Physics-embedded machine learning for electromagnetic data imaging: Examining three types of data-driven imaging methods
Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine,
geophysics, and various industries. It is an ill-posed inverse problem whose solution is …
geophysics, and various industries. It is an ill-posed inverse problem whose solution is …
Physics-driven deep-learning inversion with application to transient electromagnetics
Machine learning, and specifically deep-learning (DL) techniques applied to geophysical
inverse problems, is an attractive subject, which has promising potential and, at the same …
inverse problems, is an attractive subject, which has promising potential and, at the same …
Electromagnetic subsurface imaging in the presence of metallic structures: a review of numerical strategies
Electromagnetic (EM) imaging aims to produce large-scale, high-resolution soil conductivity
maps that provide essential information for Earth subsurface exploration. To rigorously …
maps that provide essential information for Earth subsurface exploration. To rigorously …
[HTML][HTML] A finite element based deep learning solver for parametric PDEs
We introduce a dynamic Deep Learning (DL) architecture based on the Finite Element
Method (FEM) to solve linear parametric Partial Differential Equations (PDEs). The …
Method (FEM) to solve linear parametric Partial Differential Equations (PDEs). The …
Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation
Abstract Characterization of complex subsurface structures is challenging due to the
demand to preserve geological realism of the training images in earth and environmental …
demand to preserve geological realism of the training images in earth and environmental …
Error control and loss functions for the deep learning inversion of borehole resistivity measurements
Deep learning (DL) is a numerical method that approximates functions. Recently, its use has
become attractive for the simulation and inversion of multiple problems in computational …
become attractive for the simulation and inversion of multiple problems in computational …
Coupled physics-deep learning inversion
Application of machine learning (ML) or deep learning (DL) to geophysical data inversion is
a growing topic of interest. Opportunities are in the areas of enhanced efficiency, resolution …
a growing topic of interest. Opportunities are in the areas of enhanced efficiency, resolution …
A deep neural network as surrogate model for forward simulation of borehole resistivity measurements
Inverse problems appear in multiple industrial applications. Solving such inverse problems
require the repeated solution of the forward problem. This is the most time-consuming stage …
require the repeated solution of the forward problem. This is the most time-consuming stage …
Self-supervised, active learning seismic full-waveform inversion
ABSTRACT A novel recursive, self-supervised machine-learning (ML) inversion scheme is
developed. It is applied for fast and accurate full-waveform inversion of land seismic data …
developed. It is applied for fast and accurate full-waveform inversion of land seismic data …
Deep learning inversion of gravity data for detection of CO2 plumes in overlying aquifers
We developed an effective U-Net based deep learning (DL) model for inversion of surface
gravity data on a rectangular grid to predict 2-D high-resolution subsurface CO 2 distribution …
gravity data on a rectangular grid to predict 2-D high-resolution subsurface CO 2 distribution …