复杂地质大型地下洞室群地应力场反演方法与应用  

Inversion of In-situ Stress Field of Large Underground Cavern Group in Complex Geology Methods and Applications

作　　者：裴书锋 臧东升 孙常新 何建华 李国良[4] 陈炳瑞[2] PEI Shufeng;ZANG Dongsheng;SUN Changxin;HE Jianhua;LI Guoliang;CHEN Bingrui(College of Geosciences and Eng.,North China Univ.of Water Resources and Electric Power,Zhengzhou 450046,China;State Key Lab.of Geomechanics and Geotechnical Eng.,Inst.of Rock and Soil Mechanics,Chinese Academy of Sciences,Wuhan 430071,China;Chengdu Eng.Co.Ltd.,Power China,Chengdu 610072,China;China Railway First Survey and Design Inst.Group Co.,Ltd.,Xi'an 710043,China)

机构地区：[1]华北水利水电大学地球科学与工程学院,河南郑州450046 [2]中国科学院武汉岩土力学研究所岩土力学与工程国家重点实验室,湖北武汉430071 [3]中国电建集团成都勘测设计研究院有限公司,四川成都610072 [4]中铁第一勘察设计院集团有限公司,陕西西安710043

出　　处：《工程科学与技术》2024年第5期176-189,共14页Advanced Engineering Sciences

基　　金：国家自然科学基金项目(52209132,42277141);河南省自然科学基金项目(202300410269);中国电建集团成都勘测设计研究院有限公司科技项目(CD2C20210540)。

摘　　要：深切河谷和大型结构面影响大型地下洞室群地应力场分布,准确预测地下洞室群区域地应力场对工程建设至关重要。本文提出融合逐步多元线性回归和神经网络的联合反演,通过逐步多元回归获得合理的构造运动因素及其基准值,再通过神经网络反演获得较优的反演结果。首先,提出复杂地质条件下地下洞室群地应力场反演方法,主要包括地应力实测数据分析、3维地质模型建立、逐步多元线性回归与BP人工神经网络联合反演,以及基于洞室围岩应力型破坏特征的结果验证。该方法从历史构造运动出发,约束多元回归因素及其回归系数,解决剪应力偏差过大的问题,并通过历史地质构造分析、实测地应力分析和洞室群应力型破坏特征等多源约束和验证,提高地应力反演结果的准确性。以叶巴滩水电站地下洞室群为例,对该方法进行工程应用。研究结果表明:叶巴滩水电站地下洞室群最大主应力为25~30MPa,方向为NWW-EW方位,缓倾向河谷,洞室开挖后上游侧拱肩和下游侧墙脚易发生应力型破坏;断层等大型结构面严重影响了地应力分布,临近断层部位最大主应力量值减小,方向近于垂直断层,在距断层5~10m范围内最大主应力量值迅速增大至原岩应力,方位逐渐转为近平行断层,与宏观主应力方向接近;密集发育的断层,是导致叶巴滩水电站地下洞室群区域主应力变异性强的主要原因。本研究阐明了深切河谷和多结构面共同作用下地下洞室群地应力场分布规律,特别是断层附近地应力场的变异特征,为地下洞室群应力集中区和应力型灾害部位预测提供基础依据。Deep valleys and large structural planes considerably affect the distribution of the in-situ stress field in large underground caverns.Accurate prediction of the in-situ stress field in underground caverns is crucial for engineering construction.The inversion of the in-situ stress field based on measured data is the primary method for predicting this field.Existing inversion methods are predominantly based on multiple linear regression or neural network approaches,which mainly consider factors such as gravity and tectonic movements that influence the in-situ stress field.The multivariate regression method offers simplicity,rapidity,and a unique solution;however,it occasionally fails to account for the physical significance of tectonic movement factors.This method may present issues such as negative regression coefficients for factors,strong collinearity among factors,or insignificant effects.The neural network method improves the fitting degree through its nonlinear relationship between the input and output layers,but determining the reference values for each factor can be challenging.In order to address these issues,this study proposes a joint inversion approach that combines stepwise multiple linear regression and neural networks.This approach obtains reasonable tectonic movement factors and their reference values through stepwise multiple regression,subsequently improving inversion results using neural network inversion.The study also introduces an inversion method for the in-situ stress field of underground cavern groups under complex geological conditions.The method includes analyzing measured in-situ stress data,establishing a three-dimensional geological model,performing joint inversion using stepwise multiple linear regression and BP artificial neural network,and verifying results based on the stress-type failure characteristics of the surrounding rock.Starting from the historical tectonic movements,this method constrains the multiple regression factors and their coefficients,addresses excessive shear str

关 键 词：地应力场反演 大型地下洞室群 数值模拟 智能反演 高应力 深切河谷 断层 

分 类 号：TU45[建筑科学—岩土工程]