铀矿地质2024，Vol.40Issue(4)：803-808,6.DOI:10.3969/j.issn.1000-0658.2024.40.072
基于深度学习的AMT智能地质解译技术
Deep Learning Based Geological Interpretation of AMT Data
摘要
Abstract
AMT(Audio Magnetotelluric)is widely used for obtaining geological condition related to sandstone-type uranium deposits,such as the range of buried sand body and the top boundary of basement rock.However,these geological condition are hard to interpret via measured sections without geological deduction,which relies heavily on the experience and cognition of the interpreter.On the other hand,with the development of 3D technology,artificial geological interpretation shows low efficiency and reliability.In this paper,a deep learning model constructed using U-net was used for the geological interpretation of AMT data in the Naren-Yihegaole area.To train the model,a training dataset was built based on the simulated data from random simulated models.In the prediction stage,sand bodies and basement rock were delineated from the inversion resistivity images.The comparison between two interpretations,one by deep learning method,showed high consistency with the artificial one,but with better time-saving.Therefore,the deep learning based technology is more effective than the traditional way.关键词
砂岩型铀矿/AMT/深度学习/智能地质解译Key words
sandstone-type uranium/AMT/deep learning/artificial intelligent geological interpretation分类
天文与地球科学引用本文复制引用
汪硕,周渊凯,马国财,胡跃彬,胡渤..基于深度学习的AMT智能地质解译技术[J].铀矿地质,2024,40(4):803-808,6.基金项目
中核集团青年英才项目(编号:物QNYC2020-1)和中核集团集中研发项目(编号:物SDEQ02)联合资助. （编号:物QNYC2020-1）
