煤田地质与勘探2025，Vol.53Issue(11)：34-43,10.DOI:10.12363/issn.1001-1986.25.05.0387
基于贝叶斯优化的CNN-BiLSTM-Attention的煤体结构识别方法
Coal body structure identification method based on Bayesian-optimized CNN-BiLSTM-Attention
摘要
Abstract
[Background]Coal-bearing basins contain primary and tectonically deformed coals due to multistage tecton-ic deformations.However,the gas-bearing properties of coal seams differ significantly due to varying pore and fracture densities,permeability,and mechanical properties.This makes coal structure assessment critical to coalbed methane(CBM)exploration and production.[Objective and Method]To enhance the accuracy and intelligence of coal structure identification,this study constructed a CNN-BiLSTM-Attention hybrid model that integrated a Bayesian optimization strategy.This model allowed for efficient fusion and representation of multi-scale log data by combining the local fea-ture extraction capability of the convolutional neural network(CNN),the temporal sequence modeling strength of the bi-directional long short-term memory(BiLSTM),and the feature focusing ability of the Attention mechanism.Moreover,this model showed elevated stability and high training efficiency thanks to automatic parameter tuning through Bayesian optimization.Focusing on coal seams in the Shanxi and Benxi formations within the Ordos Basin,this study constructed a dataset of primary,primary-cataclastic,and cataclastic coals based on conventional log data,subjected to normaliza-tion,outlier removal,and interpolation of missing values,as well as data from cores.Then,the hybrid model was trained and assessed using the cross-entropy loss function.[Results and Conclusions]The CNN-BiLSTM-Attention hybrid model yielded an accuracy of 95.12%,outperforming isolated BiLSTM and CNN models.This hybrid model yielded precision and recall rates above 93%for various coal structures.Furthermore,it yielded a uniform error distribution,as indicated by the confusion matrices.This model was applied to well X2,demonstrating high consistency and discrimin-ative ability for transition zones between varying coal structures.This significantly reduces misclassification between primary-cataclastic and cataclastic coals.Additionally,the hybrid model exhibits strong robust performance in pro-cessing noise in log data.This study offers a reliable and effective approach for fine-scale CBM assessment.关键词
煤体结构/深度学习/CNN-BiLSTM-Attention/贝叶斯优化/测井数据Key words
coal structure/deep learning/CNN-BiLSTM-Attention/Bayesian optimization/log data分类
天文与地球科学引用本文复制引用
边会媛,姬嘉骏,段朝伟,周军,李坤,马予梒..基于贝叶斯优化的CNN-BiLSTM-Attention的煤体结构识别方法[J].煤田地质与勘探,2025,53(11):34-43,10.基金项目
国家自然科学基金项目(42304143) （42304143）
