Li, J.; Zang, M.; Xu, N.; Mei, G.; Yang, S. An Interferometric-Synthetic-Aperture-Radar-Based Method for Predicting Long-Term Land Subsidence in Goafs through the Concatenation of Multiple Sources of Short-Term Monitoring Data. Remote Sens.2023, 15, 4203.
Li, J.; Zang, M.; Xu, N.; Mei, G.; Yang, S. An Interferometric-Synthetic-Aperture-Radar-Based Method for Predicting Long-Term Land Subsidence in Goafs through the Concatenation of Multiple Sources of Short-Term Monitoring Data. Remote Sens. 2023, 15, 4203.
Li, J.; Zang, M.; Xu, N.; Mei, G.; Yang, S. An Interferometric-Synthetic-Aperture-Radar-Based Method for Predicting Long-Term Land Subsidence in Goafs through the Concatenation of Multiple Sources of Short-Term Monitoring Data. Remote Sens.2023, 15, 4203.
Li, J.; Zang, M.; Xu, N.; Mei, G.; Yang, S. An Interferometric-Synthetic-Aperture-Radar-Based Method for Predicting Long-Term Land Subsidence in Goafs through the Concatenation of Multiple Sources of Short-Term Monitoring Data. Remote Sens. 2023, 15, 4203.
Abstract
The land subsidence occurring in goafs after coal mining is a protracted process. The accurate prediction of long-term land subsidence in goafs relies heavily on the availability of long-term monitoring data. However, the scarcity of continuous long-term land subsidence monitoring data subsequent to the cessation of mining significantly hinders the accurate prediction of long-term land subsidence in goafs. To address this challenge, this study proposes an innovative method based on Interferometric Synthetic Aperture Radar (InSAR) for predicting long-term land subsidence of goafs following coal mining. The proposed method employs a concatenation approach that integrates multiple short-term monitoring data from different coal faces, each with distinct cessation times, into a cohesive and uniform long-term sequence by normalizing the subsidence rates. The method was verified using actual monitoring data from the Yangquan No.2 mine in Shanxi Province, China. Initially, coal faces with same shapes but varying cessation times were selected for analysis. Using InSAR monitoring data collected between June and December of 2016, the average subsidence rate corresponding to the duration after coal mining cessation of each coal face was back-calculated. Subsequently, a function relating subsidence rate to the duration after coal mining cessation was fitted to the data. Finally, the relationship between cumulative subsidence and the duration after coal mining cessation was derived by integrating the function. The results indicated that the relationship between subsidence rate and duration after coal mining cessation followed an exponential function for a given coal face, whereas the relationship between cumulative subsidence and duration after coal mining cessation conformed to the Knothe time function. Notably, after the cessation of coal mining, significant land subsidence persisted in the goaf of the Yangquan No.2 mine for a duration ranging from 5 to 10 years. The cumulative subsidence curve along the long axis of the coal face ultimately exhibited an inclined W-shape. The proposed method enables the quantitative prediction of residual land subsidence in goafs, even in cases where continuous long-term monitoring data are insufficient, thus providing valuable guidance for construction decisions above the goaf.
Keywords
InSAR-based method; coal mine goaf; prediction of long-term land subsidence; concatenation of multiple short-term monitoring data
Subject
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
