ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 13 - 2025 | doi: 10.3389/feart.2025.1593432
This article is part of the Research TopicGeological Hazards in Deep Underground Engineering: Mechanism, Monitoring, Warning, and ControlView all 7 articles
Identifying Hotspots and Classifying the spatial Distribution Pattern of Karst Collapse Pillars with Moran's Index in Coal Mine
Provisionally accepted
Junsheng Yan1,2,3*
Zaibin Liu1,2,3*Hui Yang2,3Lin An1,2,3Wei Li1,2,3Tiantian Wang2,3Qian Xie2,3Chenguang Liu2,4- 1China Coal Research Institute (China), Beijing, China
- 2CCTEG Xi'an Research Institute(Group) Co., Ltd., Xi'an city, China
- 3Xi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi'an city, China
- 4School of Mining Engineering, China University of Mining and Technology, Xuzhou, Jiangsu Province, China
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Identifying hazardous karst collapse pillars (KCPs) is critical for ensuring safe coal mining operations. While previous studies have focused primarily on physical detection, the spatial clustering characteristics of KCPs have often been overlooked. This study proposes a spatial hotspot identification method based on Moran's index and applies it to the Wangpo Coal Mine in Shanxi, China. The method integrates morphological feature analysis of KCPs with a combined weighting scheme using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM). A spatial distribution index (SDI) was constructed through geographic information system (GIS) overlay analysis and spatial coordinate calibration. Global Moran's I (0.1110, p<0.05) indicates a statistically significant positive spatial autocorrelation of KCP distribution. Local Moran's I further reveals 11 spatially significant KCPs, including 5 high-high clusters. Geological interpretation shows that these high-risk KCPs are predominantly located near the intersections of faults and folds, highlighting the structural control on KCP formation. The proposed method provides a quantitative and spatially interpretable approach for KCP risk identification and has potential for application to other geohazards exhibiting spatial aggregation patterns.
Keywords: morphological characteristics, geographic information system, Coordinate calibration, Spatial distribution index, Development patterns, 4 1, 2, 3
Received: 14 Mar 2025; Accepted: 05 Aug 2025.
Copyright: © 2025 Yan, Liu, Yang, An, Li, Wang, Xie and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Junsheng Yan, China Coal Research Institute (China), Beijing, China
Zaibin Liu, CCTEG Xi'an Research Institute(Group) Co., Ltd., Xi'an city, China
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