Abstract
Large-scale and high-intensity mining underground coal has resulted in serious land subsidence. It has caused a lot of ecological environment problems and has a serious impact on the sustainable development of economy. Land subsidence cannot be accurately monitored by InSAR (interferometric synthetic aperture radar) due to the low coherence in the mining area, excessive deformation gradient, and the atmospheric effect. In order to solve this problem, a novel phase unwrapping method based on U-Net convolutional neural network was constructed. Firstly, the U-Net convolutional neural network is used to extract edge to automatically obtain the boundary information of the interferometric fringes in the region of subsidence basin. Secondly, an edge-linking algorithm is constructed based on edge growth and predictive search. The interrupted interferometric fringes are connected automatically. The whole and continuous edges of interferometric fringes are obtained. Finally, the correct phase unwrapping results are obtained according to the principle of phase unwrapping and the wrap-count (integer jump of 2π) at each pixel by edge detection. The Huaibei Coalfield in China was taken as the study area. The real interferograms from D-InSAR (differential interferometric synthetic aperture radar) processing used Sentinel-1A data which were used to verify the performance of the new method. Subsidence basins with clear interferometric fringes, interrupted interferometric fringes, and confused interferometric fringes are selected for experiments. The results were compared with the other methods, such as MCF (minimum cost flow) method. The tests showed that the new method based on U-Net convolutional neural network can resolve the problem that is difficult to obtain the correct unwrapping phase due to interrupted or partially confused interferometric fringes caused by low coherence or other reasons in the coal mining area. Hence, the new method can help to accurately monitor the subsidence in mining areas under different conditions using InSAR technology.
Highlights
The coal industry is a significant part of China’s energy system
In order to evaluate the performance of the new method, some interferograms including the whole mining subsidence basins obtained by Sentinel-1A data were used
In order to solve the problem, a novel method based on the edge extraction using U-Net convolutional neural network was proposed
Summary
The coal industry is a significant part of China’s energy system. On the one hand, coal mining has promoted the rapid development of China’s economy and made great contributions to social development (Fan et al, 2014; Chen et al, 2020a). With the development of geodetic surveying technology, more and more new technologies are applied to monitor the mining subsidence (Zhou et al, 2009; Xia et al, 2018; Chen et al, 2020a). Among those technologies, as a research hotspot, InSAR technology can monitor large area deformation with allweather imaging capability and day/night data acquisition (Du et al, 2016; Ma et al, 2016; Ng et al, 2017; Yang et al, 2018; Zheng et al, 2018). It can be seen that InSAR technology has become a new technical means of mining subsidence monitoring
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