Abstract
The Xinjiang is an important coal production base in China and also a serious coal fire disaster area. Coal fires not only waste resources, but also cause air pollution and damage to the ecological environment. Hence, it is very important to identify and monitor the underground coal fire areas accurately and efficiently for the control of coal fires. Interferometric synthetic aperture radar (InSAR) technology identifies and monitors coal fire areas by monitoring surface subsidence caused by burned out area. Compared with traditional coal fire monitoring technology, InSAR technology has the advantages of all-weather and high efficiency. But the fire areas are often distributed in wild areas, this factor significantly limits the application of the traditional Persistent Scatterer interferometry (PSI) technology. In addition, Xinjiang coal fires are mostly located in historical goafs, so it is necessary to distinguish the subsidence caused by mining and coal fires. Therefore, distributed scatterer interferometry (DSI) technology is used to monitor the Miquan fire area in Xinjiang in this paper. The results show that compared with PSI technology, DSI technology can expand the number of effective monitoring points 124 times. On this basis, spatio-temporal analysis of surface subsidence in the study area suggests that the subsidence caused by mining and coal fires exhibits significantly different space-time evolution rules. Therefore, in the future, the coal fire area and mining area can be separated and identified according to these rules. The final identified coal fire area contains all measured coal fire points, and accurately monitors the fire extinguishing area.
Highlights
Xinjiang is one of the coal-rich areas in China, but it is a serious area of coal fire hazards [1]
In this paper, a coal fire area identification and monitoring method based on distributed scatterer interferometry (DSI) technology is proposed
In order to make full use of distributed scatterers (DS) for interferometry, the two-sample t-test and the coherence matrix eigenvalue decomposition (EVD) combination method were used for statistically homogeneous pixel (SHP) identification and phase optimization, respectively
Summary
Xinjiang is one of the coal-rich areas in China, but it is a serious area of coal fire hazards [1]. Eight enormous coal fire zones were identified in Xinjiang, north of the Altay grassland and south of the Pamirs where coal fires have occurred, and the annual fire areas continue to increase. This is because there are many small abandoned coal kilns and mines. In Xinjiang, which cause many cracks and fissures. Coal fires will cause a lot of waste of resources and environmental pollution. The combustion space area generated by coal combustion will change the balance of overlying rock stress. Once gravity of rock layer is likely to be larger than the overlying rock stress such that subsidence may
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