An Approach for Estimating Underground-Goaf Boundaries Based on Combining DInSAR with a Graphical Method
Autor: | Fang Jun, Bu Pu, Yang Wentao, Li Chaokui, Liao Mengguang, Chuanguang Zhu |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Synthetic aperture radar
010504 meteorology & atmospheric sciences Article Subject 0211 other engineering and technologies Boundary (topology) Subsidence 02 engineering and technology Left behind Engineering (General). Civil engineering (General) 01 natural sciences Mining engineering Geological disaster Range (statistics) TA1-2040 Geology 021101 geological & geomatics engineering 0105 earth and related environmental sciences Civil and Structural Engineering |
Zdroj: | Advances in Civil Engineering, Vol 2020 (2020) |
ISSN: | 1687-8086 |
DOI: | 10.1155/2020/9375056 |
Popis: | The goaf left behind after mining has the potential to induce serious geological disasters due to the damaged internal structure of the rock. Estimating the boundary of the underground goaf can effectively control the occurrence of such disasters. However, traditional geophysical methods are inefficient and expensive and are particularly difficult to apply for a wide detection range. This paper proposes a new method for estimating the boundary of underground goaf using the differential interference synthetic aperture radar technique (DInSAR). More specifically, DInSAR is used to obtain the isoline of the subsidence basin above the goaf, and the direction of the two main sections of the goaf is then determined according to the basic law of mining subsidence. Following this, the basic principles of the probability integral and the graphical methods are combined to determine the mining boundary of the strike section and the incline section of the goaf. Finally, six geometric parameters reflecting the boundary of the goaf are obtained. Experiments on simulated and measured data indicate that the proposed method is feasible, with the average relative errors of the simulated and measured data reaching and maintained at 2.2% and 3.7%, respectively. |
Databáze: | OpenAIRE |
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