| CIVIL ENGINEERING |
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Inversion method of stress fields in the discontinuous zone of deep coal seam |
| ZHOU Jiaxing1,2,3, WANG Jin-an4,5, LI Fei4,5 |
1. Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China;
2. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China;
3. Key Laboratory of Hydrosphere Sciences of the Ministry of Water Resources, Tsinghua University, Beijing 100084, China;
4. School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China;
5. Key Laboratory of Ministry for Efficient Mining and Safety of Metal Mines, University of Science and Technology Beijing, Beijing 100083, China |
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Abstract [Objective] As the depth of coal mining increases, the activation of discontinuous structures, such as faults, poses a significant risk to the safe and efficient mining of coal seams. Therefore, acquiring precise knowledge of the distribution of in-situ stress is paramount for the design, construction, and disaster prevention of mining engineering. [Methods] This study proposes an inversion method for in-situ fields applicable to discontinuous zones of deep coal seams. (1) Given the discontinuity characteristics of the deep in-situ stress field, stability discriminants for normal faults and stability discriminant equations for positive faults, reverse faults, and strike-slip fault zones are derived based on the lateral pressure coefficients of in-situ stress. (2) A long short-term memory neural network algorithm is adopted to optimize the learning of the in-situ stress field data formed in different periods sequentially to effectively solve the nonlinearity, discreteness, and multi-noise problems of the measured deep in-situ stress data and to ensure that the excellent in-situ stress data information is remembered for a long time and that the inferior in-situ stress data information is forgotten in time. [Results] This study considers the main and auxiliary well areas of Yingjun's second mining area in Shanghai Miao as the research background and establishes an algorithm model for long short-term memory neural networks. Given the distribution characteristics of the in-situ stress field in fault areas at different scales, an inversion calculation of the in-situ stress field in discontinuous areas of deep coal seams was conducted. [Conclusions] The correlation coefficient between inverted and measured stress fields was 0.945, with an average error of 12.897%. The standard deviation of the stress difference is 2.000. The amount and direction of the regional stress field of the fault will also change. Compared with the regional stress field, the in-situ stress field in the DF15 and SF15 large-scale fault zones is approximately 5 MPa lower, and the counterclockwise direction is deflected. The in situ stress in the surrounding rock area at the top and bottom of the coal seams adjacent to DF15 and SF15 large-scale fault zones is relatively small, and no stress concentration area is detected. The eighth overlying coal seam was tilted toward horizontal in-situ stress by extrusion during deposition, and a concentration of in-situ stress was detected on the top rock. Therefore, for deep coal seam mining in the well field, the protective-layer mining method can be adopted, i.e., the lower 15 coals can be mined first to provide protection and pressure relief for the mining of the upper 8 coals to ensure the safety and reliability of deep-mining in the well field. Folds mainly control the distribution of the horizontal in-situ stress field, and the in-situ stress of the rock mass in the axial part of the backslope and the inner arc increases, while the in-situ stress of the outer arc of the obliquity is relatively small. Therefore, the inversion method proposed in this study can offer a new perspective for reconstructing the stress fields in deep discontinuous areas.
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| Keywords
deep coal seams
discontinuous
in-situ stress field
inversion method
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Issue Date: 22 November 2024
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[1] 谢和平. 深部岩体力学与开采理论研究进展[J]. 煤炭学报, 2019, 44(5): 1283-1305. XIE H P. Research review of the state key research development program of China: Deep rock mechanics and mining theory [J]. Journal of China Coal Society, 2019, 44(5): 1283-1305. (in Chinese)
[2] 谢和平, 李存宝, 高明忠, 等. 深部原位岩石力学构想与初步探索[J]. 岩石力学与工程学报, 2021, 40(2): 217-232. XIE H P, LI C B, GAO M Z, et al. Conceptualization and preliminary research on deep in situ rock mechanics [J]. Chinese Journal of Rock Mechanics and Engineering, 2021, 40(2): 217-232. (in Chinese)
[3] 周家兴. 基于深度学习的深部复杂地应力场反演算法研究[D]. 北京: 北京科技大学, 2022. ZHOU J X. Research on inversion algorithm of deep complex in-situ stress field based on depth learning [D]. Beijing: University of Science and Technology Beijing, 2022. (in Chinese)
[4] 薛成春, 曹安业, 牛风卫, 等. 深部不规则孤岛煤柱区冲击地压机理及防治[J]. 采矿与安全工程学报, 2021, 38(3): 479-486. XUE C C, CAO A Y, NIU F W, et al. Mechanism and prevention of rock burst in deep irregular isolated coal pillar [J]. Journal of Mining & Safety Engineering, 2021, 38(3): 479-486. (in Chinese)
[5] 李飞, 周家兴, 王金安. 深部多场耦合作用的非线性地应力构建方法[J]. 煤炭学报, 2021, 46(S1): 116-129. LI F, ZHOU J X, WANG J A. Method of nonlinear in-situ stress construction with deep multi-field coupling [J]. Journal of China Coal Society, 2021, 46(S1): 116-129. (in Chinese)
[6] 贺虎. 煤矿覆岩空间结构演化与诱冲机制研究[J]. 煤炭学报, 2012, 37(7): 1245-1246. HE H. Research on the evolution mechanism of spatial structure of overlying strata and rockburst inducing in coal mine [J]. Journal of China Coal Society, 2012, 37(7): 1245-1246. (in Chinese)
[7] 韩科明, 于秋鸽, 张华兴, 等. 上下盘开采影响下断层滑移失稳力学机制[J]. 煤炭学报, 2020, 45(4): 1327-1335. HAN K M, YU Q G, ZHANG H X, et al. Mechanism of fault activation when mining on hanging-wall and foot-wall [J]. Journal of China Coal Society, 2020, 45(4): 1327-1335. (in Chinese)
[8] 高明忠, 王明耀, 谢晶, 等. 深部煤岩原位扰动力学行为研究[J]. 煤炭学报, 2020, 45(8): 2691-2703. GAO M Z, WANG M Y, XIE J, et al. In-situ disturbed mechanical behavior of deep coal rock [J]. Journal of China Coal Society, 2020, 45(8): 2691-2703. (in Chinese)
[9] 周家兴, 王金安, 李飞. 深部工程岩体温度与渗流耦合作用下复杂应力场反演方法[J/OL]. 工程力学. DOI: 10.6052/j.issn.1000-4750.2022.05.0495. ZHOU J X, WANG J A, LI F. Inversion method for complex stress field under coupling effect of temperature and seepage in deep engineering rock mass [J/OL]. Engineering Mechanics. DOI: 10.6052/j.issn.1000-4750.2022.05.0495. (in Chinese)
[10] 李飞, 周家兴, 王金安. 基于稀少样本数据的地应力场反演重构方法[J]. 煤炭学报, 2019, 44(5): 1421-1431. LI F, ZHOU J X, WANG J A. Back-analysis and reconstruction method of in-situ stress field based on limited sample data [J]. Journal of China Coal Society, 2019, 44(5): 1421-1431. (in Chinese)
[11] 王金安, 李飞. 复杂地应力场反演优化算法及研究新进展[J]. 中国矿业大学学报, 2015, 44(2): 189-205. WANG J A, LI F. Review of inverse optimal algorithm of in-situ stress filed and new achievement [J]. Journal of China University of Mining & Technology, 2015, 44(2): 189-205. (in Chinese)
[12] 刘泉声, 王栋, 朱元广, 等. 支持向量回归算法在地应力场反演中的应用[J]. 岩土力学, 2020, 41(S1): 319-328. LIU Q S, WANG D, ZHU Y G, et al. Application of support vector regression algorithm in inversion of geostress field [J]. Rock and Soil Mechanics, 2020, 41(S1): 319-328. (in Chinese)
[13] 陈世杰, 肖明, 陈俊涛, 等. 断层对地应力场方向的扰动规律及反演分析方法[J]. 岩石力学与工程学报, 2020, 39(7): 1434-1444. CHEN S J, XIAO M, CHEN J T, et al. Disturbance law of faults to in-situ stress field directions and its inversion analysis method [J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(7): 1434-1444. (in Chinese)
[14] 裴启涛, 丁秀丽, 卢波, 等. 考虑地应力分布形式的坝址区初始应力场二次反演方法[J]. 岩土力学, 2016, 37(10): 2961-2970. PEI Q T, DING X L, LU B, et al. Two-stage back analysis method of initial geostress field in dam areas considering distribution characteristics of geostress [J]. Rock and Soil Mechanics, 2016, 37(10): 2961-2970. (in Chinese)
[15] 陈爽, 白根铭, 肖畅, 等. 断层构造对高地应力隧道初始地应力场影响分析[J]. 中国安全科学学报, 2023, 33(2): 146-151. CHEN S, BAI G M, XIAO C, et al. Analysis of influence of fault structure on initial ground stress field of high ground stress tunnel [J]. China Safety Science Journal, 2023, 33(2): 146-151. (in Chinese)
[16] 寇昊, 何川, 吴枋胤, 等. 考虑走滑断裂活动影响的公路隧道初始地应力场反演分析[J]. 中国公路学报, 2022, 35(9): 321-330. KOU H, HE C, WU F Y, et al. Inversion analysis of initial geostress field of highway tunnel considering influence of strike-slip fault activity [J]. China Journal of Highway and Transport, 2022, 35(9): 321-330. (in Chinese)
[17] 吕进国, 姜耀东, 李守国, 等. 巨厚坚硬顶板条件下断层诱冲特征及机制[J]. 煤炭学报, 2014, 39(10): 1961-1969. LÜ J G, JIANG Y D, LI S G, et al. Characteristics and mechanism research of coal bumps induced by faults based on extra thick and hard roof [J]. Journal of China Coal Society, 2014, 39(10): 1961-1969. (in Chinese)
[18] 安欧. 构造应力场[M]. 2版. 北京: 地震出版社, 2021. AN O. Structural stress field [M]. 2nd ed. Beijing: Seismological Press, 2021. (in Chinese)
[19] 周勇. 弱胶结极松软地层井巷工程围岩稳定性及控制技术研究[D]. 北京: 北京科技大学, 2022. ZHOU Y. Study on stability and control technology of tunnel surrounding rock in weak cementation and extremely soft stratum [D]. Beijing: University of Science and Technology Beijing, 2022. (in Chinese)
[20] 李鹏, 郭奇峰, 苗胜军, 等. 浅部和深部工程区地应力场及断裂稳定性比较[J]. 哈尔滨工业大学学报, 2017, 49(9): 10-16. LI P, GUO Q F, MIAO S J, et al. Comparisons of in-situ stress fields and stability of faults in shallow and deep engineering areas [J]. Journal of Harbin Institute of Technology, 2017, 49(9): 10-16. (in Chinese)
[21] 林远东, 涂敏, 付宝杰, 等. 断层自锁与活化的力学机理及稳定性控制[J]. 采矿与安全工程学报, 2019, 36(5): 898-905. LIN Y D, TU M, FU B J, et al. Mechanical mechanisms of fault self-locking and activation and its stability control [J]. Journal of Mining & Safety Engineering, 2019, 36(5): 898-905. (in Chinese)
[22] 刘培德. 泛函分析基础[M]. 北京: 科学出版社, 2006. LIU P D. Fundamentals of generalized functional analysis [M]. Beijing: Science Press, 2006. (in Chinese)
[23] CAI T, DENG Z W, PARK Y, et al. Acquisition of kHz-frequency two-dimensional surface temperature field using phosphor thermometry and proper orthogonal decomposition assisted long short-term memory neural networks [J]. International Journal of Heat and Mass Transfer, 2021, 165: 120662.
[24] 尹学振, 赵慧, 赵俊保, 等. 多神经网络协作的军事领域命名实体识别[J]. 清华大学学报(自然科学版), 2020, 60(8): 648-655. YIN X Z, ZHAO H, ZHAO J B, et al. Multi-neural network collaboration for Chinese military named entity recognition [J]. Journal of Tsinghua University (Science and Technology), 2020, 60(8): 648-655. (in Chinese)
[25] 邓建华. 深度学习: 原理、 模型与实践[M]. 北京: 人民邮电出版社, 2021. DENG J H. Deep learning: Principles, models and practice [M]. Beijing: Posts & Telecom Press, 2021. (in Chinese) |
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2024,
Vol. 64
