Year
Month
(Peer-Reviewed) Risk assessment of fault water inrush during deep mining
Zhaodan Cao ¹ ², Qixiong Gu 古启雄 ³, Zhen Huang 黄震 ³, Jiaju Fu ⁴
¹ College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
中国 杭州 浙江大学建筑工程学院
² ZJU-UIUC Institute, Zhejiang University, Haining 314400, China
中国 海宁 浙江大学伊利诺伊大学厄巴纳香槟校区联合学院
³ School of Resources and Environment Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
中国 赣州 江西理工大学资源与环境工程学院
⁴ Guiyang Architectural Design and Surveying Prospecting CO. LTD, Guiyang 550082, China
中国 贵阳 贵阳建筑勘察设计有限公司
Abstract

With the gradual depletion of shallow coal resources, the Yanzhou mine in China will enter the lower coal seam mining phase. However, as mining depth increases, lower coal seam mining in Yanzhou is threatened by water inrush in the Benxi Formation limestone and Ordovician limestone.

The existing prediction models for the water burst at the bottom of the coal seam are less accurate than expected owing to various controlling factors and their intrinsic links. By analyzing the hydrogeological exploration data of the Baodian lower seam and combining the results of the water inrush coefficient method and the Yanzhou mine pressure seepage test, an evaluation model of the seepage barrier capacity of the fault was established.

The evaluation results show the water of the underlying limestone aquifer in the Baodian mine area mainly threatens the lower coal mining through the fault fracture zone. The security of mining above confined aquifer in the Baodian mine area gradually decreases from southwest to northeast.

By comparing the water inrush coefficient method and the evaluation model of fault impermeability, the results show the evaluation model based on seepage barrier conditions is closer to the actual situation when analyzing the water breakout situation at the working face.
Risk assessment of fault water inrush during deep mining_1
Risk assessment of fault water inrush during deep mining_2
Risk assessment of fault water inrush during deep mining_3
Risk assessment of fault water inrush during deep mining_4
  • Overcoming challenges in InP-based quantum dots: from nucleation mechanisms to high-performance quantum dot light-emitting diodes
  • Yangyang Bian, Qian Li, Fei Chen, Chunhe Yang, Huaibin Shen, Aiwei Tang
  • Opto-Electronic Advances
  • 2026-03-25
  • Emerging landscape of photonic bound states in the continuum for next-generation metadevices
  • Thi Thu Ha Do, Ronghui Lin, Daniil A. Shilkin, Zhiyi Yuan, Cuong Dang, Arseniy I. Kuznetsov, Jinghua Teng, Son Tung Ha
  • Opto-Electronic Advances
  • 2026-03-25
  • A 4096-element 3D-integrated Si-SiN optical phased array for high-power coherent LiDAR
  • Han Wang, Weimin Xie, Xin Yan, Jiaqi Li, Youxi Lu, Ping Jiang, Feng Li, Kai Jin, Xu Yang, Jiali Jiang, Keran Deng, Weishuai Chen, Jing Luo, Li Jin, Junbo Feng, Kai Wei
  • Opto-Electronic Technology
  • 2026-03-20
  • Multi-scale attention residual deep convolutional dealiasing network-assisted unambiguous ultra-long baseline high-precision microwave photonic angle of arrival estimation
  • Xianglin Chen, Yin Li, Shiru Song, Yalin Yao, He Cui, Xuan Li, Zhe Guo, Yinlong Tan, Taolin Liu, Tian Jiang
  • Opto-Electronic Technology
  • 2026-03-20
  • Dual quasi-BIC resonances synergized laser cooling in halide perovskite metasurface
  • Ying Che, Peng Lu, Yang Li, Junhao Zeng, Mengxia Hu, Fei Qin, Tianyue Zhang Xiangping Li
  • Opto-Electronic Technology
  • 2026-03-20
  • High-speed and large-capacity visible light communication for 6G: advances and perspectives
  • Nan Chi, Zhilan Lu, Fujie Li, Haoyu Zhang, Yunkai Wang, Xinyi Liu, Zhiwu Chen, Zhe Feng, Zhuoran Hu, Zhixue He, Ziwei Li, Chao Shen, Junwen Zhang
  • Opto-Electronic Technology
  • 2026-03-20
  • Multi-dimensional photodetection: from material intrinsic properties and metasurface engineering to silicon photonic integration
  • Wenqi Liu, Zilan Tang, Qingzhao Hua, Liang Liu, Xiaoxia Wang, Anlian Pan
  • Opto-Electronic Technology
  • 2026-03-20
  • Holotomography-driven learning unlocks in-silico staining of single cells in flow cytometry by avoiding fluorescence co-registration
  • Daniele Pirone, Giusy Giugliano, Michela Schiavo, Annalaura Montella, Martina Mugnano, Vincenza Cerbone, Maddalena Raia, Giulia Scalia Ivana Kurelac, Diego Luis Medina, Lisa Miccio Mario Capasso, Achille Iolascon, Pasquale Memmolo, Pietro Ferraro
  • Opto-Electronic Science
  • 2026-02-25
  • Narrow beam and low-sidelobe electro-optic beam steering on thin-film lithium niobate optical phased array
  • Yang Li, Shiyao Deng, Xiao Ma, Ziliang Fang, Shufeng Li Weikang Xu, Fangheng Fu, Xu Ouyang, Yuming Wei, Tiefeng Yang Heyuan Guan, Huihui Lu
  • Opto-Electronic Science
  • 2026-02-25
  • Scene-level passive polarization 3D imaging
  • Xin Wang, Pingli Han, Xiyuan Luo, Qianqian Liu, Tong Zhang, Xue Dong, Meng Xiang, Jinpeng Liu, Yanyan Liu, Fei Liu
  • Opto-Electronic Advances
  • 2026-02-12
  • Modelling-guided inverse design strategy for semitransparent perovskite photovoltaics with customized colors
  • Seok-Beom Seo, Rira Kang, Eun-Joo Lee, So-Yeon Ju, Min Jae Lee, Byunghong Lee, Sun-Kyung Kim
  • Opto-Electronic Advances
  • 2026-02-12
  • A hybrid integrated high-precision tunable semiconductor laser
  • Yiran Zhu, Botao Fu, Zhiwei Fang, Qiyue Hu, Jianping Yu, Yunpeng Song, Yu Ma, Min Wang, Kunpeng Jia, Zhenda Xie, Ya Cheng
  • Opto-Electronic Advances
  • 2026-02-12



  • Artificial intelligence CT helps evaluate the severity of COVID-19 patients: A retrospective study                                Genetic-algorithm-based artificial intelligence control of a turbulent boundary layer
    About
    |
    Contact
    |
    Copyright © PubCard