InSAR- and PIM-Based Inclined Goaf Determination for Illegal Mining Detection
"> Figure 1
<p>Theoretical model of discontinuous medium. (<b>a</b>) random media theory model; (<b>b</b>) probability distribution of particle movement; (<b>c</b>) probabilistic subsidence simulation; (<b>d</b>) sandbox model.</p> "> Figure 2
<p>Subsidence and curvature curves of a horizontal goaf on the main section. <span class="html-italic">A</span> and <span class="html-italic">B</span> are the boundary points of the sinking basin. <span class="html-italic">E</span> is the inflection point of the subsidence curve. <math display="inline"><semantics> <msub> <mi>E</mi> <mn>0</mn> </msub> </semantics></math> is the zero point of the curvature curve. <span class="html-italic">H</span> is the mining depth. <math display="inline"><semantics> <msub> <mi>W</mi> <mi>m</mi> </msub> </semantics></math> is the maximum sinking point of the subsidence basin. <math display="inline"><semantics> <msub> <mi>δ</mi> <mn>0</mn> </msub> </semantics></math> is the boundary angle on the main section. <math display="inline"><semantics> <msub> <mi>D</mi> <mn>0</mn> </msub> </semantics></math> is the inflection point offset. <span class="html-italic">D</span> is the projection distance between the boundary point of the sinking basin and the boundary of said goaf on the horizontal plane.</p> "> Figure 3
<p>The subsidence curve caused by inclined coal mining. <math display="inline"><semantics> <msub> <mi>r</mi> <mn>1</mn> </msub> </semantics></math> is the main influence radius in the downhill direction. <math display="inline"><semantics> <msub> <mi>r</mi> <mn>2</mn> </msub> </semantics></math> is the main influence radius in the uphill direction. <math display="inline"><semantics> <mi>θ</mi> </semantics></math> is the propagation angle of the mining influence. <math display="inline"><semantics> <mi>α</mi> </semantics></math> is the inclination angle of the coal seam.</p> "> Figure 4
<p>Scheme of the inversion of an inclined mined-out area position based on time series surface subsidence and the probability integration method (PIM). InSAR, synthetic aperture radar interferometry.</p> "> Figure 5
<p>Subsidence basin caused by the simulated goaf and comparison with the calculated goaf position.</p> "> Figure 6
<p>The tilt (<b>a</b>) and curvature (<b>b</b>) of the simulated basin in the strike direction. The green line at the bottom of (<b>a</b>) and the yellow line at the bottom of (<b>b</b>) are the positions where the tilt and curvature are 0, respectively.</p> "> Figure 7
<p>Study area. The white quadrilateral in the right picture is the 132,610 working surface, and the purple dots refer to the leveling observations.</p> "> Figure 8
<p>Interferograms.</p> "> Figure 9
<p>Interference pair and time–space baseline. The blue diamonds represent Singe Look Complex ( SLC ) images, the blue lines represent the interference pairs, the solid line represents the interferogram used to calculate the deformation, and the dotted line represents the interference pair that was discarded due to incoherence.</p> "> Figure 10
<p>Time series of ground subsidence in the look of sight (LOS) direction. Each small chart represents the cumulative land subsidence from 15 June 2015 to the date above the chart (YYYYMMDD). A positive value means uplift, and a negative value means sinking.</p> "> Figure 11
<p>Time series sinking and fitting curve in the strike and dip main sections. The observation stations are arranged from north to south in the strike main section, and from east to west in the dip main section.</p> "> Figure 12
<p>A comparison of the calculated and actual goaf positions. The white rectangle is the calculated position, while the black rectangle is the actual position. H1, H2, L1, and L2 refer to the mining depth in the uphill and downhill directions and the length and width of the actual goaf, respectively. h1, h2, l1, and l2 refer to the mining depth in the uphill and downhill directions and the length and width of the calculated goaf, respectively.</p> "> Figure 13
<p>Verification of the InSAR results.</p> ">
Abstract
:1. Introduction
2. Methodology
2.1. Principle of the PIM
2.2. Relationship between Subsidence Curve and Horizontal Working Face Position
2.3. Deformation Propagation Characteristics of an Inclined Coal Seam
2.4. Inversion Method of Mining Goafs
2.4.1. Determination of the Strike Boundary and Average Mining Depth
2.4.2. Determination of the Inclination
2.4.3. Determination of the Dip Boundary
3. Simulation Experiment
3.1. Simulation Experiment
3.2. Simulation Results and Analysis
4. Project Example
4.1. Study Area and Data Sets
4.2. InSAR Processing
4.3. Results
5. Discussion
5.1. Leveling Data Verification
5.2. The Relationship between Mining Depth in the Dip Direction and the Main Influence Radius
5.3. Influence of Insufficient Mining
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Available online: https://www.worldcoal.org/coal/coal-mining (accessed on 25 November 2020).
- Miao, X.X.; Qian, M.G. Research on green mining of coal resources in China: Current status and future prospects. J. Min. Saf. Eng. 2009, 26, 1–14. [Google Scholar]
- Han, R.L.; Nan, S.Q.; Li, F.P.; Guo, X.Z.; Su, M.; Wan, X.J. Application of Micro-seismic Monitoring on Fighting Against Illegal Mining. J. Hebei Polytech. Univ. (Nat. Sci. Ed.) 2011, 4. [Google Scholar]
- Engelbrecht, J.; Inggs, M. Differential interferometry techniques on L-band data employed for the monitoring of surface subsidence due to mining. S. Afr. J. Geomat. 2013, 2, 82–93. [Google Scholar]
- Herrera, G.; Tomás, R.; López-Sánchez, J.M.; Delgado, J.; Mallorqui, J.J.; Duque, S.; Mulas, J. Advanced DInSAR analysis on mining areas: La Union case study (Murcia, SE Spain). Eng. Geol. 2007, 90, 148–159. [Google Scholar] [CrossRef] [Green Version]
- Ishwar, S.G.; Kumar, D. Application of DInSAR in mine surface subsidence monitoring and prediction. Curr. Sci. 2017, 112, 46–51. [Google Scholar] [CrossRef]
- Przyłucka, M.; Herrera, G.; Graniczny, M.; Colombo, D.; Béjar-Pizarro, M. Combination of conventional and advanced DInSAR to monitor very fast mining subsidence with TerraSAR-X data: Bytom City (Poland). Remote Sens. 2015, 7, 5300–5328. [Google Scholar] [CrossRef] [Green Version]
- Yang, Z.; Li, Z.; Zhu, J.; Yi, H.; Feng, G.; Hu, J.; Wu, L.; Preusse, A.; Wang, Y.; Papst, M. Locating and defining underground goaf caused by coal mining from space-borne SAR interferometry. ISPRS J. Photogramm. Remote. Sens. 2018, 135, 112–126. [Google Scholar] [CrossRef]
- Du, S.; Wang, Y.; Zheng, M.; Zhou, D.; Xia, Y. Goaf Locating Based on InSAR and Probability Integration Method. Remote Sens. 2019, 11, 812. [Google Scholar] [CrossRef] [Green Version]
- Litwiniszyn, J. The theories and model research of movements of ground masses. Proceedings of the European congress on ground movement. Univ. Leeds 1957, 202, 209. [Google Scholar]
- Deng, K.Z.; Tan, Z.X.; Jiang, Y. Subject of Deformation Monitoring and Subsidence Engineering; China University of Mining and Technology Press: Xuzhou, China, 2015. [Google Scholar]
- Fan, H.; Lu, L.; Yao, Y. Method combining probability integration model and a small baseline subset for time series monitoring of mining subsidence. Remote Sens. 2018, 10, 1444. [Google Scholar] [CrossRef] [Green Version]
- Ministry of Coal Industry. Regulations for Coal Pillar Retention and Coal Mining in Buildings, Water Bodies, Railways and Main Shafts; Coal Industry Press: Beijing, China, 1986.
- Abbo, A.J.; Sloan, S.W. A smooth hyperbolic approximation to the Mohr-Coulomb yield criterion. Comput. Struct. 1995, 54, 427–441. [Google Scholar] [CrossRef]
- Chen, Q. Analysis of Water Quality Test Results of Ordovician Limestone Water in Fengfeng Mining Area. Occup. Health 2009, 25, 1974. [Google Scholar]
- Nie, C. Research on Comprehensive Planning and Utilization of Water Resources in Fengfeng Mining Area of Handan City; Hebei University of Engineering: Handan, China, 2010. [Google Scholar]
- Du, L.; Zhu, W.; Tian, X. Climatic characteristics analysis of different grades of precipitation days in Handan area. J. Meteorol. Environ. 2017, 33, 102–107. [Google Scholar]
- Liu, Y.; Hu, B.; Zhang, F. Carboniferous-Permian sandstone microstructure in Tonger Minefield, Fengfeng Mining Area, Hebei Province Analysis of structural characteristics. Mod. Geol. 2003, 17, 75–79. [Google Scholar]
- Cao, D.; Zhan, W.; Zhang, J. New structural features of Handan-Fengfeng mining area and its significance for coal resource development. J. China Coal Soc. 2007, 32, 141–145. [Google Scholar]
- Hebei University of Engineering. Jiulong Mine Geology Report; Jizhong Energy Fengfeng Group: Handan, China.
- Lai, S.; Dong, Y. Fengfeng Group’s methods and approaches to increase the recovery rate of coal resources. Coal Mine Saf. 2014, 45, 191–193. [Google Scholar]
- Mora, O.; Mallorqui, J.J.; Broquetas, A. Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images. IEEE Trans. Geosci. Remote Sens. 2003, 41, 2243–2253. [Google Scholar] [CrossRef]
- Blanco-Sanchez, P.; Mallorquí, J.J.; Duque, S.; Monells, D. The coherent pixels technique (CPT): An advanced DInSAR technique for nonlinear deformation monitoring. Pure Appl. Geophys. 2008, 165, 1167–1193. [Google Scholar] [CrossRef]
Country | Coal Extraction (Mt) | Country | Coal Extraction (Mt) |
---|---|---|---|
PR China | 3550 | Russia | 420 |
India | 771 | South Africa | 259 |
USA | 685 | Germany | 169 |
Indonesia | 549 | Poland | 122 |
Australia | 483 | Kazakhstan | 114 |
Strike Direction | Length | Width | Average Depth | Depth in Uphill Direction | Depth in Downhill Direction | Inclination Angle | |
---|---|---|---|---|---|---|---|
Calculated value | 305 m | 311 m | 228 m | 209 m | 247 m | ||
Real value | 300 m | 350 m | 225 m | 200 m | 250 m | ||
Relative error | 1.11% | 1.67% | 11.14% | 1.33% | 4.50% | 1.20% | 1.23% |
Strike Direction | Length | Width | Average Depth | Depth in Uphill Direction | Depth in Downhill Direction | Inclination Angle | |
---|---|---|---|---|---|---|---|
Calculated value | 342 m | 138 m | 759 m | 761 m | 802 m | ||
Real value | 319 m | 3165 m | 774 m | 733 m | 845 m | ||
Relative error | 3.55% | 7.21% | 16.36% | 1.94% | 3.82% | 5.09% | 6.45% |
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Xia, Y.; Wang, Y. InSAR- and PIM-Based Inclined Goaf Determination for Illegal Mining Detection. Remote Sens. 2020, 12, 3884. https://doi.org/10.3390/rs12233884
Xia Y, Wang Y. InSAR- and PIM-Based Inclined Goaf Determination for Illegal Mining Detection. Remote Sensing. 2020; 12(23):3884. https://doi.org/10.3390/rs12233884
Chicago/Turabian StyleXia, Yuanping, and Yunjia Wang. 2020. "InSAR- and PIM-Based Inclined Goaf Determination for Illegal Mining Detection" Remote Sensing 12, no. 23: 3884. https://doi.org/10.3390/rs12233884