Estimating Three-Dimensional Resistivity Distribution with Magnetotelluric Data and a Deep Learning Algorithm

Estimating Three-Dimensional Resistivity Distribution with Magnetotelluric Data and a Deep Learning Algorithm

In this study, we describe a deep learning (DL)-based workflow for the three-dimensional (3D) geophysical inversion of magnetotelluric (MT) data. We derived a mathematical connection between a 3D resistivity model and the surface-observed electric/magnetic field response by using a fully connected n...

Full description

Bibliographic Details
Main Authors: Xiaojun Liu, James A. Craven, Victoria Tschirhart, Stephen E. Grasby
Format: Article
Language:English
Published: MDPI AG 2024-09-01
Series:Remote Sensing
Subjects:
deep learning
neural network
inversion problem
magnetotelluric
Mount Meager
Online Access:https://www.mdpi.com/2072-4292/16/18/3400

Internet

https://www.mdpi.com/2072-4292/16/18/3400

Similar Items