# Influence of Sliding Structure on Water Flowing Fracture Zone and Surface Subsidence in the Antidip of Overlying Strata Area

Authors: Changsheng Song; Shuang Gong
Journal: Geofluids
 (2022)

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## Abstract

Sliding structure refers to the process and result of rock deformation caused by the continuous downward sliding and cutting of the upper stratum under the influence of gravity. In the process of tectonic formation, the strata go through two times of sliding to the south and then sliding to the north after the over thrusting and overstretching, so that the strata of the overthrusting and underthrusting strata are opposite to the inclination of the overthrusting and underthrusting strata, forming the anticlinal tectonic zone. Nowadays, under the conventional geological mining conditions, the underwater mining technology and surface subsidence laws are quite mature, but the fracture zone and surface movement characteristics of the sliding structure and the antioverturning mining are still rarely studied. This paper analyzes the dam damage in the mining process and discusses the three-dimensional reconstruction of laser scanning and the acquisition method of settlement information. The three-dimensional laser scanning technology is compared with the traditional monitoring technology. The deformation characteristics of surface structures above 25091 working face are revealed. The 3D laser point cloud shows that the uneven sinking of the dam is obvious and is associated with the uneven damage of the dam mass. The actual observation at the site shows that the uneven settlement of the dam on the surface was noticeable during the 25091 working face retraction. The damage on the east side of the dam was more serious and was accompanied by the generation of cracks running east-west. The DEM model of part of the surface and dam above the 25091 working face was obtained by 3D laser scanning technology. The model was analyzed to obtain information on surface subsidence. The study reveals that the uneven subsidence condition of the dam is apparent, with the overall characteristics of large subsidence on the east side of the dam and small subsidence on the west side of the dam. The displacement of the maximum surface subsidence point reflects the special phenomenon of overburden fracture and surface movement under the influence of overburden reverse inclination.


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## Body

## 1. Introduction

At present, China’s economic development has entered a new normal, and it is extremely urgent to realize the goal of comprehensive scientific mining [1]. But because the geological mining conditions of coal mine are very complex, mining difficulty is increasing, facing all kinds of mining disasters, so China’s coal mine safety problems are still severe; mining technology does not completely adapt to the complex geological mining conditions which is an important reason for China’s coal mine safety situation.Sliding structure is formed by the hanging wall rock strata sliding from high to low along the inclined slip surface under the action of gravity [2]. Studies show that there are a lot of coal resources under this geological structure [3, 4]. For example, a large amount of coal exists in the sliding structure of the western coal field in Henan Province [5]. Under the action of complex crustal movement, the coal was uplifted by the fault block incline in Cenozoic and the secondary faults in the fault block were pushed to the depth to form sliding structures, which resulted in the loss of strata within a range and made sliding structures become the symbol of coal occurrence. Lu Dian sliding structure area is a typical case of sliding structure in western Henan. Under the complex crustal movement, the early overlying system slid from north to south and later slid from south to north, resulting in the special geological structure of overlying rock dip in the upper wall of sliding body and coal seam reverse dip, that is, overlying rock reverse dip [6].The law of overburden movement is closely related to specific mining problems, such as large quantities of coal mining, rock strata movement, and destruction; the development of falling zone and fracture zone becomes the aquifer water break into the underground channel, affecting the mine safety production. And if mining affects the surface, it will often cause the surface movement and deformation, destroy the ecological environment of the mining area on which people live, and cause serious damage to the surface buildings. In the study of rock movement, the key is to determine the height relationship between the aquifer and the conductive fracture zone. The investigation of surface movement deformation law can improve coal mine safety production and lay a solid foundation for scientific mining.The earliest study on sliding structures in China was in the late 1960s, when the existence of gravity sliding structures was confirmed in the Song Shan area of Henan Province, which was called “epidermal structures” at that time [7, 8]. One of the obvious characteristics of geological structure in western Henan coalfield is the extensive development of gravity sliding structure. Since the 1970s, the study of gravity sliding structures in China has made great progress in theory. Scientific research personnel carry out the Henan Song Shan Si Nian system such as five researches of gravity gliding tectonic of Foshan city [5, 9, 10]. On the basis of the above research, the theoretical research of sliding tectonics in China is developing continuously.3D laser scanning technology is different from traditional measurement technology in that it can fully realize the horizontal to vertical data collection, multistation data through computer splicing, and can get large range, multiangle three-dimensional space data. Nowadays, it has been widely applied in water conservancy engineering [11], digital transportation [12], digital medicine [13], digital pipeline and city [14, 15], digital archaeology [16], and other aspects, showing the broad application background and huge economic value of this technology. As an advanced and emerging measurement technology, 3D laser scanning has broken through the shortcomings of traditional measurement technology. For objects within the laser range, high precision discrete point cloud data can be obtained at high speed, and the required 3D model can be established quickly by computer processing.With the continuous development of 3D laser scanning technology, its equipment and software are constantly developing and upgrading, and its accuracy is also constantly improving. In recent years, domestic scholars have done a lot of research on its application in the field of surface deformation monitoring and achieved fruitful results. For example, Chunnian [17] organically combined UAV surveying and mapping technology with 3D laser scanning technology to overcome the shortcoming of limited field angle of view of scanning technology. The conclusion proved that the above two technologies could obtain high-precision DEM and OEM data by combining their respective technical advantages. On the basis of 3D laser scanning technology, Wenjuan [18] takes a certain mine as an example and introduces Kriging algorithm into MATLAB software to establish a digital elevation model of surface subsidence basin in the mining area. The comparison between monitoring results and measured values shows that the detection accuracy reaches millimeter level, which can be used for high-precision analysis of mining subsidence. Wenbing et al. [19] analyzed the accuracy of 3D laser scanning technology in monitoring mining subsidence and verified that its accuracy could be applied to the observation of coal mine surface subsidence, which has a good prospect of popularization and application.The application of 3D laser scanning and other “live replication technology” to the observation of surface movement deformation is a far-reaching technical revolution for the traditional measurement and research in the field of surface movement deformation. For the external data collection work, it is not necessary to set up fixed stations, and the fully automatic 3D stereo scanning can use and store a large number of measurement points containing details in a specific data format; in the internal data processing stage, the point cloud data obtained by scanning can be processed in a more solid process using the point cloud postprocessing software to obtain the surface model of the surface entity, which simplifies and clarifies the processing process and reduces the manual labor of personnel, and the large amount of data obtained can bring convenience to the actual production and related research work.In this paper, 3D laser scanning technology is used as the core with RTK to observe the surface above the mining area and the North Ditch Dam, and the DEM of the sinkhole area and the dam is obtained after internal processing, and a large amount of surface information is obtained. It was compared with the traditional level survey to analyze the feasibility of 3D laser scanning technology in monitoring the mine area. Finally, the subsidence information was extracted to obtain the moving deformation of the ground surface and the dam for an in-depth analysis.In this paper, 25091 working face of Gongcheng coal mine is taken as the main research object, combined with geological and mining conditions of Gongcheng coal mine; GMS three-dimensional geological model is used to obtain the occurrence characteristics of sliding structure [20]. Through theoretical analysis, numerical simulation, and field measurement, the influence of sliding structure on overburden fracture zone and surface movement law in the process of overburden movement is studied, which provides scientific basis for mining design and is of great significance for improving underground safety production.


## 2. Dam Damage during Mining

### 2.1. Overview of the Monitoring Area

As shown in Figure1, the area scanned by applying 3D laser scanning technology is located above the 25091 working face within the 25 mining area in the north of the Gongcheng coal mine, and the main monitoring object is the Beigou dam and the Beigou reservoir near the top of the 25091 working face, which cannot be extended again due to the complex topography of the site and the excessive surrounding vegetation. The North Ditch Dam in the scanning area is located about 53~58 m south of the 25091 working face. Figure 2 shows the actual view of the North Ditch Dam. It can be seen that the surface elevation around the dam is undulating, and there are more trees and vegetation, which are the main factors affecting the actual measurement error.Figure 1
The location of the dam and 25091 working face.Figure 2
The panorama of dam.


### 2.2. Damage to the Dam during Downhole Mining

The first phase of data collection for the monitoring area in the study was carried out on April 17, 2018, when the working face advanced 38 m; the second phase of data collection was carried out on June 17, 2018, when the working face advanced 172 m; and the third phase of data collection was carried out on October 11, 2018, when the working face advanced 427 m.According to the requirements, in addition to the regular monitoring with the application of 3D laser scanning technology, the damage of ground cracks and dams in the monitoring area was investigated during each observation conducted. The damage situation is shown in Figure3.Figure 3
The situation on the east side of the dam. (a) Dam damage in the first phase; (b) and (c) dam damage in the second phase; (d–f) dam damage in the third phase.
(a)(b)(c)(d)(e)(f)


## 2.1. Overview of the Monitoring Area

As shown in Figure1, the area scanned by applying 3D laser scanning technology is located above the 25091 working face within the 25 mining area in the north of the Gongcheng coal mine, and the main monitoring object is the Beigou dam and the Beigou reservoir near the top of the 25091 working face, which cannot be extended again due to the complex topography of the site and the excessive surrounding vegetation. The North Ditch Dam in the scanning area is located about 53~58 m south of the 25091 working face. Figure 2 shows the actual view of the North Ditch Dam. It can be seen that the surface elevation around the dam is undulating, and there are more trees and vegetation, which are the main factors affecting the actual measurement error.Figure 1
The location of the dam and 25091 working face.Figure 2
The panorama of dam.


## 2.2. Damage to the Dam during Downhole Mining

The first phase of data collection for the monitoring area in the study was carried out on April 17, 2018, when the working face advanced 38 m; the second phase of data collection was carried out on June 17, 2018, when the working face advanced 172 m; and the third phase of data collection was carried out on October 11, 2018, when the working face advanced 427 m.According to the requirements, in addition to the regular monitoring with the application of 3D laser scanning technology, the damage of ground cracks and dams in the monitoring area was investigated during each observation conducted. The damage situation is shown in Figure3.Figure 3
The situation on the east side of the dam. (a) Dam damage in the first phase; (b) and (c) dam damage in the second phase; (d–f) dam damage in the third phase.
(a)(b)(c)(d)(e)(f)


## 3. Acquisition of Point Cloud Data

### 3.1. Field Data Acquisition Process

Based on the specific topographic conditions, the process of external data acquisition conducted in this study is as follows: (a) exploration of the actual area and preliminary formulation of station locations and the best measurement plan based on the exploration results. (b) Based on the actual terrain, the scanning route is designed. Determine the location of the stations based on the defined scan route and the exploration conclusion. It is necessary to ensure that there is overlap in the field of view between the stations and that there are feature points that can be used for stitching. Based on the scanning range and accuracy requirements, the scanning parameters for each station of the 3D laser scanner are calculated. (c) Use RTK for mine joint surveys. Solve for conversion parameters based on existing control point data and verify that the parameters are converted accurately. (d) The 3D laser scanner is set up at the location of the station designed in advance. Around the station, four to five reflective targets are evenly placed. Make sure the position of the reflective targets are far, near, high, and low, and then use the preset scanning mode to collect the station cloud data. Use the automatic target search function to accurately scan the reflective targets. (e) Remove the reflection target on the base, and put it on the RTK receiver. The spatial geodetic coordinates of the point are measured, and the target center coordinates are obtained by parameter correction. (f) Preliminary analysis of the field data is performed to determine the accuracy of the data. (g) When one station scanning is completed, the next station scanning is carried out according to the preset route. The steps of (d)~(f) are repeated until the topographic survey of the whole sunken basin area is completed.The function of finding reflective sheets needs to be turned on before data scanning, and each station needs to enter the scanning reflective sheet interface to turn on finding after scanning, so that the information of moving targets can be saved to the memory chip in the scanner.


### 3.2. Control Measurement Setting and Station Setup

The coordinates of the scanner need to be marked after the scanning plan is determined by the preliminary survey, which can be regarded as the control coordinates in the point cloud data processing and generally need to use a high-precision total station or GPS to get the accurate control point coordinates. According to the determined alignment or orientation, a certain number of targets are selected as the control targets of the scanned area, which are used to convert the point clouds scanned several times into a control coordinate system with the targets as the core. In this process, the scanner finely scans the control target to obtain its accurate geometric coordinates, which are the engineering coordinates within the survey station. The engineering coordinate system in the scanner is converted to the required geodetic coordinate system by calculating the conversion parameters between the geodetic coordinate system and the engineering coordinate system in the station in the internal processing. When there are few feature objects in the measurement area, the target can be added as the splicing point. From the consideration of the later data scanning process, the number of targets scanned between different stations is not less than three, and more than four is appropriate, and the two stations should be kept in view (see Figure4).Figure 4
Field observation.When using the ground 3D laser scanner to obtain the surface information of the sinkhole area, the dam body needs to be monitored before and after the mining of the working face to obtain the surface point cloud information in different periods. The range and quality of the collected point clouds are the determining factors for the accuracy and completeness of the surface subsidence information. For example, if the monitoring area includes a high terrain hillside, several stations need to overlap in the effective range in order to facilitate the subsequent data processing and obtain the point cloud data with high quality and density. Data acquisition should also determine the scanning parameters according to the scanning range, accuracy, and required point cloud density.According to the regional topography of the site dam and the scanner into the view, the first phase of scanning designed three-dimensional laser scanner observation station location distribution as shown in Figure5. ScanPos003 and ScanPos007 build station location has a relatively high platform. The 3D laser scanner can cover a large area of the dam. It can have more overlapping parts with other build sites. Therefore, ScanPos003 and ScanPos007 use the fine scan mode. The scanning parameters were set to a laser emission frequency of 600 KHz and a point density of 500,000 points per 0.02°. The scanning angle range is 0° to 360° for horizontal scanning (surface scanning). Vertical scanning (line scan) is -40°~+60°. The areas that could not be scanned in the early stage were supplemented by other sites; for example, ScanPos002 supplemented the dam road surface and the area on the south side of the dam that was not scanned to provide data support, and its scanning parameters were set to a laser emission frequency of 600 KHz, a point density of 500,000 points emitted every 0.04°, and a scanning angle range of 0° to 360° for horizontal scanning (surface scanning) and -40° to +60° for vertical scanning (line scanning).Figure 5
The distribution of station.


### 3.3. Data Scanning

Taking the RIGEL VZ-400i used in this study as an example, the instrument was first set up at the corresponding survey site, the scanner power was connected, and the scanner was roughly aligned and leveled. In the self-alignment function within the instrument, when the instrument is turned on, the scanner will default to rotate counterclockwise from the power-on position to get the scanner’s own attitude and automatically record the attitude parameters. Set the parameters that need to be determined before scanning according to the scanning range, etc., and perform panoramic scanning with the instrument. When the scanning is finished, the data quality and the scanned data range are observed to meet the needs, and when everything is satisfied, the scanning operation can be carried out at the next station. In order to facilitate the subsequent stitching at the software level, it is necessary to ensure that there is a certain overlapping area of the scanned area of the station as much as possible when scanning.The first phase of data collection in the study was conducted on April 17, 2018, at which time the working face advanced 38 m and the mining of the coal body in the working face had not yet been able to affect the surface dam; the second phase of data collection was conducted on June 17, 2018, at which time the working face advanced 172 m and the mining of the coal body in the working face had begun to affect the surface, and the site investigation revealed uneven damage to the dam, with less damage on the west side of the dam and the dam East side of the wall at the fracture, the connection between the dam road and the lower slope of the dam exit smaller cracks; On October 11, 2018, for the third phase of data collection, at this time, the working face advanced 427 m, the mining of the coal body within the working face has increased the impact on the surface, the fracture at the east side of the dam wall increased, the connection between the dam road and the lower slope of the dam exit cracks increased, and the lower slope of the dam exit two new east-west direction cracks with a crack width of 2~4 cm, as shown in Figure6.Figure 6
Site location.
(a)
Scanning station build-up on 4/17/18(b)
Scanning station build-up on 6/17/18


### 3.4. Internal Processing of Scanned Data

After the acquisition of external data, a large amount of point cloud data needs to be preprocessed to a certain extent; mainly, because after scanning the object or surface with the 3D laser scanner, due to the different operating environments and the occasional errors of their own instruments, the scanned point cloud has a large number of nonresearch object noise, in order to improve the accuracy of the measured object as much as possible, it is necessary to identify the noise from the algorithm and software level. In order to improve the accuracy of the measured objects, the noise must be identified and removed from the algorithm and software level. In addition, after the 3D laser scanner scans each site, the point cloud data depend on the engineering coordinate system of the instrument itself. For the purpose of the study, it is necessary to convert the engineering coordinate system, on which all the point cloud data are based, to the geodetic coordinate system. At the same time, the scanned point cloud data contains a large amount of useless point data, which needs to be aligned and compressed. Therefore, the internal processing of the point cloud mainly includes four parts: alignment, dryness reduction, classification, and compression of the point cloud data.In the internal processing stage, this paper mainly relies on the conversion parameters between the engineering coordinate system and the geodetic coordinate system in the RISCAN PRO measurement instrument and applies rotation and translation to align the coordinate system where the point cloud data are located. In the dryness reduction stage, this paper adopts a method based on the statistics of local spatial distribution of the point cloud to filter the noise points. After the noise reduction, a filtering algorithm is introduced and the ground points are separated from the vegetation by the “Terrain filter” function in RISCAN PRO to realize the classification of the point cloud. Finally, the point interval threshold is set to 0.1 m, and the point cloud in the survey area is diluted to reduce the problem of large amount of data redundancy in the area, and finally, the internal processing of the point cloud is completed.


## 3.1. Field Data Acquisition Process

Based on the specific topographic conditions, the process of external data acquisition conducted in this study is as follows: (a) exploration of the actual area and preliminary formulation of station locations and the best measurement plan based on the exploration results. (b) Based on the actual terrain, the scanning route is designed. Determine the location of the stations based on the defined scan route and the exploration conclusion. It is necessary to ensure that there is overlap in the field of view between the stations and that there are feature points that can be used for stitching. Based on the scanning range and accuracy requirements, the scanning parameters for each station of the 3D laser scanner are calculated. (c) Use RTK for mine joint surveys. Solve for conversion parameters based on existing control point data and verify that the parameters are converted accurately. (d) The 3D laser scanner is set up at the location of the station designed in advance. Around the station, four to five reflective targets are evenly placed. Make sure the position of the reflective targets are far, near, high, and low, and then use the preset scanning mode to collect the station cloud data. Use the automatic target search function to accurately scan the reflective targets. (e) Remove the reflection target on the base, and put it on the RTK receiver. The spatial geodetic coordinates of the point are measured, and the target center coordinates are obtained by parameter correction. (f) Preliminary analysis of the field data is performed to determine the accuracy of the data. (g) When one station scanning is completed, the next station scanning is carried out according to the preset route. The steps of (d)~(f) are repeated until the topographic survey of the whole sunken basin area is completed.The function of finding reflective sheets needs to be turned on before data scanning, and each station needs to enter the scanning reflective sheet interface to turn on finding after scanning, so that the information of moving targets can be saved to the memory chip in the scanner.


## 3.2. Control Measurement Setting and Station Setup

The coordinates of the scanner need to be marked after the scanning plan is determined by the preliminary survey, which can be regarded as the control coordinates in the point cloud data processing and generally need to use a high-precision total station or GPS to get the accurate control point coordinates. According to the determined alignment or orientation, a certain number of targets are selected as the control targets of the scanned area, which are used to convert the point clouds scanned several times into a control coordinate system with the targets as the core. In this process, the scanner finely scans the control target to obtain its accurate geometric coordinates, which are the engineering coordinates within the survey station. The engineering coordinate system in the scanner is converted to the required geodetic coordinate system by calculating the conversion parameters between the geodetic coordinate system and the engineering coordinate system in the station in the internal processing. When there are few feature objects in the measurement area, the target can be added as the splicing point. From the consideration of the later data scanning process, the number of targets scanned between different stations is not less than three, and more than four is appropriate, and the two stations should be kept in view (see Figure4).Figure 4
Field observation.When using the ground 3D laser scanner to obtain the surface information of the sinkhole area, the dam body needs to be monitored before and after the mining of the working face to obtain the surface point cloud information in different periods. The range and quality of the collected point clouds are the determining factors for the accuracy and completeness of the surface subsidence information. For example, if the monitoring area includes a high terrain hillside, several stations need to overlap in the effective range in order to facilitate the subsequent data processing and obtain the point cloud data with high quality and density. Data acquisition should also determine the scanning parameters according to the scanning range, accuracy, and required point cloud density.According to the regional topography of the site dam and the scanner into the view, the first phase of scanning designed three-dimensional laser scanner observation station location distribution as shown in Figure5. ScanPos003 and ScanPos007 build station location has a relatively high platform. The 3D laser scanner can cover a large area of the dam. It can have more overlapping parts with other build sites. Therefore, ScanPos003 and ScanPos007 use the fine scan mode. The scanning parameters were set to a laser emission frequency of 600 KHz and a point density of 500,000 points per 0.02°. The scanning angle range is 0° to 360° for horizontal scanning (surface scanning). Vertical scanning (line scan) is -40°~+60°. The areas that could not be scanned in the early stage were supplemented by other sites; for example, ScanPos002 supplemented the dam road surface and the area on the south side of the dam that was not scanned to provide data support, and its scanning parameters were set to a laser emission frequency of 600 KHz, a point density of 500,000 points emitted every 0.04°, and a scanning angle range of 0° to 360° for horizontal scanning (surface scanning) and -40° to +60° for vertical scanning (line scanning).Figure 5
The distribution of station.


## 3.3. Data Scanning

Taking the RIGEL VZ-400i used in this study as an example, the instrument was first set up at the corresponding survey site, the scanner power was connected, and the scanner was roughly aligned and leveled. In the self-alignment function within the instrument, when the instrument is turned on, the scanner will default to rotate counterclockwise from the power-on position to get the scanner’s own attitude and automatically record the attitude parameters. Set the parameters that need to be determined before scanning according to the scanning range, etc., and perform panoramic scanning with the instrument. When the scanning is finished, the data quality and the scanned data range are observed to meet the needs, and when everything is satisfied, the scanning operation can be carried out at the next station. In order to facilitate the subsequent stitching at the software level, it is necessary to ensure that there is a certain overlapping area of the scanned area of the station as much as possible when scanning.The first phase of data collection in the study was conducted on April 17, 2018, at which time the working face advanced 38 m and the mining of the coal body in the working face had not yet been able to affect the surface dam; the second phase of data collection was conducted on June 17, 2018, at which time the working face advanced 172 m and the mining of the coal body in the working face had begun to affect the surface, and the site investigation revealed uneven damage to the dam, with less damage on the west side of the dam and the dam East side of the wall at the fracture, the connection between the dam road and the lower slope of the dam exit smaller cracks; On October 11, 2018, for the third phase of data collection, at this time, the working face advanced 427 m, the mining of the coal body within the working face has increased the impact on the surface, the fracture at the east side of the dam wall increased, the connection between the dam road and the lower slope of the dam exit cracks increased, and the lower slope of the dam exit two new east-west direction cracks with a crack width of 2~4 cm, as shown in Figure6.Figure 6
Site location.
(a)
Scanning station build-up on 4/17/18(b)
Scanning station build-up on 6/17/18


## 3.4. Internal Processing of Scanned Data

After the acquisition of external data, a large amount of point cloud data needs to be preprocessed to a certain extent; mainly, because after scanning the object or surface with the 3D laser scanner, due to the different operating environments and the occasional errors of their own instruments, the scanned point cloud has a large number of nonresearch object noise, in order to improve the accuracy of the measured object as much as possible, it is necessary to identify the noise from the algorithm and software level. In order to improve the accuracy of the measured objects, the noise must be identified and removed from the algorithm and software level. In addition, after the 3D laser scanner scans each site, the point cloud data depend on the engineering coordinate system of the instrument itself. For the purpose of the study, it is necessary to convert the engineering coordinate system, on which all the point cloud data are based, to the geodetic coordinate system. At the same time, the scanned point cloud data contains a large amount of useless point data, which needs to be aligned and compressed. Therefore, the internal processing of the point cloud mainly includes four parts: alignment, dryness reduction, classification, and compression of the point cloud data.In the internal processing stage, this paper mainly relies on the conversion parameters between the engineering coordinate system and the geodetic coordinate system in the RISCAN PRO measurement instrument and applies rotation and translation to align the coordinate system where the point cloud data are located. In the dryness reduction stage, this paper adopts a method based on the statistics of local spatial distribution of the point cloud to filter the noise points. After the noise reduction, a filtering algorithm is introduced and the ground points are separated from the vegetation by the “Terrain filter” function in RISCAN PRO to realize the classification of the point cloud. Finally, the point interval threshold is set to 0.1 m, and the point cloud in the survey area is diluted to reduce the problem of large amount of data redundancy in the area, and finally, the internal processing of the point cloud is completed.


## 4. 3D Reconstruction of Laser Scanning and Sinkhole Information Acquisition

### 4.1. Constructing DEM Model Based on Regular Grid

The method of constructing DEM model based on regular grid is very common. It is by taking discrete point cloud data, calculating the grid node elevation value according to certain interpolation algorithm, and saving it into the corresponding matrix, the commonly used regular grid is square grid, mostly using two-dimensional matrix for storage. It is relatively simple to use regular grid DEM for terrain analysis, but the elevation value of each grid node is obtained by interpolation of proximity points, which is an approximation of the actual terrain, and there will be some accuracy loss; if the grid interval is set too large, the deviation will be larger for areas with large surface undulations, and some key terrain features, such as ridges and peaks, will be lost. Since the acquired terrain data is discrete, it is necessary to find the elevation value at any point or node in the grid according to the elevation value of adjacent reference points when constructing the regular DEM, and the calculation process is DEM interpolation. Commonly used interpolation algorithms for regular grids are as follows: radial basis function interpolation, kriging interpolation, and inverse distance weighted interpolation.The inverse distance weighted interpolation (IDW) method is used to construct a regular DEM using the ground point cloud data separated by preprocessing, and the steps are as follows:(1)
Construct the grid. First traverse the preprocessed ground point cloud data, detect its extreme values, in theX and Y directions, and useXmaxandXminas the basis for the construction of the regularm×ngrid, whose side length is denoted by D. The calculation m×n method is shown in equations (2)-(5), and the parameter D is rotated according to the point cloud density, so that the grid contains enough points(1)m=INTXmax−XminD+1,n=INTYmax−YminD+1(2)
In grid node elevation value calculation, first constructKd‐tree; let there be a nadir point in thenneighborhood of the grid node whose plane coordinates arexi,yiandzithe elevation value isi=1,2,⋯,n; then use the inverse distance weighting functionx,yto calculate thefx,yelevation value of each node as(2)fx,y=∑j=1nzj/djp/∑j=1n1/djpx,y≠xi,yii=1,2,⋯,n,zix,y≠xi,yii=1,2,⋯,n,wheredj=x−xj2+y−yj2j=1,2,⋯,nis the horizontal distance fromxi,yithe discrete point to the point setx,y.pis the weighted power exponentp>0, which can be used to adjust the surface shape of the function; the larger it is, the flatter the surface is, and vice versa, the sharper it is
(3)
Construct a regular grid. Connect each grid node in turn after interpolation, and the model is the digital elevation model of the terrain surface


### 4.2. Constructing DEM with Irregular Triangular Network

There are three ways to construct an irregular triangulation network using discrete point cloud data acquired by a 3D laser scanner, which is usually based on surface projection, surface-based profiling, and body-based profiling. Among them, the surface-based projection method is to project the scattered point cloud data onto the corresponding two-dimensional plane, construct the triangulation network on the plane, and then map it to the three-dimensional space to realize the construction of spatial scattered point cloud data network, which has a lower error rate compared with other construction methods. It can show more terrain details in the DEM formed by an irregular triangulation network, and its high networking efficiency makes it possible to mostly use surface projection for networking in point cloud data processing.


### 4.3. DEM Construction within the Survey Area

The terrain surface models constructed based on irregular triangulation and regular grid methods can both effectively express the continuous changes of terrain, and both are widely used, with their own advantages and disadvantages in different usage environments, which are analyzed as follows.Irregular triangulated mesh is constructed based on the original point cloud data and is able to construct arbitrary surfaces in the form of triangulated mesh. Due to the complexity of surface topography, irregular triangulated mesh can better express the specific features of the surface. The model constructed by this method has high accuracy and can avoid data duplication analysis in the process of regular mesh construction, and the triangular mesh data can be analyzed topologically, which is more convenient. However, because the irregular triangular mesh covers more data, including the topological relationship of each triangle and triangular mesh vertex information, the higher accuracy makes its memory occupation higher.The regular mesh can only be used when the terrain is simple, and the specification mesh needs to get additional data points based on the original point cloud data to get the regular mesh in the generation process due to the irregularity of the point cloud data. The accuracy is low, but the data structure is simple and convenient for computer processing, and can be efficient for spatial analysis. However, under the conditions of low data accuracy and complex terrain, the regular grid cannot show the detailed features of the terrain, and its applicability is poor.In this paper, the surface Delaunay triangulation network is constructed by the plane projection method. Firstly, the best plane of the region is fitted by the least squares algorithm using the dedried and aligned point cloud data; the point cloud data is projected onto the plane by the surface projection method, and the irregular triangulation network is constructed by the Delaunay algorithm in two dimensions; finally, the plane triangulation network is mapped to the three-dimensional space; i.e., the irregular triangulation network of the surface is realized. The irregular triangular network of point cloud data is constructed. The obtained DEMs are shown in Figure7.Figure 7
DEM model diagram of phase I to phase IV.
(a)(b)(c)(d)


### 4.4. Acquisition of Surface Subsidence Information

The damage to the ground surface caused by underground mining is extremely serious, and the most important thing in the study of subsidence law is to obtain the subsidence of the ground surface in different periods. There are many methods to extract the surface subsidence values after collecting point cloud data of subsidence area in different periods using a ground 3D laser scanner, which can be divided into point-to-point direct comparison, point-to-local model comparison, point-to-whole model comparison, and model-to-model comparison.


#### 4.4.1. Point-To-Point Direct Comparison

The point-to-point direct comparison method is mainly done by comparing the elevation values of points in several periods of point cloud data at the same location. The process uses the Hausdorff distance algorithm to calculate the nearest distance between each point inPthe pointS′setSandP′ the adjacent points in the point set, which is calculated as follows:
(3)dP,S′=minP−P′2.The direct point-to-point comparison method is the simplest and most efficient way to compare point clouds. The closest distance between two points is obtained by making a difference between the elevation values of several periods of point cloud data at the same location. This method requires high density of the point cloud. When the density of the point cloud is low, especially when there is a cavity at the position of the reference point, the proximity point searched for will be too different from the actual reference point, which makes the accuracy lower; when the density of the point cloud is high, the proximity point near the reference point is not too different from the actual reference point position elevation value, and the accuracy is higher.


#### 4.4.2. Comparison between Point Cloud and Local Model

The comparison between point cloud and local model is applicable when there are voids at the reference point location or when the point cloud density at the reference point location is low. The DEM is constructed by Delaunay triangular network, least squares fitting plane, or quadratic height function, to supplement the location of the void or large point cloud spacing, and the elevation value at the reference point can be known more accurately by the algorithm. At this time, the comparison of the initial point cloud and the local model can effectively reduce the accuracy error caused by voids and other reasons and further improve the monitoring level.


#### 4.4.3. Point cloud and overall model comparison

When the point cloud data quality is good and the point cloud density is large, the comparison between the point cloud and the overall model can efficiently analyze the movement and deformation between the point clouds. The specific principle is to use the reference dataQ1 to build a high-precision surface model and calculate the normal vector of each area model. By finding the normal distance from any point in Q2 the point cloud data to be compared to the d model surface and saving d the deviation value in the corresponding point cloud data structure, we can obtain the change of the point cloud in the whole area. This method is suitable for areas with complex terrain, and the point cloud scanning range also needs to cover the whole measurement area as much as possible. Q1,Q2, respectively, is a collection of point cloud data from two phases before mining and during mining, and its process can be summarized as follows:
(1)
DepositQ1,Q2, into the data, respectively; construct the irregular triangular network (TIN) of the surface before miningQ1using the point-by-point insertion method, and deposit each triangle in the network shape into the Delaunay chain table(2)
For the TIN constructed in the previous step, the elevation value of any point is calculated byxi,yi the bilinear interpolation method, and the corresponding point set is obtained Q1′xi,yi,zi(3)
UsingQ2−Q1′, we can get the surface subsidence basin data set corresponding to the advancing position of the working surfaceQΔxi,yi,Δzi(4)
Using theQΔxi,yi,Δzi point set, the surface subsidence basin DEM can be established in the mining process


#### 4.4.4. Comparison between Models

When several periods of point clouds have been processed better and DEM models are obtained, the overall difference between models can be made. The elevations at the samex,y coordinate values are obtained by the same difference algorithm, and the elevation values at their grid points are subtracted to obtain the change information there. By expanding the range of points to the point cloud and differencing the position of each node in the DEM, we can get the overall regional change information, which is the surface subsidence basin.In model differencing, the difference is positive for uplift and negative for subsidence. By regrinding the point cloud data, the point cloud sparse places or hollow places are filled, and the error of point-to-point comparison is reduced when calculating the difference value, and the calculation is very efficient. It is more adaptable and efficient in locations with flat terrain, little vegetation, and good quality of scanned point cloud density, and the accuracy error of comparison results is minimized. However, for areas with particularly complex terrain, smooth areas are generated in the DEM formation, which increases the error in terms of terrain such as gullies and ravines and makes the obtained results have high deviation from the real values.The main object of this study lies in the flat terrain near the dam above the 25091 working face, and the dam also has good stability, and the quality of the point cloud data is influenced by the vegetation. The point cloud data quality is affected by the vegetation. The point cloud density is poor because the instrument cannot sweep to the surface, and some areas form hollows. At the dam body, since there is no vegetation on the dam road and the point cloud density is high and the point cloud quality is good, the acquisition of the surface subsidence data here is mainly done by point-to-point comparison, point-to-local model comparison, and point-to-model comparison.


## 4.1. Constructing DEM Model Based on Regular Grid

The method of constructing DEM model based on regular grid is very common. It is by taking discrete point cloud data, calculating the grid node elevation value according to certain interpolation algorithm, and saving it into the corresponding matrix, the commonly used regular grid is square grid, mostly using two-dimensional matrix for storage. It is relatively simple to use regular grid DEM for terrain analysis, but the elevation value of each grid node is obtained by interpolation of proximity points, which is an approximation of the actual terrain, and there will be some accuracy loss; if the grid interval is set too large, the deviation will be larger for areas with large surface undulations, and some key terrain features, such as ridges and peaks, will be lost. Since the acquired terrain data is discrete, it is necessary to find the elevation value at any point or node in the grid according to the elevation value of adjacent reference points when constructing the regular DEM, and the calculation process is DEM interpolation. Commonly used interpolation algorithms for regular grids are as follows: radial basis function interpolation, kriging interpolation, and inverse distance weighted interpolation.The inverse distance weighted interpolation (IDW) method is used to construct a regular DEM using the ground point cloud data separated by preprocessing, and the steps are as follows:(1)
Construct the grid. First traverse the preprocessed ground point cloud data, detect its extreme values, in theX and Y directions, and useXmaxandXminas the basis for the construction of the regularm×ngrid, whose side length is denoted by D. The calculation m×n method is shown in equations (2)-(5), and the parameter D is rotated according to the point cloud density, so that the grid contains enough points(1)m=INTXmax−XminD+1,n=INTYmax−YminD+1(2)
In grid node elevation value calculation, first constructKd‐tree; let there be a nadir point in thenneighborhood of the grid node whose plane coordinates arexi,yiandzithe elevation value isi=1,2,⋯,n; then use the inverse distance weighting functionx,yto calculate thefx,yelevation value of each node as(2)fx,y=∑j=1nzj/djp/∑j=1n1/djpx,y≠xi,yii=1,2,⋯,n,zix,y≠xi,yii=1,2,⋯,n,wheredj=x−xj2+y−yj2j=1,2,⋯,nis the horizontal distance fromxi,yithe discrete point to the point setx,y.pis the weighted power exponentp>0, which can be used to adjust the surface shape of the function; the larger it is, the flatter the surface is, and vice versa, the sharper it is
(3)
Construct a regular grid. Connect each grid node in turn after interpolation, and the model is the digital elevation model of the terrain surface


## 4.2. Constructing DEM with Irregular Triangular Network

There are three ways to construct an irregular triangulation network using discrete point cloud data acquired by a 3D laser scanner, which is usually based on surface projection, surface-based profiling, and body-based profiling. Among them, the surface-based projection method is to project the scattered point cloud data onto the corresponding two-dimensional plane, construct the triangulation network on the plane, and then map it to the three-dimensional space to realize the construction of spatial scattered point cloud data network, which has a lower error rate compared with other construction methods. It can show more terrain details in the DEM formed by an irregular triangulation network, and its high networking efficiency makes it possible to mostly use surface projection for networking in point cloud data processing.


## 4.3. DEM Construction within the Survey Area

The terrain surface models constructed based on irregular triangulation and regular grid methods can both effectively express the continuous changes of terrain, and both are widely used, with their own advantages and disadvantages in different usage environments, which are analyzed as follows.Irregular triangulated mesh is constructed based on the original point cloud data and is able to construct arbitrary surfaces in the form of triangulated mesh. Due to the complexity of surface topography, irregular triangulated mesh can better express the specific features of the surface. The model constructed by this method has high accuracy and can avoid data duplication analysis in the process of regular mesh construction, and the triangular mesh data can be analyzed topologically, which is more convenient. However, because the irregular triangular mesh covers more data, including the topological relationship of each triangle and triangular mesh vertex information, the higher accuracy makes its memory occupation higher.The regular mesh can only be used when the terrain is simple, and the specification mesh needs to get additional data points based on the original point cloud data to get the regular mesh in the generation process due to the irregularity of the point cloud data. The accuracy is low, but the data structure is simple and convenient for computer processing, and can be efficient for spatial analysis. However, under the conditions of low data accuracy and complex terrain, the regular grid cannot show the detailed features of the terrain, and its applicability is poor.In this paper, the surface Delaunay triangulation network is constructed by the plane projection method. Firstly, the best plane of the region is fitted by the least squares algorithm using the dedried and aligned point cloud data; the point cloud data is projected onto the plane by the surface projection method, and the irregular triangulation network is constructed by the Delaunay algorithm in two dimensions; finally, the plane triangulation network is mapped to the three-dimensional space; i.e., the irregular triangulation network of the surface is realized. The irregular triangular network of point cloud data is constructed. The obtained DEMs are shown in Figure7.Figure 7
DEM model diagram of phase I to phase IV.
(a)(b)(c)(d)


## 4.4. Acquisition of Surface Subsidence Information

The damage to the ground surface caused by underground mining is extremely serious, and the most important thing in the study of subsidence law is to obtain the subsidence of the ground surface in different periods. There are many methods to extract the surface subsidence values after collecting point cloud data of subsidence area in different periods using a ground 3D laser scanner, which can be divided into point-to-point direct comparison, point-to-local model comparison, point-to-whole model comparison, and model-to-model comparison.


### 4.4.1. Point-To-Point Direct Comparison

The point-to-point direct comparison method is mainly done by comparing the elevation values of points in several periods of point cloud data at the same location. The process uses the Hausdorff distance algorithm to calculate the nearest distance between each point inPthe pointS′setSandP′ the adjacent points in the point set, which is calculated as follows:
(3)dP,S′=minP−P′2.The direct point-to-point comparison method is the simplest and most efficient way to compare point clouds. The closest distance between two points is obtained by making a difference between the elevation values of several periods of point cloud data at the same location. This method requires high density of the point cloud. When the density of the point cloud is low, especially when there is a cavity at the position of the reference point, the proximity point searched for will be too different from the actual reference point, which makes the accuracy lower; when the density of the point cloud is high, the proximity point near the reference point is not too different from the actual reference point position elevation value, and the accuracy is higher.


### 4.4.2. Comparison between Point Cloud and Local Model

The comparison between point cloud and local model is applicable when there are voids at the reference point location or when the point cloud density at the reference point location is low. The DEM is constructed by Delaunay triangular network, least squares fitting plane, or quadratic height function, to supplement the location of the void or large point cloud spacing, and the elevation value at the reference point can be known more accurately by the algorithm. At this time, the comparison of the initial point cloud and the local model can effectively reduce the accuracy error caused by voids and other reasons and further improve the monitoring level.


### 4.4.3. Point cloud and overall model comparison

When the point cloud data quality is good and the point cloud density is large, the comparison between the point cloud and the overall model can efficiently analyze the movement and deformation between the point clouds. The specific principle is to use the reference dataQ1 to build a high-precision surface model and calculate the normal vector of each area model. By finding the normal distance from any point in Q2 the point cloud data to be compared to the d model surface and saving d the deviation value in the corresponding point cloud data structure, we can obtain the change of the point cloud in the whole area. This method is suitable for areas with complex terrain, and the point cloud scanning range also needs to cover the whole measurement area as much as possible. Q1,Q2, respectively, is a collection of point cloud data from two phases before mining and during mining, and its process can be summarized as follows:
(1)
DepositQ1,Q2, into the data, respectively; construct the irregular triangular network (TIN) of the surface before miningQ1using the point-by-point insertion method, and deposit each triangle in the network shape into the Delaunay chain table(2)
For the TIN constructed in the previous step, the elevation value of any point is calculated byxi,yi the bilinear interpolation method, and the corresponding point set is obtained Q1′xi,yi,zi(3)
UsingQ2−Q1′, we can get the surface subsidence basin data set corresponding to the advancing position of the working surfaceQΔxi,yi,Δzi(4)
Using theQΔxi,yi,Δzi point set, the surface subsidence basin DEM can be established in the mining process


### 4.4.4. Comparison between Models

When several periods of point clouds have been processed better and DEM models are obtained, the overall difference between models can be made. The elevations at the samex,y coordinate values are obtained by the same difference algorithm, and the elevation values at their grid points are subtracted to obtain the change information there. By expanding the range of points to the point cloud and differencing the position of each node in the DEM, we can get the overall regional change information, which is the surface subsidence basin.In model differencing, the difference is positive for uplift and negative for subsidence. By regrinding the point cloud data, the point cloud sparse places or hollow places are filled, and the error of point-to-point comparison is reduced when calculating the difference value, and the calculation is very efficient. It is more adaptable and efficient in locations with flat terrain, little vegetation, and good quality of scanned point cloud density, and the accuracy error of comparison results is minimized. However, for areas with particularly complex terrain, smooth areas are generated in the DEM formation, which increases the error in terms of terrain such as gullies and ravines and makes the obtained results have high deviation from the real values.The main object of this study lies in the flat terrain near the dam above the 25091 working face, and the dam also has good stability, and the quality of the point cloud data is influenced by the vegetation. The point cloud data quality is affected by the vegetation. The point cloud density is poor because the instrument cannot sweep to the surface, and some areas form hollows. At the dam body, since there is no vegetation on the dam road and the point cloud density is high and the point cloud quality is good, the acquisition of the surface subsidence data here is mainly done by point-to-point comparison, point-to-local model comparison, and point-to-model comparison.


## 4.4.1. Point-To-Point Direct Comparison

The point-to-point direct comparison method is mainly done by comparing the elevation values of points in several periods of point cloud data at the same location. The process uses the Hausdorff distance algorithm to calculate the nearest distance between each point inPthe pointS′setSandP′ the adjacent points in the point set, which is calculated as follows:
(3)dP,S′=minP−P′2.The direct point-to-point comparison method is the simplest and most efficient way to compare point clouds. The closest distance between two points is obtained by making a difference between the elevation values of several periods of point cloud data at the same location. This method requires high density of the point cloud. When the density of the point cloud is low, especially when there is a cavity at the position of the reference point, the proximity point searched for will be too different from the actual reference point, which makes the accuracy lower; when the density of the point cloud is high, the proximity point near the reference point is not too different from the actual reference point position elevation value, and the accuracy is higher.


## 4.4.2. Comparison between Point Cloud and Local Model

The comparison between point cloud and local model is applicable when there are voids at the reference point location or when the point cloud density at the reference point location is low. The DEM is constructed by Delaunay triangular network, least squares fitting plane, or quadratic height function, to supplement the location of the void or large point cloud spacing, and the elevation value at the reference point can be known more accurately by the algorithm. At this time, the comparison of the initial point cloud and the local model can effectively reduce the accuracy error caused by voids and other reasons and further improve the monitoring level.


## 4.4.3. Point cloud and overall model comparison

When the point cloud data quality is good and the point cloud density is large, the comparison between the point cloud and the overall model can efficiently analyze the movement and deformation between the point clouds. The specific principle is to use the reference dataQ1 to build a high-precision surface model and calculate the normal vector of each area model. By finding the normal distance from any point in Q2 the point cloud data to be compared to the d model surface and saving d the deviation value in the corresponding point cloud data structure, we can obtain the change of the point cloud in the whole area. This method is suitable for areas with complex terrain, and the point cloud scanning range also needs to cover the whole measurement area as much as possible. Q1,Q2, respectively, is a collection of point cloud data from two phases before mining and during mining, and its process can be summarized as follows:
(1)
DepositQ1,Q2, into the data, respectively; construct the irregular triangular network (TIN) of the surface before miningQ1using the point-by-point insertion method, and deposit each triangle in the network shape into the Delaunay chain table(2)
For the TIN constructed in the previous step, the elevation value of any point is calculated byxi,yi the bilinear interpolation method, and the corresponding point set is obtained Q1′xi,yi,zi(3)
UsingQ2−Q1′, we can get the surface subsidence basin data set corresponding to the advancing position of the working surfaceQΔxi,yi,Δzi(4)
Using theQΔxi,yi,Δzi point set, the surface subsidence basin DEM can be established in the mining process


## 4.4.4. Comparison between Models

When several periods of point clouds have been processed better and DEM models are obtained, the overall difference between models can be made. The elevations at the samex,y coordinate values are obtained by the same difference algorithm, and the elevation values at their grid points are subtracted to obtain the change information there. By expanding the range of points to the point cloud and differencing the position of each node in the DEM, we can get the overall regional change information, which is the surface subsidence basin.In model differencing, the difference is positive for uplift and negative for subsidence. By regrinding the point cloud data, the point cloud sparse places or hollow places are filled, and the error of point-to-point comparison is reduced when calculating the difference value, and the calculation is very efficient. It is more adaptable and efficient in locations with flat terrain, little vegetation, and good quality of scanned point cloud density, and the accuracy error of comparison results is minimized. However, for areas with particularly complex terrain, smooth areas are generated in the DEM formation, which increases the error in terms of terrain such as gullies and ravines and makes the obtained results have high deviation from the real values.The main object of this study lies in the flat terrain near the dam above the 25091 working face, and the dam also has good stability, and the quality of the point cloud data is influenced by the vegetation. The point cloud data quality is affected by the vegetation. The point cloud density is poor because the instrument cannot sweep to the surface, and some areas form hollows. At the dam body, since there is no vegetation on the dam road and the point cloud density is high and the point cloud quality is good, the acquisition of the surface subsidence data here is mainly done by point-to-point comparison, point-to-local model comparison, and point-to-model comparison.


## 5. Comparison of 3D Laser Scanning Technology and Traditional Monitoring Technology

Using the field data acquisition method in this paper, the error of the acquired point cloud data is mainly composed of control network error, coordinate conversion parameter error, reflection target fitting error, RTK measurement error, and 3D laser scanning own measurement error. Among them, the accuracy of 3D laser scanner is in millimeter level, which is higher than the observation accuracy of 10 mm of sinking required for subsidence monitoring. However, the accuracy of RTK measurement is at the centimeter level, and the accuracy of the point cloud after the combination of the two should depend on the accuracy of RTK, which must be lower than the conventional subsidence monitoring requirement. How much the accuracy of the scanned data affects the acquisition of the expected parameters of mining subsidence becomes the key to the application of the combination of the two for mining subsidence [21] monitoring. In this paper, the influence of the accuracy of the laser scanning data on the predicted parameters of mining subsidence is analyzed from a theoretical point of view, and the theory is verified by comparison with conventional monitoring data.


### 5.1. Analysis of the Effect of Laser Scanning Accuracy on the Predicted Parameters

It is known that the sink value at any point on the surface can be obtained from the following equation:(4)Wx,y=Wcm⋅∬D1r2⋅e−πη−x2+ξ−y2/r2dη⋅dξ,Wcm=mqcosα,tanβ=Hr,θ0=arctanWcmUwcm.From the above equation, the formula for the sinkageq coefficient can be derived as
(5)q=Wx,ym⋅cosα∬D1/r2⋅e−πη−x2+ξ−r2/r2dη⋅dξ,whereqis the sinking coefficient,tanβis the tangent of the main influence angle,θ0is the mining influence propagation angle,bis the horizontal movement coefficient,mis the coal seam thickness,His the mining depth,ris the main influence radius,Wcmis the maximum surface sinking value, andUwcmis the horizontal movement value.In the example of this paper, theH mining depth and main influence r radius can reach the level of 100 meters, and r the centimeter error of tanβ the measured value has little influence. The maximum surface subsidence value of the easily known tendency Wcm and the horizontal movement value of the point Uwcm are related to the angle of propagation θ0 of the mining influence. The 3D laser scan in the example of this paper does not have fixed measurement points; to study the horizontal movement law of the surface movement, it is still necessary to bury fixed stations; so for the time being, we do not do the influence b analysis on the horizontal movement coefficient θ0 and the mining influence propagation angle.The subsidence coefficient is usually between 0.7 and 1.1. The surface subsidence caused by coal mining is usually at the meter level. As can be seen from the formula for the sinking coefficient, the denominator of the equation is also at the meter level. Therefore, the influence of the centimeter-level error of the sinking value on the sinking coefficient is small and can meet the production needs of the mine.


### 5.2. Comparative Analysis of Laser Scanning Data and Traditional Measurement Data

In order to investigate the correctness of the monitoring results, the first three monitoring results were compared with the level measurement results of the dam and the fixed points around the dam, and the comparison results are shown in Tables1 and 2.Table 1
Elevation value comparison in leveling and TLS.
Point numberB1B2B3B4B5The first phaseElectrical leveling elevation value (m)290.9417291.0054291.0143289.9712289.4102Height measurement by 3D laser scanning (m)290.959290.999291.009289.968289.424Difference value (mm)17.3-6.4-5.3-3.213.8The second phaseElectrical leveling elevation value (m)290.8883290.8327290.816289.7291289.1131Height measurement by 3D laser scanning (m)290.872290.821290.794289.717289.095Difference value (mm)-16.3-11.7-22-12.1-18.1The third phaseElectrical leveling elevation value (m)Not measured290.4822290.3471289.127Measuring point lossHeight measurement by 3D laser scanning (m)290.46290.339289.114Difference value (mm)-22.2-8.1-13Table 2
Contrast of subsidence values in leveling and TLS.
Point numberB1B2B3B4B5Issue 2 is relative to issue 1Leveling sink value (m)-0.0534-0.1727-0.1983-0.2421-0.2971The sink value was measured by 3D laser scanning (m)-0.087-0.178-0.215-0.251-0.329Difference value (mm)-33.6-5.3-16.7-8.9-31.9Issue 3 is relative to issue 1Leveling sink value (m)Not measured-0.5232-0.6672-0.8442Measuring point lossThe sink value was measured by 3D laser scanning (m)-0.539-0.67-0.854Difference value (mm)-15.8-2.8-9.8Issue 3 is relative to issue 2Leveling sink value (m)Not measured-0.3505-0.4689-0.6021Measuring point lossThe sink value was measured by 3D laser scanning (m)-0.361-0.455-0.603Difference value (mm)-10.513.9-0.9From Tables1 and 2, it can be seen that the 3D laser scan data is close to the electronic level data, with an average error of 13.03 mm, and the maximum error occurs at the B1 and B5 points, which is 33.6 mm, and both are the edge points of the study area. It can be seen that the maximum surface subsidence monitored in the scanned area can be more than 1 m, and the maximum dam subsidence can be more than 0.8 m. In the above formula for the subsidence coefficient, the denominator has a magnitude of meters. That is, when its value is 1000 mm, the average error of 13.03 mm will raise the impact of the sinkage coefficient by only 0.303% than the required sinkage monitoring error of 10 mm. If the highest error value of 33.6 mm is used for calculation, its impact on the error of the subsidence coefficient is increased by up to 2.36% relative to the subsidence value of 1000 mm. Such an error impact is sufficient to meet the production needs of the mine, so the mining subsidence observation method established in this paper is feasible for application in the mine.


## 5.1. Analysis of the Effect of Laser Scanning Accuracy on the Predicted Parameters

It is known that the sink value at any point on the surface can be obtained from the following equation:(4)Wx,y=Wcm⋅∬D1r2⋅e−πη−x2+ξ−y2/r2dη⋅dξ,Wcm=mqcosα,tanβ=Hr,θ0=arctanWcmUwcm.From the above equation, the formula for the sinkageq coefficient can be derived as
(5)q=Wx,ym⋅cosα∬D1/r2⋅e−πη−x2+ξ−r2/r2dη⋅dξ,whereqis the sinking coefficient,tanβis the tangent of the main influence angle,θ0is the mining influence propagation angle,bis the horizontal movement coefficient,mis the coal seam thickness,His the mining depth,ris the main influence radius,Wcmis the maximum surface sinking value, andUwcmis the horizontal movement value.In the example of this paper, theH mining depth and main influence r radius can reach the level of 100 meters, and r the centimeter error of tanβ the measured value has little influence. The maximum surface subsidence value of the easily known tendency Wcm and the horizontal movement value of the point Uwcm are related to the angle of propagation θ0 of the mining influence. The 3D laser scan in the example of this paper does not have fixed measurement points; to study the horizontal movement law of the surface movement, it is still necessary to bury fixed stations; so for the time being, we do not do the influence b analysis on the horizontal movement coefficient θ0 and the mining influence propagation angle.The subsidence coefficient is usually between 0.7 and 1.1. The surface subsidence caused by coal mining is usually at the meter level. As can be seen from the formula for the sinking coefficient, the denominator of the equation is also at the meter level. Therefore, the influence of the centimeter-level error of the sinking value on the sinking coefficient is small and can meet the production needs of the mine.


## 5.2. Comparative Analysis of Laser Scanning Data and Traditional Measurement Data

In order to investigate the correctness of the monitoring results, the first three monitoring results were compared with the level measurement results of the dam and the fixed points around the dam, and the comparison results are shown in Tables1 and 2.Table 1
Elevation value comparison in leveling and TLS.
Point numberB1B2B3B4B5The first phaseElectrical leveling elevation value (m)290.9417291.0054291.0143289.9712289.4102Height measurement by 3D laser scanning (m)290.959290.999291.009289.968289.424Difference value (mm)17.3-6.4-5.3-3.213.8The second phaseElectrical leveling elevation value (m)290.8883290.8327290.816289.7291289.1131Height measurement by 3D laser scanning (m)290.872290.821290.794289.717289.095Difference value (mm)-16.3-11.7-22-12.1-18.1The third phaseElectrical leveling elevation value (m)Not measured290.4822290.3471289.127Measuring point lossHeight measurement by 3D laser scanning (m)290.46290.339289.114Difference value (mm)-22.2-8.1-13Table 2
Contrast of subsidence values in leveling and TLS.
Point numberB1B2B3B4B5Issue 2 is relative to issue 1Leveling sink value (m)-0.0534-0.1727-0.1983-0.2421-0.2971The sink value was measured by 3D laser scanning (m)-0.087-0.178-0.215-0.251-0.329Difference value (mm)-33.6-5.3-16.7-8.9-31.9Issue 3 is relative to issue 1Leveling sink value (m)Not measured-0.5232-0.6672-0.8442Measuring point lossThe sink value was measured by 3D laser scanning (m)-0.539-0.67-0.854Difference value (mm)-15.8-2.8-9.8Issue 3 is relative to issue 2Leveling sink value (m)Not measured-0.3505-0.4689-0.6021Measuring point lossThe sink value was measured by 3D laser scanning (m)-0.361-0.455-0.603Difference value (mm)-10.513.9-0.9From Tables1 and 2, it can be seen that the 3D laser scan data is close to the electronic level data, with an average error of 13.03 mm, and the maximum error occurs at the B1 and B5 points, which is 33.6 mm, and both are the edge points of the study area. It can be seen that the maximum surface subsidence monitored in the scanned area can be more than 1 m, and the maximum dam subsidence can be more than 0.8 m. In the above formula for the subsidence coefficient, the denominator has a magnitude of meters. That is, when its value is 1000 mm, the average error of 13.03 mm will raise the impact of the sinkage coefficient by only 0.303% than the required sinkage monitoring error of 10 mm. If the highest error value of 33.6 mm is used for calculation, its impact on the error of the subsidence coefficient is increased by up to 2.36% relative to the subsidence value of 1000 mm. Such an error impact is sufficient to meet the production needs of the mine, so the mining subsidence observation method established in this paper is feasible for application in the mine.


## 6. Deformation of the Ground Surface and Surface Structures above the 25091 Working Face

The ground 3D laser scan provides sufficient ground point cloud data, and when analyzing the subsidence of the surface moving basin in different periods, the method of establishing a regular grid model is used to obtain the subsidence within the moving basin by interpolating the changes in the elevation values of the same grid nodes in different periods and using elevation subtraction, under the premise of satisfying the accuracy of the finding.The method requires interpolation of point cloud data for two periods separately and comparison of the two periods based on the irregular triangular grid model. In the study, according to the surface moving basin monitoring requirements, the point and overall model comparison method is selected, and only the baseline data is constructed with the irregular triangular network model, and the ground point cloud data to be compared is used to compare directly with the triangular network model to obtain the subsidence of each point of the point cloud to be compared relative to the previous baseline model and to save it. This method can retain the accuracy of the original point cloud to a large extent and is suitable for surface moving basin subsidence value acquisition. At the same time, the saved point cloud data can be used to extract any profile points, and after sampling at a certain point interval, the profile analysis can be performed directly.


### 6.1. Horizontal Movement Analysis of the Dam Facade after Fitting

Movement deformation monitoring of buildings such as houses is an unparalleled advantage of 3D laser scanning technology over traditional measurements. In this study, the outer contour of the north wall of the dam was targeted, and the scanned point cloud data of three periods were used to obtain the overall contour line of the outer wall of the dam by fitting the algorithm after classifying and dedrying the point cloud with RISCAN Pro software. The overall contour lines obtained from the point clouds of different periods are analyzed and compared to obtain the overall deformation of the target dam (see Figure8).Figure 8
Deformation of the contour of the dam external wall.In Figure8, the contour line of the external wall of the dam 0417 represents the original position of the dam wall. In the figure, the contour line of the external wall extracted in the latter two phases is compared with the original position and it can be seen that the dam has undergone a significant horizontal movement and the direction of movement is approximately the same as the direction of work surface advancement.


### 6.2. Changes in the Subsidence of the Ground Surface and Surface Structures under 3D Laser Scanning Technology

Using the ground point cloud data obtained from the three phases after preprocessing, a model-to-model comparison was conducted to obtain the surface subsidence in different periods. Firstly, using the ground point cloud data of the first period as a reference base, an irregular triangular network surface model is constructed, and the triangular side length threshold is set to 0.1 m, and the point clouds of the second period and the third period are compared with the model of the first period, respectively, to obtain the surface subsidence during the advancement of the working surface, as shown in Figures8 and 9 secondly, using the ground point cloud data of the second period as a reference base, an irregular triangular network surface model, the triangle network edge length threshold is set to 0.1 m, and the third phase point cloud is compared with the second phase model, and the comparison results of different mining stages are shown in Figure 9.Figure 9
Comparison of point cloud DEM in the second and first phase.When the first phase data was collected, the working face was advanced by 38 m, and the impact caused by underground mining had not yet reached the surface, so the data at this time was used as a reference base; when the second phase data was collected, the working face was advanced by 172 m. Figure8 shows the comparison between the point cloud DEM of the second phase and the first phase of the scan. The color range of the cloud indicates the sinking amount of -0.1~0.2 m, respectively, with the deepest blue color representing the sinking amount of 0.2 m and the deepest red color representing the sinking amount of -0.1 m. From the data, the amount of surface subsidence was already more obvious in the second phase of data collection, and the surface subsidence values were almost all greater than 0.2 m. The figure shows that underground mining has reached the dam, and the east side of the dam is darker than the west side of the dam, which proves that the amount of subsidence on the east side of the dam is greater than the amount of subsidence on the west side of the dam. In the above figure, there is a gap between some areas and other areas, which is due to the fact that the surface vegetation in the area is too dense for the laser scanner to sweep to the surface point.At the time of data collection in the third phase, the working face was advanced by 427 m. From Figures10 and Figures 11, the surface subsidence is intensified. The color range of the cloud map indicates the subsidence amount of -0.1~1.0 m, the deepest blue color represents the subsidence amount of 1.0 m, and the deepest red color represents the subsidence amount of -0.1 m. The comparison between phase III and phase II shows that the whole map shows more regular subsidence. Among them, the east side of the dam is darker than the west side, indicating that the sinkage value on the east side of the dam is larger than that on the west side, and the dam shows uneven sinking. The closer the color is to the extraction area in the cloud map, the darker the color is, indicating that the amount of subsidence under the influence of mining is greater. From the overall area, the sinking trend of the ground surface is more obvious.Figure 10
Comparison of point cloud DEM in the third and first phases.Figure 11
Comparison of point cloud DEM in the third and third phases.At the time of data collection in the fourth phase, the working surface had been pushed through. From Figure12, the surface subsidence has increased. Among them, the color range of the cloud map indicates the subsidence amount of -0.1~1.3 m, respectively, the deepest blue color represents the subsidence amount of 1.3 m, and the deepest red color represents the subsidence amount of -0.1 m. From the overall area, the sinking trend of the ground surface is more obvious. The maximum sinking area is greater than 1.3 m and shows a regular sinking movement.Figure 12
Comparison of point cloud DEM in the third and third phases.


### 6.3. Analysis of the Sinking Deformation of the Dam Cross Section

On the basis of the DEM established by the point cloud data of each period, a longitudinal profile was laid along the road surface of the dam to analyze the deformation of the longitudinal section of the road surface of the dam; three transverse profile lines of the dam were laid along the transverse section of the dam to analyze the deformation of the north-south direction of the dam. The relative positions of the extracted transverse and longitudinal profile lines of the dam and the working surface are shown in Figure13.Figure 13
Section line in the dam relative to working face.The sinking curve of the section line in the cross-section of the dam is shown in Figures14–17.Figure 14
The subsidence deformation of the longitudinal section line of the dam.Figure 15
The subsidence with 1# lateral line in the dam.Figure 16
The subsidence with 2# lateral line in the dam.Figure 17
The subsidence with 3# lateral line in the dam.Generally speaking, the west side of the dam lags slightly behind the east side of the roadway. The subsidence with 3# lateral line in the dam shows that there is a difference in the amount of subsidence between the north side and the south side of the dam, in which the north side of the dam has more subsidence than the south side. This is the result of the working face from the south to the north; the dam is in the vicinity of the mining area, which makes the dam affected by coal mining and the relative location of the mining area has a strong correlation, which presents the closer to the mining area, the greater the dam affected by coal mining, the closer to the center of the mining area, the greater the dam affected by coal mining characteristics.In addition, Figure14 shows the unevenness of the sinking condition of the dam in the east-west direction. In the third period, the working face advanced 427 m, and the analysis of the sinking curve shows that the east side of the dam sinks about 700 mm, while the west side of the dam sinks about 500 mm, and the sinking difference of nearly 200 mm between the east and west side reveals the unevenness in the moving deformation of the dam. The point is also proved in the data of the fourth period. However, at this time, there was a more uniform sinking of the dam compared to the third period, i.e., the area as a whole sank by about 200 mm in the fourth period. In the dam damage situation in Section 2.2 of this paper, the damage gap between the east and west sides of the dam is obvious, where the damage on the east side is drastic; for example, the wall on the east side of the dam formed a step drop and reached 6~10 cm; the road surface on the east side of the dam produced cracks running east-west. It shows the correlation between the heterogeneity of the dam subsidence condition and the damage gap between the east and west sides of the dam.


### 6.4. Analysis of Surface Subsidence Pattern

The surface subsidence curves obtained from the transverse section line along the open cut and the longitudinal section line along the center line of the mining area are shown in Figures18 and 19.Figure 18
The subsidence deformation along the tendency profile line in the direction of the open cut.Figure 19
The subsidence in decline direction along the midline of the mining area.The above figure shows the subsidence deformation of the inclination and strike profile. Although the data are distorted at some points due to insufficient vegetation filtering, it can still reflect the overall subsidence trend of the ground surface above the mining area.From the perspective of this paper, the area affected by mining will gradually change with the advancement of the working face, and the offset of the position of the maximum sinking point reflects the special phenomenon presented by the overburden breaking and surface movement under the influence of overburden reverse tilt. For example, in the sinking curve shown in Figure18, it can be seen that the maximum sinking point is located about 60 m to the west of the open-cutting eye when the working face advances 173 m during the second phase of data collection; at this time, the mining influence has just spread to the surface, and the surface movement is mainly sinking. With the continuous advance of the coal seam, the mining influence continues to propagate upward, and as the overlying rock seam’s reverse dip angle becomes larger, the positive stress on the reverse dip rock seam decreases while the shear stress increases, and the shear movement in the overlying rock occurs nearly parallel to the rock seam level, changing the overlying rock movement law. In the third phase, the working face was advanced 427 m, and its maximum sinking point was about 35 m to the west of the open-cutting eye, while in the fourth phase, the working face had been pushed and its maximum sinking point was about 10 m to the west of the open-cutting midpoint. It means that the maximum sink point is westward; i.e., the upper hill of the mining area is shifted. This highlights the influence of the overburden reverse dip on the surface movement.


## 6.1. Horizontal Movement Analysis of the Dam Facade after Fitting

Movement deformation monitoring of buildings such as houses is an unparalleled advantage of 3D laser scanning technology over traditional measurements. In this study, the outer contour of the north wall of the dam was targeted, and the scanned point cloud data of three periods were used to obtain the overall contour line of the outer wall of the dam by fitting the algorithm after classifying and dedrying the point cloud with RISCAN Pro software. The overall contour lines obtained from the point clouds of different periods are analyzed and compared to obtain the overall deformation of the target dam (see Figure8).Figure 8
Deformation of the contour of the dam external wall.In Figure8, the contour line of the external wall of the dam 0417 represents the original position of the dam wall. In the figure, the contour line of the external wall extracted in the latter two phases is compared with the original position and it can be seen that the dam has undergone a significant horizontal movement and the direction of movement is approximately the same as the direction of work surface advancement.


## 6.2. Changes in the Subsidence of the Ground Surface and Surface Structures under 3D Laser Scanning Technology

Using the ground point cloud data obtained from the three phases after preprocessing, a model-to-model comparison was conducted to obtain the surface subsidence in different periods. Firstly, using the ground point cloud data of the first period as a reference base, an irregular triangular network surface model is constructed, and the triangular side length threshold is set to 0.1 m, and the point clouds of the second period and the third period are compared with the model of the first period, respectively, to obtain the surface subsidence during the advancement of the working surface, as shown in Figures8 and 9 secondly, using the ground point cloud data of the second period as a reference base, an irregular triangular network surface model, the triangle network edge length threshold is set to 0.1 m, and the third phase point cloud is compared with the second phase model, and the comparison results of different mining stages are shown in Figure 9.Figure 9
Comparison of point cloud DEM in the second and first phase.When the first phase data was collected, the working face was advanced by 38 m, and the impact caused by underground mining had not yet reached the surface, so the data at this time was used as a reference base; when the second phase data was collected, the working face was advanced by 172 m. Figure8 shows the comparison between the point cloud DEM of the second phase and the first phase of the scan. The color range of the cloud indicates the sinking amount of -0.1~0.2 m, respectively, with the deepest blue color representing the sinking amount of 0.2 m and the deepest red color representing the sinking amount of -0.1 m. From the data, the amount of surface subsidence was already more obvious in the second phase of data collection, and the surface subsidence values were almost all greater than 0.2 m. The figure shows that underground mining has reached the dam, and the east side of the dam is darker than the west side of the dam, which proves that the amount of subsidence on the east side of the dam is greater than the amount of subsidence on the west side of the dam. In the above figure, there is a gap between some areas and other areas, which is due to the fact that the surface vegetation in the area is too dense for the laser scanner to sweep to the surface point.At the time of data collection in the third phase, the working face was advanced by 427 m. From Figures10 and Figures 11, the surface subsidence is intensified. The color range of the cloud map indicates the subsidence amount of -0.1~1.0 m, the deepest blue color represents the subsidence amount of 1.0 m, and the deepest red color represents the subsidence amount of -0.1 m. The comparison between phase III and phase II shows that the whole map shows more regular subsidence. Among them, the east side of the dam is darker than the west side, indicating that the sinkage value on the east side of the dam is larger than that on the west side, and the dam shows uneven sinking. The closer the color is to the extraction area in the cloud map, the darker the color is, indicating that the amount of subsidence under the influence of mining is greater. From the overall area, the sinking trend of the ground surface is more obvious.Figure 10
Comparison of point cloud DEM in the third and first phases.Figure 11
Comparison of point cloud DEM in the third and third phases.At the time of data collection in the fourth phase, the working surface had been pushed through. From Figure12, the surface subsidence has increased. Among them, the color range of the cloud map indicates the subsidence amount of -0.1~1.3 m, respectively, the deepest blue color represents the subsidence amount of 1.3 m, and the deepest red color represents the subsidence amount of -0.1 m. From the overall area, the sinking trend of the ground surface is more obvious. The maximum sinking area is greater than 1.3 m and shows a regular sinking movement.Figure 12
Comparison of point cloud DEM in the third and third phases.


## 6.3. Analysis of the Sinking Deformation of the Dam Cross Section

On the basis of the DEM established by the point cloud data of each period, a longitudinal profile was laid along the road surface of the dam to analyze the deformation of the longitudinal section of the road surface of the dam; three transverse profile lines of the dam were laid along the transverse section of the dam to analyze the deformation of the north-south direction of the dam. The relative positions of the extracted transverse and longitudinal profile lines of the dam and the working surface are shown in Figure13.Figure 13
Section line in the dam relative to working face.The sinking curve of the section line in the cross-section of the dam is shown in Figures14–17.Figure 14
The subsidence deformation of the longitudinal section line of the dam.Figure 15
The subsidence with 1# lateral line in the dam.Figure 16
The subsidence with 2# lateral line in the dam.Figure 17
The subsidence with 3# lateral line in the dam.Generally speaking, the west side of the dam lags slightly behind the east side of the roadway. The subsidence with 3# lateral line in the dam shows that there is a difference in the amount of subsidence between the north side and the south side of the dam, in which the north side of the dam has more subsidence than the south side. This is the result of the working face from the south to the north; the dam is in the vicinity of the mining area, which makes the dam affected by coal mining and the relative location of the mining area has a strong correlation, which presents the closer to the mining area, the greater the dam affected by coal mining, the closer to the center of the mining area, the greater the dam affected by coal mining characteristics.In addition, Figure14 shows the unevenness of the sinking condition of the dam in the east-west direction. In the third period, the working face advanced 427 m, and the analysis of the sinking curve shows that the east side of the dam sinks about 700 mm, while the west side of the dam sinks about 500 mm, and the sinking difference of nearly 200 mm between the east and west side reveals the unevenness in the moving deformation of the dam. The point is also proved in the data of the fourth period. However, at this time, there was a more uniform sinking of the dam compared to the third period, i.e., the area as a whole sank by about 200 mm in the fourth period. In the dam damage situation in Section 2.2 of this paper, the damage gap between the east and west sides of the dam is obvious, where the damage on the east side is drastic; for example, the wall on the east side of the dam formed a step drop and reached 6~10 cm; the road surface on the east side of the dam produced cracks running east-west. It shows the correlation between the heterogeneity of the dam subsidence condition and the damage gap between the east and west sides of the dam.


## 6.4. Analysis of Surface Subsidence Pattern

The surface subsidence curves obtained from the transverse section line along the open cut and the longitudinal section line along the center line of the mining area are shown in Figures18 and 19.Figure 18
The subsidence deformation along the tendency profile line in the direction of the open cut.Figure 19
The subsidence in decline direction along the midline of the mining area.The above figure shows the subsidence deformation of the inclination and strike profile. Although the data are distorted at some points due to insufficient vegetation filtering, it can still reflect the overall subsidence trend of the ground surface above the mining area.From the perspective of this paper, the area affected by mining will gradually change with the advancement of the working face, and the offset of the position of the maximum sinking point reflects the special phenomenon presented by the overburden breaking and surface movement under the influence of overburden reverse tilt. For example, in the sinking curve shown in Figure18, it can be seen that the maximum sinking point is located about 60 m to the west of the open-cutting eye when the working face advances 173 m during the second phase of data collection; at this time, the mining influence has just spread to the surface, and the surface movement is mainly sinking. With the continuous advance of the coal seam, the mining influence continues to propagate upward, and as the overlying rock seam’s reverse dip angle becomes larger, the positive stress on the reverse dip rock seam decreases while the shear stress increases, and the shear movement in the overlying rock occurs nearly parallel to the rock seam level, changing the overlying rock movement law. In the third phase, the working face was advanced 427 m, and its maximum sinking point was about 35 m to the west of the open-cutting eye, while in the fourth phase, the working face had been pushed and its maximum sinking point was about 10 m to the west of the open-cutting midpoint. It means that the maximum sink point is westward; i.e., the upper hill of the mining area is shifted. This highlights the influence of the overburden reverse dip on the surface movement.


## 7. Conclusions

(a)
After comparing the 3D laser scan data with the electronic level data, it can be seen that the sinkhole monitoring error of the 3D laser scan is 13.03 mm on average. Compared to the sinkhole value of 1 m, its error impact on the sinkhole coefficient will only increase by 0.303%, which is sufficient to meet the production needs of the mine(b)
The 3D laser point cloud shows that the uneven sinking of the dam is remarkable. The actual observation on site shows that the uneven settlement of the dam on the surface was obvious during the recovery period of 25091 working face. The damage on the east side of the dam was more serious, accompanied by cracks generated in the east-west direction. The DEM model of part of the surface and dam above the 25091 working face was obtained by 3D laser scanning technology. The model was analyzed to obtain information on surface subsidence. The study analysis shows that the uneven subsidence condition of the dam is obvious. Overall, the east side of the dam shows a large subsidence and the west side of the dam shows a small subsidence(c)
During the advance of the working face, the maximum surface sinking point gradually shifted to the upper hill. This reflects the intrinsic change of the mining influence area under the anticlinal rocks and reveals the controlling effect of the anticlinal rocks on the surface movement



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*Source: 3339264-2022-04-28.xml*