Prediction of concealed faults in front of a coalface using feature learning
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ORIGINAL PAPER
Prediction of concealed faults in front of a coalface using feature learning Qiang Wu 1,2 & Zhichao Hao 1,2 & Yingwang Zhao 1,2 & Hua Xu 3 Received: 6 July 2019 / Accepted: 8 April 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The existence of concealed faults not only decreases the production efficiency of a coal mine but also wastes resources and increases the risk of mine disasters. In this study, a method was developed to predict concealed faults in front of a coalface. The spatial distribution law of faults developed in the study area was characterized using the locations and attributes of fault zones, which can be determined by learning the strikes and locations of the faults with the K-means algorithm. Then, the concealed faults in front of coalfaces can be predicted by extending the fault zones along their strikes to unmined areas within the study area. Three attributes of fault zones, including extending index, buffer radius, and average throw, were defined and calculated to provide a quantitative evaluation of prediction results. The extending index represented the existence probability of the predicted fault. The buffer radius denoted the possible offset of the actual exposure point relative to the predicted location. The average throw gave the throw of the predicted fault. The method could also provide dynamic prediction as mining works were going on. Finally, the method was applied in mining region 302 of the Yanzishan Coal Mine in north China, and it was illustrated to be effective. In the test, the faults successfully predicted accounted for 82%, 89% of which was located within the range of buffer radius and also 89% had throw errors less than 50%. Keywords Fault zone . Feature learning . Concealed fault . Dynamic prediction
Introduction Faults are the most common geological structures in coal mines. They not only waste coal resources but also directly lead to mine disasters, such as roof collapses, gas outbursts, and water inrushes (Zhang et al. 2019). Large faults, with a throw of more than 20 m, are typically found at the exploration stage and are used as the boundary for defining the mining region. However, small faults are difficult to identify completely under the present technical conditions of exploration and are mainly exposed during the production process. Therefore, the prediction of small structures, especially faults with a throw of less than 10 m, has
* Zhichao Hao [email protected] 1
College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing, China
2
National Engineering Research Center of Coal Mine Water Hazard Controlling, Beijing, China
3
Information Engineering College, Beijing Institute of Petrochemical Technology, Beijing, China
long been the focus of mine geological research (Wu et al. 2008). Effective fault prediction would improve production efficiency and enable the avoidance of mine disasters. The most common practices used to find out concealed faults ahead of coalfaces
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