基于深度学习的可控源音频大地电磁二维反演  

2D Controlled Source Audio-frequency Magnetotelluric Inversion Based on Deep Learning

作　　者：汪茂 谭捍东[2] 付兴 WANG Mao;TAN Han-dong;FU Xing(School of Information Engineering,China University of Geosciences,Beijing,100083,China;School of Geophysics and Information Technology,China University of Geosciences,Beijing,100083,China)

机构地区：[1]中国地质大学(北京)信息工程学院,北京100083 [2]中国地质大学(北京)地球物理与信息技术学院,北京100083

出　　处：《科学技术与工程》2025年第7期2683-2690,共8页Science Technology and Engineering

摘　　要：可控源音频大地电磁(controlled-source audio-frequency magnetotellurics, CSAMT)采用人工场源,具有较强的抗干扰性,广泛应用于油气勘探、矿产普查等方面。传统的二维反演技术已发展成熟,深度学习目前在地球物理探测中有了一些研究进展,但深度学习在CSAMT反演中的研究还是空缺,因此开发基于深度学习的CSAMT二维反演算法对推进深度学习在电磁勘探中的发展非常有意义。对深度学习的卷积层、池化层、全连接层和UNet网络的特点进行了介绍;对如何构建训练集、本文所采用的UNet网络以及如何设置训练的各参数进行了阐述。将训练好的网络储存下来,在做反演计算时,将网络导入程序中,用网络对观测数据进行预测,得到反演结果。设计了多个理论模型进行反演试算,实验结果验证了算法的可靠性和有效性。对深度学习反演和数据空间OCAAM反演的计算时间进行了统计,在构建训练集和训练网络时需要较多时间,但采用训练好的网络反演的时间远低于传统反演的计算时间,具有反演速度快的特点。Controlled-source audio-frequency magnetotellurics(CSAMT)uses artificial sources,providing strong anti-interference capabilities.It is widely used in oil exploration,mineral surveys and other areas.Traditional 2D inversion technology is mature,and deep learning has recently made some research advancements in geophysical exploration.There is still a research gap in applying deep learning to CSAMT inversion.Therefore,developing a 2D inversion algorithm for CSAMT based on deep learning is highly significant for advancing the use of deep learning in electromagnetic exploration.The characteristics of deep learning components such as convolutional layers,pooling layers,fully connected layers,and the UNet network were introduced.An explanation was provided on how to construct the training dataset,the UNet network used in this study,and how to set various training parameters.The network was saved after training.When the inversion was needed,the net was loaded and the algorithm could predict the result.Several theoretical models were designed for inversion,and the experiment results verified the reliability and effectiveness of the algorithm.The time of the deep learning inversion and the tranditional inversion was recorded.Building training set needed much time,but the time of deep learning inverison was much less than the tranditional inversion.The deep learning inversion is more efficient than the traditional inversion.

关 键 词：深度学习 UNet CSAMT 反演 

分 类 号：P319[天文地球—固体地球物理学]