基于随机森林声波曲线构建的盾构机掘进路线预测  

Tunneling route prediction of shield machine based on random forest P-wave generation

作　　者：师素珍[1] 刘最亮 谷剑英 段培飞 韩琦 齐佑朝 张新 Shi Su-zhen;Gu Jian-ying;Liu Zui-liang;Duan Pei-fei;Han Qi;Qi You-chao;Zhang Xin(State Key Laboratory of Coal Resources and Safety Mining,Beijing 100083,P.R.China;Yangquan Coal Industry(Group)Co.LTD,Yangquan 37000;Bei Fang Investigation,Design&Research CO.LTD,Tianjin 300222;College of Geoscience and Surveying Engineering,China University of Mining and Technology,Beijing 100083,P.R.China)

机构地区：[1]煤炭资源与安全开采国家重点实验室,北京100083 [2]华阳新材料科技集团有限公司,阳泉37000 [3]中国矿业大学(北京)地球科学与测绘工程学院,北京100083 [4]中水北方勘测设计研究有限责任公司,天津300222

出　　处：《Applied Geophysics》2024年第1期69-79,204,共12页应用地球物理（英文版）

基　　金：supported by the Joint Fund of the State Key Laboratory of Coal Resources and Safe Mining-Beijing University Outstanding Young Scientists Program Project (BJJWZYJH01201911413037);State Key Laboratory of“Coal Resources and Safe Mining”Open Fund (SKLCRSM19ZZ02)。

摘　　要：近年来,煤矿井下为了提高生产效率,将盾构机应用到巷道掘进中。地质条件是决定盾构机工作效率的重要因素,盾构机施工围岩以石灰岩、砂岩等中硬岩最为有利,因此拟掘进巷道位置的岩性预测成为提高盾构机工作效率的核心问题。目前地震反演是岩性预测的重要手段,阳泉新景区块由于部分声波测井曲线缺失,对波阻抗反演产生了影响,因此,如何利用已有的测井曲线生成缺失的声波测井曲线以及如何利用盾构机工作行进中的岩性揭露数据不断更新前方岩性分布预测结果成为当前亟待解决的关键。首先对测井曲线进行标准化等预处理,针对声波测井曲线缺失问题,引入随机森林回归算法,利用密度(Density)、自然伽马(GR)、视电阻率(Res)和自然电位(SP)4种测井曲线作为特征变量,建立曲线回归预测模型,预测声波(P-wave)测井曲线。在分析声波曲线和伽马曲线原理的基础上,进行拟声波曲线构建并做拟声波反演,在反演数据体上解释K7砂岩的顶和底界面,将解释得到的地震解释层位信息从时间域数据转换到深度域,经验证实预测结果与揭露数据符合率较高。同时,结合盾构机工作行进中的岩性揭露数据对目标巷道剖面的K7砂体分布预测进行了多次更新迭代,精细刻画了巷道位置K7砂岩的分布情况,为盾构机掘进路线优选提供了有效指导。In recent years,coal mine has applied shield tunneling machines to roadway excavation to improve production efficiency.Geological condition is an important factor that determines the efficiency of shield machines.The shield machine is most favorable for medium to hard surrounding rocks such as limestone and sandstone.Therefore,the lithology prediction of the location of a planned excavation roadway becomes the core issue in improving the efficiency of the shield machine.At present,seismic inversion is an essential method for lithology prediction.However,in Yangquan Xinjing area,missing P-wave logging curves affects the impedance inversion.Therefore,using existing logging curves to generate missing P-wave logging curves and using sandstone exposure data to continuously update lithology distribution prediction results are of great interest.In this study,logging curves were first pretreated by standardization to ensure the inversion effect.Because of the missing acoustic logging curves,the random forest regression algorithm was introduced using density,natural gamma,apparent resistivity,and spontaneous potential curves as characteristic variables to establish a curve regression prediction model.Then,P-wave logging curves were acquired.After a full analysis of the principles of acoustic and gamma curves,a quasi-acoustic curve is constructed,and a quasi-acoustic inversion was performed.The top and bottom interfaces of the K7 sandstone were interpreted on the inversion data body.The interpreted horizon information was converted from the time domain to the depth domain.The predicted results agreed well with the exposed data.At the same time,combined with the lithology exposure data from the shield tunneling machine,the distribution prediction of the K7 sand body in the target roadway section was updated and iterated many times,which provided effective guidance for the optimization of the tunneling route of the shield tunneling machine.

关 键 词：随机森林 曲线构建 拟声波反演 K7砂岩展布 盾构机掘进路线 

分 类 号：TD421.5[矿业工程—矿山机电] P631[天文地球—地质矿产勘探]