Prediction of floor water disasters based on fractal analysis of geologic structure and vulnerability index method for deep coal mining in the Yanzhou mining area

Floor water-inrush incidents in coal mines in China are major problems which pose a severe threat to the daily safety of mining operations. The traditional prediction approach used for the past 50 years is the water-inrush coefficient method (WCM), which only considers two factors and cannot be appl...

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Bibliographic Details
Main Authors: Wei Qiao, Wenping Li, Xin Zhang, Yunfei Niu, Youkuo Chen, Yangzhou Wang, Tao Xing
Format: Article
Language:English
Published: Taylor & Francis Group 2019-01-01
Series:Geomatics, Natural Hazards & Risk
Subjects:
Online Access:http://dx.doi.org/10.1080/19475705.2019.1574911
Description
Summary:Floor water-inrush incidents in coal mines in China are major problems which pose a severe threat to the daily safety of mining operations. The traditional prediction approach used for the past 50 years is the water-inrush coefficient method (WCM), which only considers two factors and cannot be applied to deep coal mining. Coal floor water inrush is controlled by many factors. Based on data collection and fractal analysis of geological structures in the Yanzhou mining area, this study proposed a vulnerability index method by integrating two tools: the analytic hierarchy process (AHP) and geographic information systems (GIS). The proposed methods include: water inrush factor analysis, fractal analysis, establishment of thematic maps of each factor, normalization of thematic maps, threshold value determination using AHP, the vulnerability index method (VIM), and the demonstration of final prediction results. This study also analyzed 314 examples of coal floor water inrush disasters and a prediction line was determined for the WCM to develop an Integrated Prediction Method (IPM). It was found that the results from the vulnerability method and IPM were very different and the vulnerability method results were more similar to actual mining conditions.
ISSN:1947-5705
1947-5713