计算机与数字工程2024，Vol.52Issue(1)：1-8,17,9.DOI:10.3969/j.issn.1672-9722.2024.01.001
基于条件对抗增强的Transformer煤矿微震定位方法
Microseismic Localization Method of Transformer Coal Mine Based on Conditional Confrontation Enhancement
摘要
Abstract
With the development of artificial intelligence technology and the widespread use of microseismic monitoring sys-tems in coal mines,more and more deep learning models are applied to solve the source localization problem of microseismic events in coal mines.However,the small amount of microseismic data and single data are not enough to train large and deep neural network models,and the small and shallow neural network models are not enough to characterize the source of microseismic events influ-enced by multiple factors,thus leading to the low localization accuracy and weak robustness of the localization models,and poor per-formance in practical production life,which seriously hinders the development of deep learning models in the field of microseismic localization.To address the above problems,a Transformer coal mine microseismic localization method based on conditional adver-sarial augmentation,CGAN-Transformer,is proposed,which firstly augments microseismic data with small and single data volume into microseismic data with large data volume and certain diversity by a network model of CGAN architecture.Secondly,transforms microseismic waveform data into microseismic data using Transformer encoder layer to convert microseismic waveform data into fea-ture data and then use its attention mechanism to further learn the deep-level features and complex inter-station dependencies of mi-croseismic waveform data,and also use Gaussian distribution random variables to offset the influence of different geological condi-tions on localization accuracy.Finally,by introducing a hybrid density output layer to obtain Gaussian distribution parameters,the optimal source location is calculated.The experimental results on a mining dataset in Chile and Liaoning verify the effectiveness of the method.The results show that both the epicenter error and the source error obtained by this method are better than other meth-ods,and the localization error is reduced by 38%and 12%on the two datasets,respectively,achieving the purpose of improving the source localization accuracy and the robustness of the localization model.关键词
生成对抗网络/Transformer模型/微震定位/注意力机制/混合密度网络Key words
generative adversarial networks/Transformer model/microseismic location/attention mechanism/mixed densi-ty network分类
数理科学引用本文复制引用
丁琳琳,胡永亮,李昱达,王凯璐,王慧颖..基于条件对抗增强的Transformer煤矿微震定位方法[J].计算机与数字工程,2024,52(1):1-8,17,9.基金项目
国家自然科学基金项目(编号:62072220) （编号:62072220）
国家重点研发计划项目(编号:2022YFC3004603) （编号:2022YFC3004603）
辽宁省自然科学基金计划项目(编号:2022-KF-13-06)资助. （编号:2022-KF-13-06）
