摘要
利用CUGTEM-GKⅡ瞬变电磁仪对矿山巷道的超前勘探工作进行实时物探,并形成三维模型成像结果,给出了矿山岩层地下水涌水量、巷道顶板及两帮位移量等预警数据。其中,机器学习算法整合深度卷积神经网络、多列神经网络、数据二值化输出、模糊决策矩阵预警等算法,给出了覆盖范围较大的粗略预警结果和覆盖范围较小的精细预警结果。经仿真分析,粗略预警结果和精细预警结果均表现出一定的数据敏感性,且精细预警可以在巷道掘进问题岩层前24 h给出较高敏感度的预警结果。
The CUGTEM-GK II transient electromagnetic instrument is used for real-time geophysical exploration of mine roadways,generating 3D model imaging results,and providing forecasting and warning data for water inflow in underground rock layers,displacement of roadway roof and walls,etc.Machine learning algorithms integrated deep convolutional neural networks,multilayer neural networks,data binary output,fuzzy decision matrix warning,etc.,to provide rough warning results with wide coverage and precise warning results with narrower coverage.Through simulation analysis,both rough warning results and precise warning results show certain data sensitivity,with the precise warning results able to provide high sensitivity warning results 24 h before encountering problematic rock layers during roadway excavation.
作者
尹纪超
王慎利
Yin Jichao;Wang Shenli(Shandong Zhengyuan Geophysical Information Technology Co.,Ltd.)
出处
《黄金》
CAS
2024年第6期27-30,共4页
Gold
基金
国家自然科学基金项目(GDKJXM20210069)。
关键词
瞬变电磁法
矿山巷道
超前探测
机器学习
神经网络
数据预警
transient electromagnetic method
mine roadways
advanced detection
machine learning
neural networks
data warning
