Self-supervised, active learning seismic full-waveform inversion

D Colombo, E Turkoglu, E Sandoval-Curiel, T Alyousuf - Geophysics, 2024‏ - library.seg.org
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 …

Paired autoencoders for likelihood-free estimation in inverse problems

M Chung, E Hart, J Chung, B Peters… - … Learning: Science and …, 2024‏ - iopscience.iop.org
We consider the solution of nonlinear inverse problems where the forward problem is a
discretization of a partial differential equation. Such problems are notoriously difficult to …

Paired autoencoders for inverse problems

M Chung, E Hart, J Chung, B Peters… - arxiv preprint arxiv …, 2024‏ - arxiv.org
We consider the solution of nonlinear inverse problems where the forward problem is a
discretization of a partial differential equation. Such problems are notoriously difficult to …

An over complete deep learning method for inverse problems

M Eliasof, E Haber, E Treister - arxiv preprint arxiv:2402.04653, 2024‏ - arxiv.org
Obtaining meaningful solutions for inverse problems has been a major challenge with many
applications in science and engineering. Recent machine learning techniques based on …

A test-time learning approach to reparameterize the geophysical inverse problem with a convolutional neural network

A Xu, LJ Heagy - IEEE Transactions on Geoscience and …, 2024‏ - ieeexplore.ieee.org
Regularization is critical for solving ill-posed geophysical inverse problems. Explicit
regularization is often used, but there are opportunities to explore the implicit regularization …

Learning Regularization for Graph Inverse Problems

M Eliasof, MSR Siddiqui, CB Schönlieb… - arxiv preprint arxiv …, 2024‏ - arxiv.org
In recent years, Graph Neural Networks (GNNs) have been utilized for various applications
ranging from drug discovery to network design and social networks. In many applications, it …

A data-dependent regularization method based on the graph Laplacian

D Bianchi, D Evangelista, S Aleotti, M Donatelli… - arxiv preprint arxiv …, 2023‏ - arxiv.org
We investigate a variational method for ill-posed problems, named $\texttt {graphLa+}\Psi $,
which embeds a graph Laplacian operator in the regularization term. The novelty of this …

Investigating the application of test-time machine learning methods for geophysics inverisons

A Xu - 2024‏ - open.library.ubc.ca
Artificial intelligence (AI) has become a driving force for innovation, and Canada has been at
the forefront of this movement. One area where AI shows great promise is the earth …