Identifying hotspots and classifying the spatial distribution pattern of karst collapse pillars with Moran’s index in coal mine
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 identificati...
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| Format: | Article |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1593432/full |
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| author | Junsheng Yan Junsheng Yan Junsheng Yan Zaibin Liu Zaibin Liu Zaibin Liu Zaibin Liu Hui Yang Hui Yang Hui Yang Lin An Lin An Lin An Wei Li Tiantian Wang Qian Xie Qian Xie Qian Xie Chenguang Liu Chenguang Liu Chenguang Liu Chenguang Liu |
| author_facet | Junsheng Yan Junsheng Yan Junsheng Yan Zaibin Liu Zaibin Liu Zaibin Liu Zaibin Liu Hui Yang Hui Yang Hui Yang Lin An Lin An Lin An Wei Li Tiantian Wang Qian Xie Qian Xie Qian Xie Chenguang Liu Chenguang Liu Chenguang Liu Chenguang Liu |
| author_sort | Junsheng Yan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8c81464bd63a4246802187638765ea85 |
| institution | Kabale University |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-8c81464bd63a4246802187638765ea852025-08-26T04:13:07ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-08-011310.3389/feart.2025.15934321593432Identifying hotspots and classifying the spatial distribution pattern of karst collapse pillars with Moran’s index in coal mineJunsheng Yan0Junsheng Yan1Junsheng Yan2Zaibin Liu3Zaibin Liu4Zaibin Liu5Zaibin Liu6Hui Yang7Hui Yang8Hui Yang9Lin An10Lin An11Lin An12Wei Li13Tiantian Wang14Qian Xie15Qian Xie16Qian Xie17Chenguang Liu18Chenguang Liu19Chenguang Liu20Chenguang Liu21Xi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an, ChinaXi’an CCTEG Transparent Geology Technology Co. Ltd., Xi’an, ChinaState Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Beijing, ChinaXi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an, ChinaXi’an CCTEG Transparent Geology Technology Co. Ltd., Xi’an, ChinaState Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Beijing, ChinaChina Coal Research Institute, Beijing, ChinaXi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an, ChinaXi’an CCTEG Transparent Geology Technology Co. Ltd., Xi’an, ChinaState Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Beijing, ChinaXi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an, ChinaXi’an CCTEG Transparent Geology Technology Co. Ltd., Xi’an, ChinaState Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Beijing, ChinaChina Coal Research Institute, Beijing, ChinaXi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an, ChinaXi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an, ChinaXi’an CCTEG Transparent Geology Technology Co. Ltd., Xi’an, ChinaState Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Beijing, ChinaXi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an, ChinaXi’an CCTEG Transparent Geology Technology Co. Ltd., Xi’an, ChinaState Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Beijing, ChinaChina University of Mining and Technology, Xuzhou, ChinaIdentifying 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.https://www.frontiersin.org/articles/10.3389/feart.2025.1593432/fullmorphological characteristicsgeographic information systemcoordinate calibrationspatial distribution indexdevelopment patterns |
| spellingShingle | Junsheng Yan Junsheng Yan Junsheng Yan Zaibin Liu Zaibin Liu Zaibin Liu Zaibin Liu Hui Yang Hui Yang Hui Yang Lin An Lin An Lin An Wei Li Tiantian Wang Qian Xie Qian Xie Qian Xie Chenguang Liu Chenguang Liu Chenguang Liu Chenguang Liu Identifying hotspots and classifying the spatial distribution pattern of karst collapse pillars with Moran’s index in coal mine Frontiers in Earth Science morphological characteristics geographic information system coordinate calibration spatial distribution index development patterns |
| title | Identifying hotspots and classifying the spatial distribution pattern of karst collapse pillars with Moran’s index in coal mine |
| title_full | Identifying hotspots and classifying the spatial distribution pattern of karst collapse pillars with Moran’s index in coal mine |
| title_fullStr | Identifying hotspots and classifying the spatial distribution pattern of karst collapse pillars with Moran’s index in coal mine |
| title_full_unstemmed | Identifying hotspots and classifying the spatial distribution pattern of karst collapse pillars with Moran’s index in coal mine |
| title_short | Identifying hotspots and classifying the spatial distribution pattern of karst collapse pillars with Moran’s index in coal mine |
| title_sort | identifying hotspots and classifying the spatial distribution pattern of karst collapse pillars with moran s index in coal mine |
| topic | morphological characteristics geographic information system coordinate calibration spatial distribution index development patterns |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1593432/full |
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