海洋地质与第四纪地质2026，Vol.46Issue(1)：114-122,9.DOI:10.16562/j.cnki.0256-1492.2024020203
基于卷积神经网络的速度约束海洋大地电磁反演
Velocity constrained marine magnetotelluric inversion based on convolutional neural networks
摘要
Abstract
High-resolution marine magnetotelluric inversion has always been an important topic in the field of marine magnetotelluric sounding.To improve the accuracy of marine magnetotelluric inversion,by taking advantage of the high imaging accuracy of marine seismic detection methods,the velocity structure obtained by deep seismic detection as prior information was added into the convolutional neural network magnetotelluric inversion.By constructing two-channel data as input of inversion network,the velocity-constrained convolutional neural network magnetotelluric inversion was realized.Based on the proposed method,data sets established for geological profiles of the Qianliyan region in the South Yellow Sea were trained.Results show that the proposed method could improve the accuracy and the vertical resolution of the inversion.Moreover,the results of anti-noise tests also show that the proposed method could achieve higher resolution inversion for noisy data sets.This study provided a new idea for high-resolution marine magnetotelluric inversion.关键词
卷积神经网络/速度约束/海洋大地电磁反演/分辨能力Key words
convolutional neural network/velocity constraint/marine magnetotelluric inversion/spatial resolution分类
海洋科学引用本文复制引用
卢艳艳,裴建新,封常青,吴俊良..基于卷积神经网络的速度约束海洋大地电磁反演[J].海洋地质与第四纪地质,2026,46(1):114-122,9.基金项目
山东省重点研发计划-重大科技创新工程项目"海洋矿产资源电磁探测和高光谱成像关键技术及设备研发"(2020CXGC010706) （2020CXGC010706）
国家自然科学基金项目"海洋极低频电磁噪声产生机理及数据模型研究"(U2241201) （U2241201）
国家自然科学基金面上项目"东沙海域海流感应电磁场特征及海流速度反演方法研究"(41974085) （41974085）
