工矿自动化2025，Vol.51Issue(7)：59-65,7.DOI:10.13272/j.issn.1671-251x.2025050016
基于深度学习的矿井时移电阻率监测数据特征提取与智能筛选方法
Deep learning-based feature extraction and intelligent screening method for time-lapse resistivity monitoring data in mines
摘要
Abstract
The time-lapse resistivity monitoring data in mines is a high-dimensional dataset that includes attributes such as recovery direction,working face extension direction,depth direction,and resistivity values.Its true distribution is unknown,and blindly applying existing dimensionality reduction methods may diminish certain high-dimensional attributes that are closely related to data quality.At present,data selection relies heavily on manual experience,resulting in a low level of automation.To address these issues,a deep learning-based feature extraction and intelligent screening method for time-lapse resistivity monitoring data in mines was proposed.First,high-dimensional discrete data containing spatial 3D coordinate information and resistivity values were subjected to dimensionality reduction to capture essential features of the data,eliminate redundancy,and achieve multi-scale feature extraction.Then,the ResNet10 convolutional neural network was used to extract 2D features from each slice and compute their structural similarity to assess the spatial continuity and smoothness of resistivity anomalies.A Transformer network was used to extract 3D features from the resistivity monitoring data.Finally,spectral clustering was applied in the feature space to perform intelligent screening of the monitoring data.The proposed method and manual selection method were used to extract features and perform quality selection on 16 monitoring datasets collected in a single day from a coal mine area.The results showed that manual selection by different personnel produced completely different results,indicating strong subjectivity,poor repeatability,lack of fixed evaluation criteria,and took an average of 30 minutes,leading to poor real-time performance.The proposed method achieved 100%consistency in the experimental results,and each selection took less than 30 seconds,indicating that the selection results were objective,stable,reliable,and fast.关键词
时移电阻率监测/ResNet10卷积神经网络/Transformer网络/谱聚类/煤矿地质透明化/深度学习/结构相似性Key words
time-lapse resistivity monitoring/ResNet10 convolutional neural network/Transformer network/spectral clustering/coal mine geological transparency/deep learning/structural similarity分类
矿业与冶金引用本文复制引用
刘尧,刘再斌,邢震..基于深度学习的矿井时移电阻率监测数据特征提取与智能筛选方法[J].工矿自动化,2025,51(7):59-65,7.基金项目
"十四五"国家重点研发计划项目(2023YFC3012105) （2023YFC3012105）
陕西省博士后科研资助项目(2024BSHSDZZ213) （2024BSHSDZZ213）
陕西省自然科学基础研究计划项目(2024JC-YBQN-0268) （2024JC-YBQN-0268）
天地科技股份有限公司科技创新创业资金专项项目(2023-TD-ZD001-001) （2023-TD-ZD001-001）
中煤科工西安研究院(集团)有限公司科技创新基金项目(2023XAYJS04). （集团）
