Coal mining subsidence area identification and classification method based on multi-source data

Abstract

The invention relates to the technical field of mining area subsidence condition data processing, in particular to a coal mining subsidence area identification and classification method based on multi-source data, which comprises the following specific steps: S1, subsidence area range identification: acquiring changes of a coal mining subsidence area range by means of a multi-source data monitoring and analysis technology, S2, comparison data acquisition: according to the newest coal mining subsidence area range, acquiring the change of the coal mining subsidence area range; s3, data contrastive analysis: superposing the data to analyze the subsidence depth change condition of the coal mining subsidence area, mutually fusing and verifying the data to ensure accuracy, monitoring land surface by InSAR and airborne LiDAR, measuring underwater by an unmanned ship, and mutually compensating technologies to ensure omnibearing and high efficiency; satellite monitoring can realize large-scale and large-range monitoring, a macroscopic decision is provided for natural resource management, and low-altitude unmanned aerial vehicle aerial survey, unmanned surveying ships and field check provide refined monitoring for natural resource related functional rooms.

Description

Technical Field

[0001] The invention relates to the technical field of mining area subsidence data processing, and in particular to a coal mining subsidence area identification and classification method based on multi-source data. Background Art

[0002] Mining subsidence refers to the phenomenon that underground mineral resource mining causes rock movement and surface subsidence. After underground mineral resources are mined, the original mechanical equilibrium state of the rock mass around the mining area is destroyed, the rock layer moves and deforms, and spreads to the surface. Houses, buildings, railways, etc. located within the mining area will be deformed or damaged, threatening the lives and property of local residents. Therefore, it is necessary to monitor mining subsidence in mining areas. Summary of the invention

[0003] The purpose of the present invention is to provide a method for identifying and classifying coal mining subsidence areas based on multi-source data, so as to solve the problem of inconvenience in identifying coal mining subsidence areas mentioned in the above background technology. To achieve the above purpose, the present invention provides the following technical solution: A method for identifying and classifying coal mining subsidence areas based on multi-source data, comprising the following specific steps:

[0004] S1: Identification of subsidence area scope: Relying on multi-source data monitoring and analysis technology, the changes in the scope of coal mining subsidence areas are obtained. Areas with cumulative settlement changes exceeding 200 mm in the coal mining subsidence area are listed as expansion areas, and the changes in the area and distribution of coal mining subsidence areas are analyzed through the trend of range changes.

[0005] S2: Comparative data collection: Based on the latest scope of the coal mining subsidence area, the drone-mounted LiDAR technology and unmanned survey ship were used to measure the latest high-precision DEM data of the coal mining subsidence area above and below the water.

[0006] S3: Data comparison and analysis: The latest DEM data is superimposed with the historical DEM data to analyze the changes in the subsidence depth of the coal mining subsidence area, and the difference between the two DEM periods is calculated using the formula. The coal mining subsidence area is divided into three categories: mild, moderate and deep, and their distribution is analyzed. The classification of coal mining subsidence areas provides data support for the selection of treatment plans for coal mining subsidence areas, making up for the deficiency that InSAR monitoring cannot obtain the classification of subsidence in some water areas.

[0007] Preferably, the multi-source data technology specifically utilizes SBAS-InSAR technology, drone-mounted LiDAR, unmanned survey ships and other historical data of subsidence areas, integrates multi-source data, identifies the scope of subsidence areas and realizes subsidence area classification.

[0008] Preferably, the technical method flow of the multi-source data monitoring and analysis technology for extracting the scope of coal mine subsidence is as follows: a. Research on the extraction of surface subsidence due to mining using SBAS-InSAR technology; b. Visual interpretation of surface deformation in mining areas combined with optical images; c. Mapping of coal mining subsidence patches.

[0009] Preferably, the mining surface subsidence extraction research adopts the professional software SARscape to obtain the surface deformation of the subsidence area by SBAS-InSAR processing, and then calculates the cumulative surface deformation and the maximum deformation of the subsidence patch within the research time range.

[0010] Preferably, the surface deformation of the mining area is combined with visual interpretation of optical images, surface deformation maps and corresponding high-resolution optical images, as well as surface cumulative deformation and settlement patches, and then displayed in spatial superposition. The surface settlement caused by coal mining in the study area is visually interpreted through human-computer interaction, and the settlement edge range line is outlined and represented by polygons.

[0011] Preferably, the mapping of coal mining subsidence maps is to extract the coal mining subsidence range maps, namely, extract the coal mining subsidence range maps, and the spatial distribution maps, the mining rights range maps, and the coal mining working face maps by fitting the mining surface settlement extraction and the mining area surface deformation with the optical image visual interpretation.

[0012] Preferably, the accuracy verification of the InSAR monitoring results is mainly carried out by internal coincidence verification and external coincidence verification.

[0013] Preferably, the internal conformity test is performed by selecting typical monitoring points, analyzing the deformation information of the monitoring point time series, and using a calculation formula to calculate whether the regression analysis fitting residual of the typical point is normally distributed with zero mean; the external conformity verification requires external leveling measurement data for analysis.

[0014] Preferably, the calculation formula is Hi=DEMi(current)-DEMi(historical), wherein Hi is the subsidence value of point i, DEMi(current) is the elevation of the latest DEM data of point i, and DEMi(historical) is the elevation of the historical DEM data of point i.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] In the present invention, data are integrated and verified with each other to ensure accuracy, InSAR and airborne LiDAR monitor the land surface, unmanned ships measure underwater, and the technologies complement each other to ensure all-round efficiency. Satellite monitoring can monitor on a large scale and over a large range, providing macro-decision-making for natural resource management. Low-altitude UAV aerial surveys, unmanned survey ships and field verification provide refined monitoring for natural resource-related functional departments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A research roadmap for monitoring the scope and depth of coal mining subsidence areas in the present invention;

[0018] Figure 2 It is a flow chart of the method for identifying sinkhole areas of multi-source data of the present invention;

[0019] Figure 3 This is a schematic diagram of the Sentinel-1A data covering northern Anhui covered by the present invention;

[0020] Figure 4 This is the Sentinel-1A data list for 2019-2022 of the present invention;

[0021] Figure 5 This is the cumulative deformation time series diagram of the subsidence area of ​​the present invention (Huainan mining area);

[0022] Figure 6 This is the cumulative deformation time series diagram of the subsidence area of ​​the present invention (Huaibei mining area);

[0023] Figure 7 This is a schematic diagram of optical remote sensing images of mining subsidence in Dingji Coal Mine in Huainan according to the present invention;

[0024] Figure 8 The InSAR technology of the present invention is used to obtain the surface subsidence result map;

[0025] Fig. 9 It is the level monitoring point of Xinhu Coal Mine of the present invention;

[0026] Fig.10 It is the residual and normal distribution diagram of Z57, Z58 and Z59 of the present invention;

[0027] Fig.11 This is the result diagram of the level monitoring of Z18, Z22 and Z28 of the present invention;

[0028] Fig.12 This is a comparison diagram of InSAR and level monitoring of the present invention;

[0029] Fig.13 Comparison diagram of the InSAR and level cumulative settlement monitoring results of the present invention (Z18, Z19, Z22);

[0030] Fig.14 Comparison diagram of InSAR and level cumulative settlement monitoring results of the present invention (Z24, Z28, Z59);

[0031] Fig.15 This is the classification of the subsidence depth of the coal mining subsidence area in the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without creative work are within the scope of protection of the present invention.

[0033] See also Figures 1 to 15 The present invention provides a technical solution: a method for identifying and classifying coal mining subsidence areas based on multi-source data, comprising the following specific steps:

[0034] S1: Identification of subsidence area scope: Relying on multi-source data monitoring and analysis technology, the changes in the scope of coal mining subsidence areas are obtained. Areas with cumulative settlement changes exceeding 200 mm in the coal mining subsidence area are listed as expansion areas, and the changes in the area and distribution of coal mining subsidence areas are analyzed through the trend of range changes.

[0035] S2: Comparative data collection: Based on the latest scope of the coal mining subsidence area, the drone-mounted LiDAR technology and unmanned survey ship were used to measure the latest high-precision DEM data of the coal mining subsidence area above and below the water.

[0036] S3: Data comparison and analysis: The latest DEM data is superimposed with the historical DEM data to analyze the changes in the subsidence depth of the coal mining subsidence area, and the difference between the two DEM periods is calculated using the formula. The coal mining subsidence area is divided into three categories: mild, moderate and deep, and their distribution is analyzed. The classification of coal mining subsidence areas provides data support for the selection of governance plans for coal mining subsidence areas.

[0037] Embodiment 1: According to the distribution of coal mining subsidence areas determined in Anhui Province in 2019, SAR remote sensing images are selected. Figure 3 This is a schematic diagram of the Sentinel-1A data coverage of the six cities in northern Anhui. The red area is the subsidence area, the yellow quadrilateral is the location of the concentrated subsidence area, and the two orange and blue quadrilaterals are Sentinel-1A data, Path142, Frame101, Path142, Frame96. A total of 90 shots were taken from January 1, 2020 to December 31, 2022. Figure 4 It is a list of the times when the SAR data of the monitoring area was taken.

[0038] a. Research on the extraction of surface subsidence due to mining: The professional software SARscape is used to obtain the monthly surface deformation of the subsidence area from 2019 to 2022 mainly by SBAS-InSAR processing, and then the cumulative surface deformation and the maximum deformation of the subsidence map within the research time range are calculated. Figure 5 and 6 This is the cumulative deformation diagram for three years.

[0039] b. Surface deformation in mining areas combined with visual interpretation of optical images: With coal mining, the ground moves above the goaf, and the range of movement is the surface moving basin. A surface moving basin will only be formed when the mining area reaches a certain threshold. Usually, when the length and width of the goaf exceed 0.2 to 0.3 times the average mining depth, the ground begins to move. As the area of ​​the goaf continues to expand, the area and maximum subsidence value of the moving basin also continue to increase. At this time, there will be a maximum subsidence point in the center of the subsidence basin, and the entire basin will be in the shape of a "bowl", which is called a non-fully mined moving basin. Usually, when the length and width of the goaf are greater than 1.4 times the mining depth, there will be an extreme subsidence value under this condition. When the maximum subsidence value in the moving basin is equal to this value, it will no longer increase with the expansion of the goaf area. At this time, there will be more than one maximum subsidence point in the center of the basin. At this time, the moving basin is in the shape of a "bowl" with a relatively flat bottom, which is called a fully mined moving basin. If Figure 7 As shown in the figure, the Dingji coal mine in Huainan formed a waterlogged area with the large-scale surface subsidence. From the image and underwater terrain, it can be known that the entire subsidence area is in the shape of a "basin". Common optical images mainly include: panchromatic, visible light and multispectral data. The spatial texture characteristics of the research object can be obtained through the high spatial resolution of panchromatic and visible light images. Different types of objects have different spectral curves due to the differences in the characteristics of the objects themselves in receiving and radiating electromagnetic waves. According to the spectral characteristics of pixels, optical remote sensing image classification belongs to the category of pixel-level classification and discrimination. Compared with other methods, it can clearly see the actual topography, vegetation coverage, type, and surface changes of the objects. Therefore, combined with the surface deformation map and the corresponding high-resolution optical image map and the surface settlement pattern characteristics "bowl shape" and "basin shape" caused by coal mining, the surface settlement caused by coal mining in the study area is visually interpreted through human-computer interaction, and the settlement edge range line is outlined, which is represented by polygons. Coal mine sedimentation translation should be carried out with computer as the main working platform, combined with the background data of the mining area, based on multi-data fusion and human-computer interaction. Figure 8 .

[0040] c. Draw the coal mining subsidence map: extract the coal mining subsidence range map by fitting the coal mining subsidence range and spatial distribution map, mining rights range map, and coal mining working surface map obtained through the above process.

[0041] d. The 11-phase ground level monitoring data of the Z18-Z29 and Z57-Z59 level points of Xinhu Coal Mine from May 25, 2021 to November 30, 2021 were selected as the data for external compliance verification. Among them, Z18-Z20 is located outside the coal mining subsidence area delineated in 2022; Z21-Z22 is located at the boundary of the subsidence area; Z23-Z29 is located inside the coal mining subsidence area; Z57-Z59 is located in the center of the coal mining subsidence area. See the schematic diagram of the specific points for details. Fig. 9 .

[0042] e. Internal consistency test: This project uses the InSAR time series results of three typical points, Z57, Z58, and Z59, to perform internal consistency accuracy test. Regression analysis is performed on the time series results of the three typical points, Z57, Z58, and Z59, and the residuals are calculated and statistically analyzed. The results are as follows: Fig.10 As shown in the figure. The blue points on the figure are the actual InSAR observation values, and the red line is the regression line and the fitted normal distribution curve. Fig.10 It can be clearly seen that the residual distribution is approximately a normal distribution with a mean of 0 and a variance of 0.6. The difference between the InSAR monitoring results and the regression curve is mainly concentrated near 0, and the range is mainly concentrated between -1cm and 1cm. According to statistics, the proportion of differences falling in the interval (-1cm, 1cm) is 85.71%. This result proves the reliability of the InSAR monitoring results.

[0043] f. External compliance verification: According to the existing data and the imaging time of SAR images in 2021, 11 phases of ground level monitoring data of leveling points Z18-Z29 and Z57-Z59 from May 25, 2021 to November 30, 2021 were selected. In order to better analyze the results and verify the accuracy, the data were processed as follows:

[0044] (1) Taking May 25, 2023 as the benchmark, subtract the elevation results of May 25 from the elevation results of other dates to obtain the time series cumulative settlement value;

[0045] (2) Considering that the elevation values ​​of the leveling points change approximately linearly in each monitoring period, the missing values ​​in the leveling point monitoring results are supplemented by piecewise linear interpolation;

[0046] (3) Although the 11-period monitoring time of the leveling measurement includes the monitoring time of the InSAR interferometer, the monitoring time periods of the two are not completely consistent. To solve this problem, considering that the subsidence changes approximately linearly in each monitoring time period, the InSAR time series results are linearly interpolated to solve the inconsistency between the leveling monitoring period and the InSAR monitoring period. Fig.11 The 11 phases of monitoring data for the Z18, Z22 and Z28 leveling points are given.

[0047] The InSAR annual cumulative settlement values ​​at each leveling point were compared and analyzed with the leveling results. Fig.12 As shown in the figure, the blue lines are the annual cumulative settlement monitored by InSAR and the annual cumulative settlement monitored by leveling, and the red line is the absolute difference between the two.

[0048] Depend on Fig.12 It can be seen that the absolute value difference of the annual cumulative settlement values ​​at each leveling point of the two is concentrated in the interval of [0cm, 4cm], all less than 5cm. The SBAS-InSAR monitoring results are highly consistent with the leveling monitoring results. That is, if the leveling monitoring results are used as the true ground settlement values, SBAS-InSAR can more accurately reflect the ground subsidence position in the mining area.

[0049] For further quantitative comparative analysis, the InSAR and leveling time series results of the Z18, Z19, Z22, Z24, Z28, and Z59 leveling points were selected for analysis. Fig.13 and Fig.14 As shown, the blue is the time series cumulative settlement measured by leveling, the red is the time series cumulative settlement monitored by InSAR, and the bar graph is the absolute error between InSAR and leveling measurements at the corresponding time points.

[0050] Depend on Fig.13 and Fig.14 It can be seen that when the settlement value (absolute value) is less than 10 cm, the settlement value monitored by SBAS-InSAR and the settlement value monitored by leveling have strong consistency over time. However, when the settlement value (absolute value) is greater than 10 cm, the settlement value monitored by SBAS-InSAR is less than the settlement value monitored by leveling, but the two still have the same trend. The average difference between the SBAS-InSAR and leveling monitoring results is 1.1 cm, the standard deviation is 1.49 cm, and the trend of the SBAS-InSAR monitoring results is consistent with that of the leveling monitoring results, indicating that the InSAR results are well consistent with the leveling monitoring results, proving that the InSAR results are accurate and reliable.

[0051] g. The current 2015 digital elevation model collected from the Anhui Basic Surveying and Mapping Geographic Information Center is used as the base period DEM. The DEM of the non-waterlogged area of ​​the subsidence area is obtained by airborne LIDAR, and the underwater DEM data of the waterlogged area is obtained by unmanned boats and related underwater topographic measurement methods. The high-precision topographic data of the entire subsidence area is obtained comprehensively, and the current situation is 2022, which is used as the current period DEM.

[0052] h. Calculate the difference between the two DEMs according to the formula and divide the coal mining subsidence area into light subsidence area, moderate subsidence area and deep subsidence area. See the division example Fig.15 .

[0053] Mild subsidence area: Coal mining subsidence area with subsidence value <500mm. There is basically no water accumulation or a small amount of water accumulation in the subsidence area. Some surfaces have large slopes and water and soil are easily lost.

[0054] Moderate subsidence area: Coal mining subsidence area with subsidence value of 500mm≤<1500mm. The subsidence area shows seasonal water accumulation and partially loses its original land function.

[0055] Deep subsidence area: Coal mining subsidence area with subsidence value ≥1500mm. The subsidence area is waterlogged all year round and almost all of the original land functions have been lost.

[0056] i. Classification and results of subsidence areas based on subsidence depth: For the high-precision DEM data obtained in 2022, a total of 1,573 detection points were measured, with a gross error rate of 1.8%, a maximum elevation accuracy difference of 0.24M, a mean error of 0.09M, and the accuracy met the requirements.

[0057] The above shows and describes the basic principles, main features and advantages of the present invention. Technical personnel in this industry should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for identifying and classifying coal mining subsidence areas based on multi-source data, characterized in that: The specific steps include: S1: Identification of subsidence area scope: Relying on multi-source data monitoring and analysis technology, the changes in the scope of coal mining subsidence areas are obtained. Areas with cumulative settlement changes exceeding 200 mm in the coal mining subsidence area are listed as expansion areas, and the changes in the area and distribution of coal mining subsidence areas are analyzed through the trend of range changes. S2: Comparative data collection: Based on the latest scope of coal mining subsidence area, the UAV-mounted LiDAR technology and unmanned survey ship were used to measure the latest high-precision DEM data of the coal mining subsidence area above and below the water. S3: Data comparison and analysis: The latest DEM data is superimposed with the historical DEM data to analyze the changes in the subsidence depth of the coal mining subsidence area, and the difference between the two DEM periods is calculated using the formula. The coal mining subsidence area is divided into three categories: mild, moderate and deep, and their distribution is analyzed. The classification of coal mining subsidence areas provides data support for the selection of treatment plans for coal mining subsidence areas, making up for the deficiency that InSAR monitoring cannot obtain the classification of subsidence in some water areas.

2. The method for identifying and classifying coal mining subsidence areas based on multi-source data according to claim 1 is characterized by: The multi-source data technology specifically utilizes SBAS-InSAR technology, UAV-mounted LiDAR, unmanned survey ships and other historical data of subsidence areas, integrates multi-source data, identifies the scope of subsidence areas and realizes subsidence area classification.

3. The method for identifying and classifying coal mining subsidence areas based on multi-source data according to claim 2 is characterized in that: The technical method flow of the multi-source data monitoring and analysis technology for extracting the scope of coal mine subsidence is as follows: a. Research on the extraction of surface subsidence in mining using SBAS-InSAR technology; b. Visual interpretation of surface deformation in mining areas combined with optical images; c. Mapping of coal mining subsidence patches.

4. The method for identifying and classifying coal mining subsidence areas based on multi-source data according to claim 3 is characterized by: The research on extracting surface subsidence due to mining adopts the professional software SARscape to obtain the surface deformation of the subsidence area by SBAS-InSAR processing, and then calculates the cumulative surface deformation and the maximum deformation of the subsidence map within the research time range.

5. The method for identifying and classifying coal mining subsidence areas based on multi-source data according to claim 3 is characterized in that: The surface deformation of the mining area is combined with the visual interpretation of optical images, the surface deformation map and the corresponding high-resolution optical image map, as well as the surface cumulative deformation and settlement patches, and then displayed in spatial superposition. The surface settlement caused by coal mining in the study area is visually interpreted through human-computer interaction, and the settlement edge range line is outlined and represented by a polygon.

6. The method for identifying and classifying coal mining subsidence areas based on multi-source data according to claim 3 is characterized by: The coal mining subsidence map described above is to extract the coal mining subsidence range map by fitting the mining surface settlement extraction and mining area surface deformation with optical image visual interpretation of the coal mining subsidence range and spatial distribution map, mining rights range map, and coal mining working face map.

7. The method for identifying and classifying coal mining subsidence areas based on multi-source data according to claim 3 is characterized by: The accuracy verification of the InSAR monitoring results is mainly carried out by internal coincidence verification and external coincidence verification.

8. The method for identifying and classifying coal mining subsidence areas based on multi-source data according to claim 7 is characterized in that: The internal coincidence test is performed by selecting a typical monitoring point, analyzing the deformation information of the monitoring point time series, and using a calculation formula to calculate whether the regression analysis fitting residual of the typical point is normally distributed with zero mean; External compliance verification requires external leveling measurement data for analysis.

9. The method for identifying and classifying coal mining subsidence areas based on multi-source data according to claim 1 is characterized in that: The calculation formula is Hi=DEMi(current)-DEMi(historical), where Hi is the subsidence value of point i, DEMi(current) is the elevation of the latest DEM data of point i, and DEMi(historical) is the elevation of the historical DEM data of point i.

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