CN114199189B - A mining subsidence monitoring method combining UAV and DInSAR technology - Google Patents

Abstract

The present invention relates to the technical field of mining subsidence monitoring, and in particular to a mining subsidence monitoring method that combines drones and DInSAR technology. The drone monitoring results of the mining subsidence area are integrated with the DInSAR differential settlement results. According to the method proposed by the present invention The data screening method filters DInSAR and UAV monitoring data so that the filtered DInSAR monitoring values are used for edge monitoring, and the filtered UAV monitoring values are used for the main subsidence value of the subsidence center. It not only takes advantage of the high accuracy of UAV data in the center of the subsidence area, but also retains the superiority of DInSAR differential results in edge monitoring, making up for the shortcomings of the DInSAR method's incoherence in large gradient deformation and the UAV technology's small deformation at the edge. Monitoring deficiencies. The advantages of the two data are complemented to achieve high-precision monitoring of mining subsidence areas.

Description

A mining subsidence monitoring method combining UAV and DInSAR technology

Technical field

The invention relates to the technical field of mining subsidence monitoring, and in particular to a mining subsidence monitoring method that combines drones and DInSAR technology.

Background technique

Mining causes surface movement and deformation because after the ore layers in the mining area are mined, the original stress balance of the surrounding rock mass is destroyed, which will inevitably cause stress to be redistributed to achieve a new balance. After a large area of underground mineral strata is mined, the upper rock strata lose support, and the original stress balance conditions are destroyed, causing movement and deformation. Subsequently, the overlying rock strata collapse, crack, and bend, causing the surface to sink and dent deformation. Here, the ground collapse and ground subsidence caused by mining of mineral strata are collectively referred to as mining subsidence.

Mining subsidence is affected by many factors, and complete surface deformation information needs to be monitored to provide scientific basis for safe mining and comprehensive environmental management of subsidence areas. The traditional mining subsidence monitoring methods are leveling and GPS measurement. However, traditional monitoring technologies such as leveling and GPS measurement require a lot of manpower and material resources, and cannot directly obtain continuous surface deformation information. UAV low-altitude photogrammetry technology and differential interferometric synthetic aperture radar (DInSAR) technology, as new geodetic methods developed in recent years, have made up for the shortcomings of traditional monitoring technology to a certain extent. Mining subsidence is a large-deformation geological disaster. Although drone monitoring of mining subsidence can obtain the subsidence value of the center of the subsidence area, the subsidence data cannot reach the millimeter-level accuracy of traditional monitoring. It is difficult to carry out high-precision monitoring of the edges of the mining area. Edge expression Poor ability; DInSAR technology is affected by many factors such as orbit error and atmospheric delay. When the surface deformation gradient is too large, the interference image pair becomes decoherent, and effective interference fringes cannot be obtained, so that the main value of mining subsidence cannot be obtained. This results in lower accuracy in subsidence areas with large and rapid settlement.

Due to the limitations of existing monitoring technologies, the monitoring process cannot accurately obtain subsidence deformation information on the entire surface. Therefore, it is urgent to propose a method for monitoring mining subsidence deformation to obtain more complete and high-precision monitoring results.

Contents of the invention

The purpose of the present invention is to provide a mining subsidence monitoring method that combines drones and DInSAR technology, so as to solve the aforementioned problems existing in the existing technology.

In order to achieve the above objects, the technical solutions adopted by the present invention are as follows:

A mining subsidence monitoring method that combines drones and DInSAR technology, the method includes:

Monitor the edge area of mining subsidence through DInSAR, determine the coherence threshold of DInSAR monitoring based on SAR satellite parameters, and use the monitoring point with a DInSAR monitoring value less than the coherence threshold and closest to the center of mining subsidence as the boundary of the DInSAR monitoring area;

Use drones to monitor the central area of mining subsidence. Determine the monitoring threshold of drone monitoring based on the drone monitoring value and the corresponding measured level value. The drone monitoring value is greater than the monitoring threshold and is farthest from the mining subsidence center. The monitoring point serves as the boundary of the drone monitoring area;

According to the coordinates and corresponding monitoring values of each monitoring point in the DInSAR monitoring area and UAV monitoring area, high-precision surface deformation information of mining subsidence is obtained.

Preferably, the coherence threshold for DInSAR monitoring is determined based on SAR satellite parameters, specifically:

The theoretical maximum deformation gradient d _max detectable by DInSAR is obtained according to the radar wavelength and pixel size of the SAR satellite, as described The λ is the radar wavelength, and the μ is the pixel size;

The coherence coefficient is determined according to the preset DInSAR detectable maximum deformation gradient D _max and the d _max , the D _max =d _max +0.002 (γ-1), and the γ is the coherence coefficient;

The obtained coherence coefficient is used as the coherence threshold for screening DInSAR monitoring values.

Preferably, the monitoring threshold for drone monitoring is determined based on the drone monitoring value and the corresponding measured level value, specifically as follows:

According to the formula Get the monitoring threshold T;

Among them, U _i is the UAV monitoring value of the i-th monitoring point, _Wi is the measured level value of the i-th monitoring point, and n is the number of monitoring points.

Preferably, if there are unmonitored points between the DInSAR monitoring area and the UAV monitoring area, the inverse distance weighting method is used to determine the monitoring value of the unmonitored points, specifically:

According to the distance between an unmonitored point and its surrounding monitored points, the formula Calculate the monitoring value of unmonitored points;

In the formula, i is an unmonitored point, j is the monitoring points around the unmonitored point, W _i and W _j represent the monitoring values of the corresponding points respectively, and d _j is the distance between the unmonitored point and its surrounding monitoring points.

The beneficial effects of the present invention are:

The present invention provides a mining subsidence monitoring method that combines UAV and DInSAR technology. The UAV monitoring results of the mining subsidence area are integrated with the DInSAR differential subsidence results. According to the data screening method proposed by the present invention, D- InSAR and UAV monitoring data are filtered, so that the filtered DInSAR monitoring value is used for edge monitoring, and the filtered UAV monitoring value is used for the main subsidence value of the subsidence center. It not only takes advantage of the high accuracy of UAV data in the center of the subsidence area, but also retains the superiority of DInSAR differential results in edge monitoring, making up for the shortcomings of the D-InSAR method's incoherence in large gradient deformation and the UAV technology in edge monitoring. Shortcomings in micro deformation monitoring. The advantages of the two data are complemented to achieve high-precision monitoring of mining subsidence areas.

Description of drawings

Figure 1 is a schematic diagram of each monitoring area in the mining subsidence area in the embodiment of the present invention;

In the figure, 1-the edge of the mining subsidence impact of the mining area, 2-the boundary of the DInSAR monitoring area, 3-the boundary of the UAV monitoring area.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

The present invention provides a mining subsidence monitoring method that combines drones and DInSAR technology. The method includes:

Monitor the edge area of mining subsidence through DInSAR, determine the coherence threshold of DInSAR monitoring based on SAR satellite parameters, and use the monitoring point with a DInSAR monitoring value less than the coherence threshold and closest to the center of mining subsidence as the boundary of the DInSAR monitoring area;

Use drones to monitor the central area of mining subsidence. Determine the monitoring threshold of drone monitoring based on the drone monitoring value and the corresponding measured level value. The drone monitoring value is greater than the monitoring threshold and is farthest from the mining subsidence center. The monitoring point serves as the boundary of the drone monitoring area;

According to the coordinates and corresponding monitoring values of each monitoring point in the DInSAR monitoring area and UAV monitoring area, high-precision surface deformation information of mining subsidence is obtained.

At present, different SAR satellites mainly use L-band, C-band, and X-band. For example, ALOS-1 and JERS-1 satellites use L-band with a wavelength of 23.5cm; RADARSTA and Sentinel satellites use C-band with a wavelength of 5.6cm; TerraSAR-X and COSMO satellites use X-band with a wavelength of 3cm. In DInSAR technology, the maximum deformation amount of adjacent pixels that can be obtained by SAR satellites through interferometry in adjacent revisit periods is λ/4. Therefore, the maximum deformation amount of adjacent pixels that can be obtained for the L-band is 5.87cm, the maximum deformation amount of adjacent pixels that can be obtained for the C-band is 1.4cm, and the maximum deformation amount of adjacent pixels that can be obtained for the X-band is 0.75cm. In addition, under ideal conditions, the maximum settlement magnitude that DInSAR technology can monitor during two imaging periods is: W=r·λ/4μ, where: W represents the line-of-sight settlement, r represents the main influence radius of the mining area, and μ is the pixel Size (ground resolution of SAR sensor). The maximum vertical subsidence monitored by InSAR is ΔRmax=W/cosθ, where ΔRmax represents the maximum subsidence in the vertical direction, and θ is the radar incident angle.

In practical applications, the subsidence gradient and subsidence amount that can be monitored in each band of SAR satellites are smaller than their theoretical values. For high-intensity and large-gradient deformations, DInSAR technology is also difficult to handle, so it is necessary to use UAV data to monitor large subsidence gradients. Based on experience, the root mean square error between drone data and measured data is roughly 15cm-20cm. When the subsidence reaches the meter level, using drones to monitor the deformation of the mining area can basically meet the accuracy requirements. However, for the edge areas of subsidence, the accuracy It seems relatively insufficient.

Therefore, the present invention combines drones and DInSAR technology to jointly monitor surface subsidence and deformation, and uses the advantages of the drone's maneuverability and small terrain restrictions to monitor the center of the mining subsidence area; the use of DInSAR technology can make up for the measurement difficulties in traditional monitoring methods. It has the disadvantage of sparse points, but has the characteristics and advantages of real-time and high precision. It can monitor the deformation of small subsidence at the edge of the mining subsidence area. By integrating UAV deformation monitoring results with DInSAR results, the advantages of the two data complement each other to obtain more accurate monitoring results. The distribution of the DInSAR monitoring area and the drone monitoring area is shown in Figure 1. In the figure, 1 is the edge affected by mining subsidence in the mining area, 2 is the boundary of the DInSAR monitoring area, and 3 is the boundary of the drone monitoring area. It should be noted that the distribution of the monitoring area The schematic diagram is only a specific implementation schematic diagram provided by the present invention, and is only used to illustrate the present invention but not to limit the scope of the present invention.

In this embodiment, it is necessary to determine the coherence threshold for DInSAR monitoring based on SAR satellite parameters, and eliminate areas with low coherence to determine the DInSAR monitoring area. The formula for determining the coherence threshold is as follows:

D _max =d _max +0.002(γ-1)

In the formula, D _max is the maximum deformation gradient that DInSAR can detect; γ is the coherence coefficient of the interferogram, ranging between 0 and 1; d _max is the theoretical maximum deformation gradient that DInSAR can detect, that is (λ is the radar wavelength, μ is the pixel size). D _max can be preset according to specific implementation scenarios to obtain appropriate coherence coefficients as coherence thresholds. For example, for the image of Sentinel-1A using C-band and resolution of 20m, it can be calculated from the formula that when the coherence coefficient γ is less than 0.3, the maximum deformation gradient _Dmax that DInSAR can detect is almost 0. Therefore, in order to ensure the accuracy of the phase unwrapping results, the coherence coefficient 0.3 is used as the screening threshold for DInSAR monitoring values. By extracting the coherence coefficient of each monitoring point, during the data processing process, the coherence threshold of the interference pair is set to 0.3 to obtain a high coherence interference pattern.

In this embodiment, the specific steps to determine the DInSAR monitoring area are: process the two scene SAR images, and perform the two-track method difference in combination with an external DEM (Digital Elevation Model, Digital Elevation Model) to obtain a differential interference pattern. The "value extraction to point" function of the "Spatial Analyst" tool in ArcGIS is used to process the above differential interferogram and extract the DInSAR monitoring values of each monitoring point on the working surface. Then use the set coherence threshold of 0.3 to filter them, use ArcGIS to open the coherence map obtained during DInSAR processing, obtain the coherence coefficients of each monitoring point on the working surface, and use the coherence threshold set above to filter them. After screening, the monitoring point with a coherence less than 0.3 and closest to the edge of the mining subsidence basin is used as the limit for DInSAR monitoring. That is, other monitoring points on the side of this monitoring point close to the edge of the mining subsidence basin are monitored using DInSAR.

In this embodiment, the monitoring threshold for drone monitoring needs to be determined based on the drone monitoring value and the corresponding measured level value to determine the drone monitoring area. The formula for determining the monitoring threshold T is as follows:

Among them, U _i is the UAV monitoring value of the i-th monitoring point, _Wi is the measured level value of the i-th monitoring point, and n is the number of monitoring points.

The specific steps for determining the drone monitoring area in this embodiment are: using drone technology to obtain two periods of DEM data corresponding to the DInSAR interference pair time. The two periods of DEM were then subtracted to obtain the situation of the surface subsidence basin caused by mining in the study area during the two periods of time. Extract the UAV monitoring values of each monitoring point, and then use the above monitoring threshold T to filter the monitoring values obtained by the UAV. Select the monitoring point whose UAV monitoring value is greater than the monitoring threshold T and is farthest from the center of the mining subsidence basin. The limit of drone monitoring is to use drone monitoring at other monitoring points near the center of the mining subsidence basin.

In this embodiment, the mining subsidence edge area monitored with high precision by DInSAR and the mining subsidence center area monitored with high precision by the UAV are fused for data. If there is an unknown gap between the DInSAR monitoring area and the UAV monitoring area, If a monitoring point (that is, there is a null value between the fused DInSAR monitoring area and the UAV monitoring area boundary), then the inverse distance weighting method is used to determine the monitoring value of the unmonitored point. The specific steps are:

According to the distance between the unmonitored point (xi, yi) and its surrounding monitoring points (xj, yj), the monitoring value of the unmonitored point is calculated by the following formula:

In the formula, i is an unmonitored point, j is the monitoring points around the unmonitored point, W _i and W _j represent the monitoring values of the corresponding points respectively, d _j is the unmonitored point (xi, yi) and its surrounding monitoring points (xj , yj), the distance between the unmonitored point (xi, yi) and its surrounding monitored points (xj, yj)

In this embodiment, if there is an overlap between the DInSAR monitoring area and the UAV monitoring area, since the monitoring error in the overlapping area is not large, the monitoring value of either monitoring area can be used or the average of the two monitoring values can be used. .

In this embodiment, after obtaining the complete subsidence value of each monitoring point (i.e., monitoring value), the coordinates of each monitoring point and the corresponding subsidence value can be used to draw a subsidence curve, which can visually display the overall subsidence characteristics of the subsidence basin. .

By adopting the above technical solutions disclosed in the present invention, the following beneficial effects are obtained:

The present invention provides a mining subsidence monitoring method that combines UAV and DInSAR technology. The UAV monitoring results of the mining subsidence area are integrated with the DInSAR differential subsidence results. According to the data screening method proposed by the present invention, D- InSAR and UAV monitoring data are filtered, so that the filtered DInSAR monitoring value is used for edge monitoring, and the filtered UAV monitoring value is used for the main subsidence value of the subsidence center. It not only takes advantage of the high accuracy of UAV data in the center of the subsidence area, but also retains the superiority of DInSAR differential results in edge monitoring, making up for the shortcomings of the D-InSAR method's incoherence in large gradient deformation and the UAV technology in edge monitoring. Shortcomings in micro deformation monitoring. The advantages of the two data are complemented to achieve high-precision monitoring of mining subsidence areas.

The above are only preferred embodiments of the present invention. It should be noted that those skilled in the art can make several improvements and modifications without departing from the principles of the present invention. These improvements and modifications can also be made. The scope of protection of the present invention should be considered.

Claims (2)

1. A mining subsidence monitoring method combining unmanned aerial vehicle and DInSAR technologies, the method comprising:

the method comprises the steps of monitoring an edge area of mining subsidence through a DINSAR, determining a coherence threshold of the DINSAR monitoring according to SAR satellite parameters, and taking a monitoring point which is smaller than the coherence threshold and closest to a mining subsidence center as a boundary of the DINSAR monitoring area;

determining a coherence threshold of the DINSAR monitoring according to SAR satellite parameters, wherein the coherence threshold comprises the following specific steps:

obtaining a theoretical maximum deformation gradient d which can be detected by the DINSAR according to the radar wavelength and the pixel size of the SAR satellite _max The saidThe lambda is the radar wavelength, and the mu is the pixel size;

maximum deformation gradient D detectable according to preset DINSAR _max And said d _max Determining a coherence coefficient, said D _max ＝d _max +0.002 (γ -1), the γ being the coherence coefficient;

taking the obtained coherence coefficient as a coherence threshold for screening the DINSAR monitoring value;

the method comprises the steps of monitoring a mining subsidence central area through an unmanned aerial vehicle, determining a monitoring threshold value of unmanned aerial vehicle monitoring according to an unmanned aerial vehicle monitoring value and a corresponding actually measured level value, and taking a monitoring point which is larger than the monitoring threshold value and farthest away from the mining subsidence center as a boundary of the unmanned aerial vehicle monitoring area;

determining a monitoring threshold value of unmanned aerial vehicle monitoring according to the unmanned aerial vehicle monitoring value and the corresponding actually measured level value, specifically:

according to the formulaAcquiring a monitoring threshold T;

wherein the U is _i Unmanned aerial vehicle monitoring value W for ith monitoring point _i The measured level value of the ith monitoring point is obtained, and n is the number of monitoring points;

and acquiring high-precision surface deformation information of mining subsidence according to the coordinates of each monitoring point and the corresponding monitoring value of the DINSAR monitoring area and the unmanned aerial vehicle monitoring area.

2. The mining subsidence monitoring method combining the unmanned aerial vehicle and the DInSAR technology according to claim 1, wherein if an unmonitored point exists between the DInSAR monitoring area and the unmanned aerial vehicle monitoring area, determining a monitoring value of the unmonitored point by adopting an inverse distance weighting method, specifically:

according to the distance between the non-monitored point and the monitored points around the non-monitored point, the method passes through the formulaCalculating a monitoring value of an unmonitored point;

wherein i is an unmonitored point, j is a monitored point around the unmonitored point, W _i And W is _j Respectively representing the monitoring values of the corresponding points, d _j Is the distance between the non-monitored point and the monitored points around it.