targets, it may not be possible to achieve high-precision measurement of the boundary of the coal mine goaf subsidence basin.

[0028] Additional aspects and advantages of the present application will be partially given in the following description, partially become apparent from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and / or additional aspects and advantages of the present application will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0030] Figure 1 It is a flowchart of a method for measuring the boundary of a coal mine goaf subsidence basin according to an embodiment of the present application;

[0031] Figure 2It is a schematic block diagram of the principle for measuring the boundary of a coal mine goaf subsidence basin based on radar remote sensing SAR images according to an embodiment of the present application;

[0032] Figure 3 It is a schematic diagram for reconstructing the mining process according to an interference fringe map according to an embodiment of the present application;

[0033] Figure 4 It is a schematic diagram of the spatio-temporal baseline distribution of a stacked interference pair according to an embodiment of the present application;

[0034] Figure 5 It is a schematic diagram of the average surface deformation rate of a coal mine goaf subsidence basin according to an embodiment of the present application;

[0035] Figure 6 It is a schematic diagram of the spatio-temporal baseline of a single-master-image differential interference pair according to an embodiment of the present application;

[0036] Figure 7 It is a schematic diagram of the spatio-temporal baseline of a multi-master-image differential interference pair according to an embodiment of the present application;

[0037] Figure 8 It is a schematic diagram for obtaining high-density measurement points by coupling the differential interference phases of single and multi-master images according to an embodiment of the present application;

[0038] Figure 9 It is a schematic diagram of the time-series deformation of millimeter-level high-precision measurement points on the boundary of a coal mine goaf subsidence basin according to an embodiment of the present application;

[0039] Figure 10 It is a schematic block diagram of a measuring device for the boundary of a coal mine goaf subsidence basin provided according to an embodiment of the present application;

[0040] Figure 11 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. Detailed implementation manners

[0041] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as limiting the present application.

[0042] The measurement method of the subsidence basin boundary in a coal mine goaf according to the embodiments of the present application will be described below with reference to the accompanying drawings. In view of the related technologies mentioned in the above background art, since DInSAR may be affected by factors such as spatio-temporal decoherence, atmospheric delay, and DEM error, which limit the accuracy of surface deformation monitoring in mining areas, and since it is difficult for PS-InSAR and SBAS-InSAR to obtain a sufficient number of monitoring point targets, it may not be possible to achieve high-precision measurement of the subsidence basin boundary in a coal mine goaf. The present application provides a measurement method for the subsidence basin boundary in a coal mine goaf. In this method, SAR image data of the subsidence basin boundary in a coal mine goaf can be obtained, a registered SAR image data set can be generated, and then a first multi-master image interference phase data set can be established. The mining process can be inverted according to the central position of the differential interference fringe pattern, and the differential interference maps are stacked to obtain an average rate map of surface deformation affected by mining, so as to delineate the range of surface deformation affected by mining. Thus, a single-master image differential interference phase data set and a second multi-master image interference phase data set can be generated to establish a coupled image interference phase data set. Interference processing and time series inversion analysis are performed on the coherent target points with stable scattering characteristics to obtain deformation data of the subsidence basin boundary in a coal mine goaf, which can effectively improve the density and spatial coverage rate of monitoring points, help obtain time series deformation data of the subsidence basin boundary in a coal mine goaf, and achieve high-precision measurement at the millimeter level. Thereby, the problems in the related technologies are solved, that is, since DInSAR may be affected by factors such as spatio-temporal decoherence, atmospheric delay, and DEM error, which limit the accuracy of surface deformation monitoring in mining areas, and since it is difficult for PS-InSAR and SBAS-InSAR to obtain a sufficient number of monitoring point targets, it may not be possible to achieve high-precision measurement of the subsidence basin boundary in a coal mine goaf, etc.

[0043] Specifically, Figure 1 is a schematic flow chart of a measurement method for the subsidence basin boundary in a coal mine goaf provided by an embodiment of the present application.

[0044] As Figure 1 shown, the measurement method for the subsidence basin boundary in a coal mine goaf includes the following steps:

[0045] In step S101, synthetic aperture radar (SAR) image data covering the subsidence basin in a coal mine goaf is obtained, and the SAR image data is preprocessed to generate a registered SAR image data set.

[0046] It can be understood that the SAR image data refers to the SLC (Single-look Complex) of the image. Among them, image registration of the SAR image data requires the assistance of a digital elevation model. The preprocessing includes but is not limited to selection, geocoding, image registration, deskewing, adding precise orbit data, and cropping.

[0047] In the actual implementation process, combined with Figure 2 As shown, the embodiments of the present application can obtain SAR image data, that is, the single-look complex image pair of the image. Through data processing such as selection, geocoding, image registration, de-skewing, adding precise orbit data, and cropping, and with the assistance of DEM, a precisely registered SAR image dataset covering the study area can be generated.

[0048] Specifically, taking a certain coal mine as the research object and Sentinel-1A / B satellite data as an example, after basic data preprocessing such as selection of single-look complex (SLC) image pairs, geocoding, image registration, de-skewing, adding precise orbit data, and cropping, a registered SAR time series dataset can be formed. Among them, 25 Sentinel-1 SAR data from July 2020 to May 2021 can be obtained. Sentinel-1A / B is an important part of the ESA Copernicus program, equipped with a C-band SAR sensor, and the single-satellite revisit period is 12 days. The image data range completely covers the study area data, the imaging mode is the Interferometric Wide (IW) mode, the polarization mode is dual polarization (VV), the incidence angle is 41.67°, the image data type is SLC data, and the information of the Sentinel-1A satellite data used in the study area is shown in Table 1. Table 1 is the information description table of Sentinel-1A satellite data.

[0049] The embodiments of the present application can apply precise orbit data to the SAR image, and by virtue of the accurate satellite position and velocity information provided by the precise orbit file, update the orbit state vector of the SAR image metadata to reduce satellite orbit errors.

[0050] Furthermore, the embodiments of the present application can use open-source digital elevation model (DEM) data to compensate for the offset caused by the terrain and perform precise image registration. In the case of a large vertical baseline, the ground undulation will cause additional offsets between the master and slave images, and there will be a local offset of about 2m for every 1000m change in elevation; the calculation assisted by the DEM model takes into account the offset error generated by the terrain. Download the digital elevation model NASADEM data with a resolution of 30m for precise registration, and the spatial reference coordinate system is GCS WGS1984. In addition, the registered image is cropped to the study area, and the longitude and latitude of the center position of the study area are 112.39°, 39.00°.

[0051] Table 1

[0052] Serial Number Imaging Date Imaging Mode Polarization Method Incident Angle Data Type 1 20200725 IW VV 41.67° SLC 2 20200806 IW VV 41.67° SLC 3 20200818 IW VV 41.67° SLC 4 20200830 IW W 41.67° SLC 5 20200911 IW VV 41.67° SLC 6 20200923 IW VV 41.67° SLC 7 20201005 IW VV 41.67° SLC 8 20201017 IW VV 41.67° SLC 9 20201029 IW VV 41.67° SLC 10 20201110 IW VV 41.67° SLC 11 20201122 IW VV 41.67° SLC 12 20201204 IW VV 41.67° SLC 13 20201216 IW VV 41.67° SLC 14 20201228 IW VV 41.67° SLC 15 20210109 IW VV 41.67° SLC 16 20210121 IW VV 41.67° SLC 17 20210202 IW VV 41.67° SLC 18 20210214 IW VV 41.67° SLC 19 20210226 IW VV 41.67° SLC 20 20210310 IW VV 41.67° SLC 21 20210322 IW VV 41.67° SLC 22 20210403 1W VV 41.67° SLC 23 20210415 1W VV 41.67° SLC 24 20210427 1W VV 41.67° SLC 25 20210521 IW VV 41.67° SLC

[0053] By preprocessing the SAR synthetic aperture radar image data of the subsidence basin boundary in the coal mine gob area, the embodiments of the present application can effectively ensure the quality of the data, help eliminate or reduce errors caused by sensors, atmospheric conditions or terrain, and improve the accuracy and reliability of subsequent analysis.

[0054] In step S102, a first multi-master image interference phase data set is established based on the SAR image data set to obtain a differential interferogram corresponding to the first multi-master image interference phase data set, and the mining process is reconstructed according to the differential interferogram.

[0055] It can be understood that the first multi-master image interference phase data set can generate interferograms for each image and the seven images of subsequent dates according to the date of obtaining the images. Optionally, in an embodiment of the present application, the first multi-master image interference phase data set is (n - 4)×7 - 1 pairs of interferograms generated by n SAR images, where n is a positive integer.

[0056] Specifically, as Figure 3 shown, the embodiments of the present application can select 9 interferogram maps during the period from July 25, 2020 to May 21, 2021 according to the time sequence to reconstruct the mining process. The mining process is inverted according to the differential interference fringes. The coal mining in this study area started in August 2020 and gradually mined westward, and the mining was basically completed in April 2021.

[0057] The embodiments of the present application establish a first multi-master image interference phase data set based on the SAR image data set and obtain the corresponding differential interferogram, which can include more observation angles and time points, effectively reflect the small changes of complex terrains such as the subsidence basin boundary in the coal mine gob area, help improve the accuracy of surface deformation monitoring, and can help reconstruct the mining history and predict the future settlement trend.

[0058] Optionally, in an embodiment of the present application, reconstructing the mining process according to the differential interferogram includes: processing the first multi-master image interference phase data set to obtain an average coherence map of the first multi-master image interference phase data set; determining at least one unwrapping reference point that meets a preset condition based on the average coherence map to perform phase unwrapping on the interferogram according to the at least one unwrapping reference point to obtain a differential interference fringe map; and reconstructing the mining process according to the center position of the differential interference fringe map.

[0059] In some embodiments, in combination with Figure 2As shown, embodiments of the present application can calculate a baseline file and establish an interferometric dataset of multi-master images according to the obtained registered SAR image dataset based on the principle of multi-master images, and perform multi-look processing during the interferogram generation process to improve coherence. Among them, according to the date of image acquisition, an interferogram is generated between each image and seven images of subsequent dates respectively. That is, (25 - 4)×7 - 1 = 146 pairs of interferograms can be generated from 25 SAR images. Furthermore, an average coherence map of the multi-master image interferogram set can be generated, and unwrapping reference points that are closer to the research mining area, have higher coherence, and flatter terrain can be selected, so that the minimum cost flow method can be used to perform interferogram phase unwrapping to obtain a differential interferogram stripe map, and the mining process can be inverted based on the center position of the differential interferogram stripe map.

[0060] Embodiments of the present application perform phase unwrapping on the interferogram, and finally obtain a differential interferogram stripe map, which can effectively select areas with high coherence as unwrapping reference points, ensure the accuracy of phase unwrapping, reduce the cumulative error during the unwrapping process, and help improve the reliability and accuracy of the differential interferogram stripe map.

[0061] In step S103, the differential interferogram is stacked to obtain an average rate map of surface deformation affected by mining, so as to delineate the range of surface deformation affected by mining according to the average rate map.

[0062] Optionally, in an embodiment of the present application, stacking the differential interferogram to obtain an average rate map of surface deformation affected by mining includes: stacking the differential interferogram to obtain a first differential interferogram set, estimating and removing the quadratic phase change rate from the first differential interferogram set to obtain a second differential interferogram set; calculating the phase change rate, the standard deviation of the phase change rate, and the standard deviation of the residual phase for each pixel point in the second differential interferogram set, and performing linear regression on the atmospheric components related to elevation based on the phase change rate, the standard deviation of the phase change rate, and the standard deviation of the residual phase for each pixel point to obtain the atmospheric phase delay, and filtering the atmospheric phase delay using a large window to obtain a target differential interferogram set; generating a masking map for phase unwrapping based on the intensity and coherence of the pixel points in the target differential interferogram set, and determining the target measurement points based on the masking map, so as to obtain an average rate map of surface deformation affected by mining based on the target measurement points.

[0063] Specifically, in combination with Figure 2 and Figure 4 As shown, embodiments of the present application can stack the generated 146 pairs of differential interferograms, estimate and remove the quadratic phase change rate from the differential interferogram set, calculate the phase change rate, the standard deviation of the phase change rate, and the standard deviation of the residual phase, and then perform linear regression on the atmospheric components related to elevation, filter the atmospheric phase using a large window, and be able to generate a masking map for phase unwrapping based on intensity and coherence, retain the measurement points with higher coherence, and obtain asFigure 5 The average rate map of surface deformation affected by mining is shown, where the average rate (V mean ) of surface deformation affected by mining can be obtained through formula (1), so that the range of surface deformation affected by mining can be preliminarily delineated according to the average rate map of surface deformation.

[0064] Optionally, in an embodiment of the present application, the expression of the average rate map of surface deformation affected by mining is:

[0065]

[0066] where V mean is the average rate of surface deformation, φ i is the unwrapped phase of N interferograms, and ΔT i is the time interval.

[0067] In addition, the average rate (V mean ) of surface deformation in formula (1) can be calculated from the unwrapped phase (φ i ) of N interferograms and the time interval (ΔT i ) of SAR data acquisition. Here, N = 146, φ i represents the unwrapped phase of the interferogram, and the time interval ΔT i of SAR data acquisition corresponds to the acquisition interval time of two images required to generate the interferogram.

[0068] In the embodiment of the present application, stacking processing is performed on the differential interferograms, which can eliminate or reduce the influence of atmospheric delay and other non-permanent changes on the interferograms, help reduce noise, thereby improving the accuracy of monitoring surface deformation, and the average rate map can be used to determine the area with the most significant surface deformation, that is, the range directly affected by coal mining activities, which is conducive to the reasonable allocation of resources, helps to evaluate potential geological disaster risks, plan mining operations, and formulate repair measures.

[0069] In step S104, a single-master-image differential interference phase data set and a second multi-master-image interference phase data set are generated based on the SAR image data set, and a coupled-image interference phase data set is established based on the single-master-image differential interference phase data set and the second multi-master-image interference phase data set, so as to analyze the coupled-image interference phase data set to obtain the time-series deformation data of the boundary of the coal mine goaf subsidence basin.

[0070] During the actual execution process, in combination with Figure 2As shown in the figure, the embodiment of the present application can select permanent scatterers in the study area according to the obtained registered SAR image dataset, select the master image based on the spatio-temporal baseline, and establish a single-master-image differential interferometric phase dataset. Furthermore, according to the obtained registered SAR image dataset, based on the principle of short baseline and multi-master images, the baseline file can be calculated and the interferometric dataset of multi-master images can be established, that is, the second multi-master-image interferometric phase dataset.

[0071] Optionally, in an embodiment of the present application, the second multi-master-image interferometric phase dataset is (n - 2)×3(n - 2)*3 interferograms generated from n SAR images, where n is a positive integer.

[0072] Specifically, the interferometric phase dataset of multi-master images is specifically: according to the date of obtaining the images, each image generates interferograms with three subsequent-date images respectively, that is, n SAR images can generate (n - 2)×3 pairs of interferograms, and the influence of spatio-temporal decorrelation is controlled by setting the time and space baseline thresholds.

[0073] In addition, multi-look processing can be performed during the generation of interferograms to improve coherence, that is, to prepare for subsequent extraction of multi-view points. Among them, the multi-look parameters are determined by the specific range size of the study area. Thus, the single- and multi-master-image differential interferometric phases are converted into a vector data format for compatibility processing. The two differential interferograms are converted into real and imaginary parts and superimposed respectively, and then converted into a complex format, and a coupled image interferometric phase dataset can be established.

[0074] The embodiment of the present application generates a single-master-image differential interferometric phase dataset and a second multi-master-image interferometric phase dataset based on the SAR image dataset, and further establishes a coupled image interferometric phase dataset, which can significantly increase the density of monitoring points, make the monitoring results more refined, improve data compatibility, and enhance monitoring flexibility. Among them, the multi-master-image method reduces phase noise through multi-look processing, enhances the monitoring ability of fast or non-uniform surface motion, and effectively improves the monitoring accuracy. The coupling of the single-master-image differential interferometric phase dataset and the multi-master-image interferometric phase dataset makes it possible to measure surface deformation at the millimeter-level accuracy, effectively improving the monitoring accuracy.

[0075] Optionally, in an embodiment of the present application, a coupled image interferometric phase dataset is established based on a single-master image differential interferometric phase dataset and a second multi-master image interferometric phase dataset, including: selecting permanent scatterers based on the SAR image dataset, and selecting master images according to the spatio-temporal baseline principle, so as to combine the permanent scatterers and the master images to establish a single-master image differential interferometric phase dataset; calculating a baseline file based on the SAR image dataset, and establishing a second multi-master image interferometric phase dataset based on the baseline file by using the short baseline principle and the multi-master image principle; performing compatibility processing on the single-master image differential interferometric phase dataset and the second multi-master image interferometric phase dataset, and converting each pixel value in the single-master image differential interferometric phase dataset and the second multi-master image interferometric phase dataset into a corresponding complex value, so as to superimpose the complex values at the corresponding positions to obtain a coupled image interferometric phase dataset.

[0076] Specifically, in the embodiment of the present application, the permanent scatterers in the study area can be selected according to the obtained registered SAR image dataset, the master images can be selected according to the spatio-temporal baseline, and a single-master image differential interferometric phase dataset can be established. Among them, as Figure 6 shown, there are 25 SAR images in the embodiment of the present application. By selecting the image on December 4, 2024 as the master image and the remaining images as slave images, 24 differential interferometric pairs can be constructed.

[0077] Furthermore, in the embodiment of the present application, the baseline file can be calculated and the interferometric dataset of multi-master images can be established according to the obtained registered SAR image dataset based on the short baseline and multi-master image principles. Specifically, it can be as follows: according to the date of obtaining the images, an interferogram is generated for each image and three subsequent images respectively, that is, (25 - 2)×3 = 69 interferograms can be generated from 25 SAR images, and the influence of spatio-temporal decorrelation is controlled by setting the time and space baseline thresholds; the schematic diagram of the spatio-temporal baseline of the multi-master image differential interferometric pair is as Figure 7 shown; multi-looking processing is performed during the interferogram generation process to improve coherence, and the multi-looking parameters are determined by the specific size of the study area range, and the multi-looking parameters are 5:1.

[0078] In addition, in the embodiment of the present application, the single- and multi-master image differential interferometric phases can be converted into a vector data format for compatibility processing. The two differential interferograms are converted into real and imaginary parts and superimposed respectively, and then converted into a complex format to establish a coupled image interferometric phase dataset. Among them, 8799 monitoring points are obtained according to the single-master image differential interferometric phase, and the number of monitoring points obtained according to the multi-master image differential interferometric phase is 100834. The schematic diagram of the high-density and high-spatial-coverage monitoring points obtained by the coupled interferometric phase is as Figure 8 shown.

[0079] Based on the extraction of permanent scatterers from a single master image in the embodiments of the present application, the multi-master image method is coupled to increase the spatial coverage rate of monitoring points. The multi-master image method can effectively reduce the phase noise in the interferogram through the multi-look method, and can monitor fast and non-uniform surface movements and large-scale surface monitoring.

[0080] In step S105, the measurement result of the subsidence basin boundary of the coal mine goaf is obtained by combining the mining history, the range of surface deformation affected by mining, and the time-series deformation data.

[0081] Specifically, combine Figure 2 As shown, in the embodiments of the present application, reference points can be selected in the master image containing only permanent scatterers. Among them, the selection of reference points can comprehensively consider characteristics such as the middle position near the research area in the spatial domain, stability, and a high density of permanent scatterers. In the embodiments of the present application, stability and a high density of permanent scatterers are mainly considered. Furthermore, through continuous time-series calculation and iteration, the model parameters are gradually corrected from coarse to fine, non-interested coherent points are removed, and only the coherent target points with stable scattering characteristics are subjected to interferometric processing and time-series inversion analysis.

[0082] In addition, the multi-master image interferometric phase dataset is used to separate phase components such as elevation and atmospheric errors, and phase unwrapping errors are removed through spatial filtering, etc., to form a correctly unwrapped multi-master image phase set. Further, the single-master image time-series unwrapped phase is converted, the atmospheric phase is separated, and the coupled single-multi-master image interferometric time series analysis of time is obtained, and millimeter-level deformation data of the surface affected by the goaf is obtained, realizing high-precision measurement of the subsidence basin boundary of the coal mine goaf, and obtaining Figure 9 As shown in the schematic diagram of the time-series deformation of the high-precision measurement points of the subsidence basin boundary of the coal mine goaf. According to the millimeter-level high-precision time-series deformation measurement results, the time when the settlement starts to occur can be identified, which Figure 3 coincides with. The deformation of the subsidence basin boundary started in August 2020. As mining progresses, the surface deformation gradually accumulates, showing a deformation law similar to an "S" curve.

[0083] In the embodiments of the present application, the deformation data of the subsidence basin boundary of the coal mine goaf is obtained by combining the range of surface deformation affected by mining and the coupled image interferometric phase dataset. By obtaining the millimeter-level deformation data of the surface affected by the goaf and coupling the advantages of PSI and SBAS, more high-coherence monitoring points can be obtained, thereby greatly increasing the density of monitoring points, improving the refinement level of monitoring results, and the higher the density of monitoring points, the more accurately different phase component signals in the interferogram can be estimated, realizing centimeter-level measurement and delineation accuracy of the subsidence basin range and high-precision measurement of the millimeter-level time-series deformation of the subsidence basin boundary.

[0084] According to the measurement method of the subsidence basin boundary in the coal mine goaf proposed in the embodiments of the present application, synthetic aperture radar (SAR) image data of the subsidence basin boundary in the coal mine goaf can be obtained, a registered SAR image data set can be generated, and then a first multi-master image interference phase data set can be established. According to the center position of the differential interference fringe pattern, the mining process can be inverted, and the differential interference maps are stacked to obtain an average rate map of the surface deformation affected by mining, so as to delineate the range of the surface deformation affected by mining. Thus, a single-master image differential interference phase data set and a second multi-master image interference phase data set can be generated to establish a coupled image interference phase data set, and interference processing and time series inversion analysis are performed on the coherent target points with stable scattering characteristics to obtain the deformation data of the subsidence basin boundary in the coal mine goaf, which can effectively improve the density and spatial coverage rate of the monitoring points, help obtain the time series deformation data of the subsidence basin boundary in the coal mine goaf, and achieve millimeter-level high-precision measurement. Thereby, in the related technology, since DInSAR may be affected by factors such as spatio-temporal decorrelation, atmospheric delay, and DEM error, which limits the accuracy of the surface deformation monitoring in the mining area, and since it is difficult for PS-InSAR and SBAS-InSAR to obtain a sufficient number of monitoring point targets, it may not be possible to achieve high-precision measurement of the subsidence basin boundary in the coal mine goaf and other problems are solved.

[0085] Next, a measurement device for the subsidence basin boundary in the coal mine goaf proposed in the embodiments of the present application is described with reference to the accompanying drawings.

[0086] Figure 10 It is a block diagram of the measurement device for the subsidence basin boundary in the coal mine goaf according to the embodiments of the present application.

[0087] As Figure 10 shown, the measurement device 10 for the subsidence basin boundary in the coal mine goaf includes: an acquisition module 100, a construction module 200, a processing module 300, a generation module 400, and a measurement module 500.

[0088] Specifically, the acquisition module 100 is configured to acquire synthetic aperture radar (SAR) image data covering the subsidence basin in the coal mine goaf and preprocess the SAR image data to generate a registered SAR image data set;

[0089] The construction module 200 is configured to establish a first multi-master image interference phase data set based on the SAR image data set to obtain a differential interference map corresponding to the first multi-master image interference phase data set, and reconstruct the mining process according to the differential interference map;

[0090] The processing module 300 is configured to stack the differential interference maps to obtain an average rate map of the surface deformation affected by mining, so as to delineate the range of the surface deformation affected by mining according to the average rate map;

[0091] A generation module 400 is configured to generate a single - master - image differential - interferometric phase dataset and a second multi - master - image interferometric phase dataset based on a SAR image dataset, and establish a coupled - image interferometric phase dataset based on the single - master - image differential - interferometric phase dataset and the second multi - master - image interferometric phase dataset, so as to analyze the coupled - image interferometric phase dataset to obtain time - series deformation data of the boundary of the goaf subsidence basin in a coal mine.

[0092] A measurement module 500 is configured to obtain a measurement result of the boundary of the goaf subsidence basin in a coal mine by combining the mining history, the range of surface deformation affected by mining, and the time - series deformation data.

[0093] Optionally, in an embodiment of the present application, the construction module 200 includes: a first processing unit, a first generation unit, and a reconstruction unit.

[0094] Among them, the first processing unit is configured to process the first multi - master - image interferometric phase dataset to obtain an average coherence map of the first multi - master - image interferometric phase dataset.

[0095] The first generation unit is configured to determine at least one unwrapping reference point that meets a preset condition based on the average coherence map, so as to perform phase unwrapping on the interferogram according to the at least one unwrapping reference point to obtain a differential - interferometric fringe map.

[0096] The reconstruction unit is configured to reconstruct the mining history according to the center position of the differential - interferometric fringe map.

[0097] Optionally, in an embodiment of the present application, the first multi - master - image interferometric phase dataset is (n - 4)×7 - 1 pairs of interferograms generated from n SAR images, where n is a positive integer.

[0098] Optionally, in an embodiment of the present application, the processing module 300 includes: a second processing unit, a calculation unit, and a second generation unit.

[0099] Among them, the second processing unit is configured to perform stacking processing on the differential - interferogram to obtain a first differential - interferogram set, and estimate and remove the quadratic phase change rate from the first differential - interferogram set to obtain a second differential - interferogram set.

[0100] The calculation unit is configured to calculate the phase change rate, the standard deviation of the phase change rate, and the standard deviation of the residual phase of each pixel point in the second differential - interferogram set, and perform linear regression on the atmospheric components related to elevation based on the phase change rate, the standard deviation of the phase change rate, and the standard deviation of the residual phase of each pixel point to obtain an atmospheric phase delay, and filter out the atmospheric phase delay using a large window to obtain a target differential - interferogram set.

[0101] A second generating unit, configured to generate a phase unwrapping mask map based on the intensity and coherence of pixel points in the target differential interferogram set, and determine target measurement points based on the mask map, so as to obtain an average rate map of surface deformation affected by mining based on the target measurement points.

[0102] Optionally, in an embodiment of the present application, the expression of the average rate map of surface deformation affected by mining is:

[0103]

[0104] Wherein, V mean is the average rate of surface deformation, φ i is the unwrapped phase of N interferograms, and ΔT i is the time interval.

[0105] Optionally, in an embodiment of the present application, the generating module 400 includes: a first constructing unit, a second constructing unit, and a third processing unit.

[0106] The first constructing unit is configured to select permanent scatterers based on the SAR image data set, and select a master image according to the spatio-temporal baseline principle, so as to combine the permanent scatterers and the master image to establish a single-master-image differential interferometric phase data set;

[0107] The second constructing unit is configured to calculate a baseline file according to the SAR image data set, and establish a second multi-master-image interferometric phase data set based on the baseline file by using the short baseline principle and the multi-master-image principle;

[0108] The third processing unit is configured to perform compatibility processing on the single-master-image differential interferometric phase data set and the second multi-master-image interferometric phase data set, and convert each pixel value in the single-master-image differential interferometric phase data set and the second multi-master-image interferometric phase data set into a corresponding complex value, so as to superimpose the complex values at corresponding positions to obtain a coupled image interferometric phase data set.

[0109] Optionally, in an embodiment of the present application, the second multi-master-image interferometric phase data set is (n - 2)×3(n - 2)*3 pairs of interferograms generated from n SAR images, where n is a positive integer.

[0110] It should be noted that the foregoing explanation of the embodiments of the measurement method for the boundary of the coal mine goaf subsidence basin is also applicable to the measurement device for the boundary of the coal mine goaf subsidence basin in this embodiment, and will not be elaborated here.

[0111] The measuring device for the boundary of the subsidence basin in the coal mine goaf according to the embodiment of the present application can acquire synthetic aperture radar (SAR) image data of the boundary of the subsidence basin in the coal mine goaf, generate a registered SAR image data set, and then establish a first multi-master image interference phase data set. According to the center position of the differential interference fringe pattern, the mining process is inverted, and the differential interference maps are stacked to obtain an average rate map of the ground surface deformation affected by mining, so as to delineate the range of the ground surface deformation affected by mining. Thus, a single-master image differential interference phase data set and a second multi-master image interference phase data set can be generated to establish a coupled image interference phase data set. By performing interference processing and time series inversion analysis on the coherent target points with stable scattering characteristics, the deformation data of the boundary of the subsidence basin in the coal mine goaf can be obtained, which can effectively improve the density and spatial coverage rate of monitoring points, contribute to obtaining the time series deformation data of the boundary of the subsidence basin in the coal mine goaf, and achieve millimeter-level high-precision measurement. Therefore, in the related art, due to the possible influence of factors such as spatio-temporal decorrelation, atmospheric delay, and DEM error on DInSAR, the accuracy of the surface deformation monitoring in the mining area is limited. And because it is difficult for PS-InSAR and SBAS-InSAR to obtain a sufficient number of monitoring point targets, it may not be possible to achieve high-precision measurement of the boundary of the subsidence basin in the coal mine goaf and other problems are solved.

[0112] Figure 11 The structural schematic diagram of the electronic device provided by the embodiment of the present application. The electronic device may include:

[0113] A memory 1101, a processor 1102, and a computer program stored on the memory 1101 and executable on the processor 1102.

[0114] When the processor 1102 executes the program, it implements the measuring method for the boundary of the subsidence basin in the coal mine goaf provided in the above embodiment.

[0115] Further, the electronic device further includes:

[0116] A communication interface 1103 for communication between the memory 1101 and the processor 1102.

[0117] The memory 1101 is used to store a computer program executable on the processor 1102.

[0118] The memory 1101 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0119] If the memory 1101, the processor 1102, and the communication interface 1103 are implemented independently, the communication interface 1103, the memory 1101, and the processor 1102 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 11 only a thick line is used in

[0120] to represent it, but it does not mean that there is only one bus or one type of bus. Optionally, in a specific implementation, if the memory 1101, the processor 1102, and the communication interface 1103 are integrated on a single chip, the memory 1101, the processor 1102, and the communication interface 1103 can communicate with each other through an internal interface.

[0121] The processor 1102 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0122] The embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method for measuring the boundary of the goaf subsidence basin as described above is implemented.

[0123] The embodiments of the present application further provide a computer program product, the computer program of which can run computer instructions, and when the computer instructions are executed by a processor, the method for measuring the boundary of the goaf subsidence basin as described above is implemented.

[0124] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0125] In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0126] Any process or method description, whether in a flowchart or described in some other way, can be understood to represent a module, segment, or part of code including one or N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the technical field to which the embodiments of this application belong.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0128] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0129] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0130] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, may exist separately physically for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0131] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for measuring the boundary of a subsidence basin in a coal mine goaf, characterized in that Including the following steps: Obtain synthetic aperture radar (SAR) image data covering the subsidence basin of the coal mine goaf, and preprocess the SAR image data to generate a registered SAR image data set; Based on the SAR image data set, establish a first multi-master image interferometric phase data set to obtain a differential interferogram corresponding to the first multi-master image interferometric phase data set, and reconstruct the mining process according to the differential interferogram; Perform stacking processing on the differential interferogram to obtain an average rate map of surface deformation affected by mining, so as to delineate the range of surface deformation affected by mining according to the average rate map; Generate a single-master image differential interferometric phase data set and a second multi-master image interferometric phase data set based on the SAR image data set, and establish a coupled image interferometric phase data set based on the single-master image differential interferometric phase data set and the second multi-master image interferometric phase data set, so as to analyze the coupled image interferometric phase data set to obtain time-series deformation data of the boundary of the coal mine goaf subsidence basin; Combine the mining process, the range of surface deformation affected by mining, and the time-series deformation data to obtain the measurement result of the boundary of the coal mine goaf subsidence basin.

2. The method according to claim 1, wherein The reconstructing the mining process according to the differential interferogram includes: Process the first multi-master image interferometric phase data set to obtain an average coherence map of the first multi-master image interferometric phase data set; Determine at least one unwrapping reference point that meets a preset condition based on the average coherence map, and perform phase unwrapping on the interferogram according to the at least one unwrapping reference point to obtain a differential interference fringe map; Reconstruct the mining process according to the center position of the differential interference fringe map.

3. The method according to claim 2, wherein The first multi-master image interferometric phase data set is (n - 4)×7 - 1 pairs of interferograms generated from n SAR images, where n is a positive integer greater than 4.

4. The method according to claim 1, wherein The performing stacking processing on the differential interferogram to obtain an average rate map of surface deformation affected by mining includes: Perform stacking processing on the differential interferogram to obtain a first differential interferogram set, and estimate and remove the quadratic phase change rate from the first differential interferogram set to obtain a second differential interferogram set; Calculate the phase change rate, the standard deviation of the phase change rate, and the standard deviation of the residual phase of each pixel point in the second differential interferogram set, and perform linear regression on the atmospheric components related to elevation based on the phase change rate, the standard deviation of the phase change rate, and the standard deviation of the residual phase of each pixel point to obtain the atmospheric phase delay, and use a large window to filter out the atmospheric phase delay to obtain a target differential interferogram set; Generate a mask map for phase unwrapping based on the intensity and coherence of the pixel points in the target differential interferogram set, and determine target measurement points based on the mask map, so as to obtain the average rate map of surface deformation affected by mining based on the target measurement points.

5. The method according to claim 4, wherein The expression of the average rate map of surface deformation affected by mining is: Among them, V mean is the average rate of surface deformation, φ i is the unwrapped phase of N interferograms, and ΔT i is the time interval.

6. The method according to claim 1, wherein The establishing a coupled image interferometric phase data set based on the single-master image differential interferometric phase data set and the second multi-master image interferometric phase data set includes: Select permanent scatterers based on the SAR image dataset, and select a master image according to the spatio-temporal baseline principle, so as to combine the permanent scatterers and the master image to establish the single-master-image differential interferometric phase dataset; Calculate a baseline file based on the SAR image dataset, and based on the baseline file, use the short baseline principle and the multi-master-image principle to establish the second multi-master-image interferometric phase dataset; Perform compatibility processing on the single-master-image differential interferometric phase dataset and the second multi-master-image interferometric phase dataset, and convert each pixel value in the single-master-image differential interferometric phase dataset and the second multi-master-image interferometric phase dataset into a corresponding complex value, so as to superimpose the complex values at the corresponding positions to obtain the coupled image interferometric phase dataset.

7. The method according to claim 6, wherein The second multi-master-image interferometric phase dataset is (n - 2)×3(n - 2)*3 interferograms generated from n SAR images, where n is a positive integer greater than 4.

8. A measuring device for the boundary of a subsidence basin in a coal mine goaf, characterized in that Comprising: An acquisition module, configured to acquire synthetic aperture radar (SAR) image data covering a coal mine goaf subsidence basin, and preprocess the SAR image data to generate a registered SAR image dataset; A construction module, configured to establish a first multi-master-image interferometric phase dataset based on the SAR image dataset to obtain a differential interferogram corresponding to the first multi-master-image interferometric phase dataset, and reconstruct the mining process according to the differential interferogram; A processing module, configured to perform stacking processing on the differential interferogram to obtain an average rate map of surface deformation affected by mining, so as to delineate the range of surface deformation affected by mining according to the average rate map; A generation module, configured to generate a single-master-image differential interferometric phase dataset and a second multi-master-image interferometric phase dataset based on the SAR image dataset, and establish a coupled image interferometric phase dataset based on the single-master-image differential interferometric phase dataset and the second multi-master-image interferometric phase dataset, so as to analyze the coupled image interferometric phase dataset to obtain time-series deformation data of the boundary of the coal mine goaf subsidence basin; A measurement module, configured to combine the mining process, the range of surface deformation affected by mining, and the time-series deformation data to obtain a measurement result of the boundary of the coal mine goaf subsidence basin.

9. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the method for measuring the boundary of a coal mine goaf subsidence basin according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to be used for implementing the method for measuring the boundary of a coal mine goaf subsidence basin according to any one of claims 1-7.

11. A computer program product, comprising a computer program, characterized in that, The computer program is executed to be used for implementing the method for measuring the boundary of a coal mine goaf subsidence basin according to any one of claims 1-7.

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