A supervised descent learning technique for solving directional electromagnetic logging-while-drilling inverse problems
In this article, a new scheme based on the supervised descent method (SDM) for solving
directional electromagnetic logging-while-drilling (LWD) inverse problems is proposed. The …
directional electromagnetic logging-while-drilling (LWD) inverse problems is proposed. The …
Deep learning audio magnetotellurics inversion using residual-based deep convolution neural network
In this study, we developed a novel 18-layers residual full convolutional neural network
(18RFCN) for audio magnetotellurics (AMT) data inversion. Different from traditional …
(18RFCN) for audio magnetotellurics (AMT) data inversion. Different from traditional …
Supervised descent learning for thoracic electrical impedance tomography
Objective: The absolute image reconstruction problem of electrical impedance tomography
(EIT) is ill-posed. Traditional methods usually solve a nonlinear least squares problem with …
(EIT) is ill-posed. Traditional methods usually solve a nonlinear least squares problem with …
Pixel-and model-based microwave inversion with supervised descent method for dielectric targets
We present a general framework of applying supervised descent method (SDM) to the pixel-
and model-based full-wave inversion of microwave data for dielectric targets. SDM is a …
and model-based full-wave inversion of microwave data for dielectric targets. SDM is a …
Application of supervised descent method for 2D magnetotelluric data inversion
The supervised descent method (SDM) is applied to 2D magnetotellurics (MT) data
inversion. SDM contains offline training and online prediction. The training set is composed …
inversion. SDM contains offline training and online prediction. The training set is composed …
Magnetotelluric deep learning forward modeling and its application in inversion
F Deng, J Hu, X Wang, S Yu, B Zhang, S Li, X Li - Remote Sensing, 2023 - mdpi.com
Magnetotelluric (MT) inversion and forward modeling are closely linked. The optimization
and iteration processes of the inverse algorithm require frequent calls to forward modeling …
and iteration processes of the inverse algorithm require frequent calls to forward modeling …
Cooperative deep learning inversion of controlled-source electromagnetic data for salt delineation
Various geophysical data types have advantages for exploring the subsurface, and more
reliable exploration can be realized through integration of such data. However, the imaging …
reliable exploration can be realized through integration of such data. However, the imaging …
A neural network-based hybrid framework for least-squares inversion of transient electromagnetic data
Inversion of large-scale time-domain transient electromagnetic (TEM) surveys is
computationally expensive and time-consuming. The calculation of partial derivatives for the …
computationally expensive and time-consuming. The calculation of partial derivatives for the …
[HTML][HTML] DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications
Deep learning (DL) algorithms have shown incredible potential in many applications. The
success of these data-hungry methods is largely associated with the availability of large …
success of these data-hungry methods is largely associated with the availability of large …
Fast full-wave electromagnetic forward solver based on deep conditional convolutional autoencoders
This letter proposes a novel deep learning (DL) based fast solver for the electromagnetic
forward (EMF) process. This proposed fast full-wave solver for EMF process is designed …
forward (EMF) process. This proposed fast full-wave solver for EMF process is designed …