Transient Electromagnetic Data Inversion: A Machine Learning Approach with CNN-LightGBM Model
The inversion of transient electromagnetic (TEM) data presents a complex nonlinear
problem, and traditional inversion methods encounter certain limitations. Data-driven …
problem, and traditional inversion methods encounter certain limitations. Data-driven …
A two-dimensional magnetotelluric deep learning inversion approach based on improved Dense Convolutional Network
Magnetotelluric (MT) inversion is an important means of MT data interpretation. The use of
deep learning technology for MT inversion has attracted much attention because it is not …
deep learning technology for MT inversion has attracted much attention because it is not …
Physics-Informed Deep Learning Inversion with Application to Noisy Magnetotelluric Measurements
W Liu, H Wang, Z Xi, L Wang - Remote Sensing, 2023 - mdpi.com
Despite demonstrating exceptional inversion production for synthetic data, the application of
deep learning (DL) inversion methods to invert realistic magnetotelluric (MT) measurements …
deep learning (DL) inversion methods to invert realistic magnetotelluric (MT) measurements …
Multi-scale Dilated Convolutional Neural Networks for Transient Electromagnetic Inversion
K Cheng, X Yang, X Wu - IEEE Transactions on Geoscience …, 2024 - ieeexplore.ieee.org
The inversion of transient electromagnetic (TEM) data entails a complex nonlinear problem
with high dimensionality and ill-posedness. All convolutional neural networks (CNNs) for …
with high dimensionality and ill-posedness. All convolutional neural networks (CNNs) for …
A three-dimensional magnetotelluric inversion method based on the joint data-driven and physics-driven deep learning technology
The conventional magnetotelluric (MT) inversion method is subject to the influence of the
initial model, which leads to an unstable inversion process and a tendency to get trapped at …
initial model, which leads to an unstable inversion process and a tendency to get trapped at …
Three Dimensional Magnetotelluric Forward Modeling Through Deep Learning
For a long time, the 2-D and 3-D magnetotelluric (MT) forward modeling is mainly
accomplished by computational methods. Traditional methods are time-consuming due to …
accomplished by computational methods. Traditional methods are time-consuming due to …
An Alternating Direction Method of Multipliers Algorithm for One-Dimensional Magnetotelluric Anisotropic Inversion using Fourier Series Expansion
In this study, we present a novel approach for 1-D magnetotelluric (MT) anisotropy inversion
that aims to improve the reliability and efficiency of the inversion process. First, to reduce the …
that aims to improve the reliability and efficiency of the inversion process. First, to reduce the …
基于残差神经网络的大地电磁二维反演
余俊虎， 唐新功， 熊治涛 - 地球物理学报, 2025 - dsjyj.com.cn
本文开展了基于残差神经网络的大地电磁二维反演研究. 采用高斯随机场设计并生成了5
万个不同规模, 不同边界形状(规则边界与光滑边界), 不同电阻率对比度, 单个到多个电性异常体 …
万个不同规模, 不同边界形状(规则边界与光滑边界), 不同电阻率对比度, 单个到多个电性异常体 …
Enhancing Deep Learning based RMT Data Inversion using Gaussian Random Field
Deep learning (DL) methods have emerged as a powerful tool for the inversion of
geophysical data. When applied to field data, these models often struggle without additional …
geophysical data. When applied to field data, these models often struggle without additional …
Two-dimensional inversion of magnetotelluric electromagnetic fields based on residual neural networks
JH YU, XG TANG, ZT XIONG - Chinese Journal of Geophysics, 2025 - en.dzkx.org
This paper conducts a study on two-dimensional magnetotelluric inversion based on a
residual neural network. We generated 50000 models with various scales, different …
residual neural network. We generated 50000 models with various scales, different …