ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Geohazards and Georisks
This article is part of the Research TopicNatural Hazards Accompanying Underground Exploitation of Mineral Raw MaterialsView all 11 articles
Research on Deformation Prediction Method for Mining-Induced Overburden in Coal Mines Based on BP Neural Network
Provisionally accepted
- 1Shenhua Geological & Exploration Company Ltd., China Energy Group, Beijing, China
- 2China University of Mining and Technology, Xuzhou, China
- 3Key Laboratory of Ground Fissures Geological Hazards (Jiangsu Research Institute of Geological Survey), Nanjing, China
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Coal mining triggers the initiation, propagation, and coalescence of fractures in overburden strata, which can readily induce geological disasters such as mine water inrush and surface subsidence. This study employed distributed optical fiber sensing (DOFS) technology to capture strain distribution curves in overburden strata during coal extraction via physical similarity modeling of Longwall Face 22107 at Jinfeng Cuncaota Coal Mine. Based on the geological conditions of the overburden and coal mining parameters, seven key factors influencing mining-induced deformation were identified. A neural network architecture was constructed to establish a prediction model for overburden deformation states using measured strain data. Results indicate that the distribution of the caved and fractured zones is closely related to the positions of the main roof and key strata. Predictions from the backpropagation neural network (BPNN) align well with measured strain values, achieving a coefficient of determination (R²) greater than 0.9 and a root mean square error (RMSE) below 10%, demonstrating high applicability. These findings validate the feasibility of integrating DOFS with BPNN for predicting mining-induced overburden deformation.
Keywords: Mining-induced overburden, Distributed monitoring, Deformation prediction, BackpropagationNeural Network (BPNN), Coal mine
Received: 03 Nov 2025; Accepted: 28 Nov 2025.
Copyright: © 2025 Li, Piao, Yin, Lu, Liang and Du. 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: Chunde Piao
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