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深部煤层非连续区地应力场反演方法

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随着煤矿开采深度的增加,断层等非连续构造活化会对煤层安全高效开采带来巨大隐患,掌握精确的地应力场分布对于开采工程设计与施工、防灾减灾具有重要的指导意义.针对深部地应力场的非线性及非连续性特征,推导了正断层、逆断层和走滑断层区域地应力侧压力系数的稳定性判别式,并基于长短时记忆神经网络算法,提出了深部煤层非连续区的地应力场反演方法.该方法将不同时期的地应力资料作为时间序列进行优化学习,有效解决了深部实测地应力数据的非线性、离散性、多噪声等问题,确保了优良地应力数据信息长期记忆,不良地应力数据信息及时遗忘.以上海庙矿区鹰骏二号井田主副井区域为例,考虑了不同尺度断层的地应力场分布特征,反演得到详尽的地应力场分布.反演与实测地应力场的相关系数为0.945,平均误差为12.897%,地应力差值的标准差为2.000.因此,该方法可为深部煤层非连续区地应力场反演提供新的思路.
Inversion method of stress fields in the discontinuous zone of deep coal seam
[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.

deep coal seamsdiscontinuousin-situ stress fieldinversion method

周家兴、王金安、李飞

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清华大学水利水电工程系,北京 100084

清华大学水圈科学与水利工程全国重点实验室,北京 100084

清华大学水利部水圈科学重点实验室,北京 100084

北京科技大学土木与资源工程学院,北京 100083

北京科技大学金属矿山高效开采与安全教育部重点实验室,北京 100083

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深部煤层 非连续 地应力场 反演方法

2024

清华大学学报(自然科学版)
清华大学

清华大学学报(自然科学版)

CSTPCD北大核心
影响因子:0.586
ISSN:1000-0054
年,卷(期):2024.64(12)