摘要
针对传统反演方法存在的初始模型依赖、计算时间较长等问题,提出一种基于卷积神经网络的磁异常反演方法。该方法首先设计大量磁异常体模型,进行正演模拟产生样本数据集;接着借鉴经典的卷积神经网络VGG-13设计了一种全新的VGG磁异常反演网络(VGGINV);然后使用样本数据集训练该网络,并优化网络参数;最后对理论模型和实测数据进行反演实验。实验结果表明,该方法可以准确地反演出磁异常体的位置和磁化强度,具有较强的学习能力和一定的泛化能力,能有效解决磁异常数据反演问题。
To resolve the problems of traditional inversion methods,such as initial model dependence and long calculation time,we proposed a magnetic anomaly inversion method based on convolutional neural network.With this method,a number of magnetic anomalous body models were designed to perform forward simulation,which generated various sample data-sets.Subsequently,a new VGG magnetic anomaly inversion network(VGGINV)was designed based on the classic convolutional neural network VGG-13.After that,the sample data-set was used to train the network and optimize the network parameters.Finally,inversion experiment was performed based on this theoretical model and actual field data.The experimental results show that the proposed method can accurately invert the position and magnetization of magnetic anomaly,with strong learning ability and certain generalization ability,and can effectively solve problems in magnetic anomaly data inversion.
作者
薛瑞洁
熊杰
张月
王蓉
XUE Ruijie;XIONG Jie;ZHANG Yue;WANG Rong(College of Electronics and Information,Yangtze University,Jingzhou,Hubei 434023,China)
出处
《现代地质》
CAS
CSCD
北大核心
2023年第1期173-183,共11页
Geoscience
基金
国家自然科学基金项目(61673006)
湖北省教育厅科学技术项目(B2016034)。
关键词
深度学习
地球物理反演
磁异常
卷积神经网络
VGG
deep learning
geophysical inversion
magnetic anomaly
convolutional neural network
VGG
