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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 …
Paired autoencoders for likelihood-free estimation in inverse problems
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 …
discretization of a partial differential equation. Such problems are notoriously difficult to …
Paired autoencoders for inverse problems
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 …
discretization of a partial differential equation. Such problems are notoriously difficult to …
An over complete deep learning method for inverse problems
Obtaining meaningful solutions for inverse problems has been a major challenge with many
applications in science and engineering. Recent machine learning techniques based on …
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
Regularization is critical for solving ill-posed geophysical inverse problems. Explicit
regularization is often used, but there are opportunities to explore the implicit regularization …
regularization is often used, but there are opportunities to explore the implicit regularization …
Learning Regularization for Graph Inverse Problems
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 …
ranging from drug discovery to network design and social networks. In many applications, it …
A data-dependent regularization method based on the graph Laplacian
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 …
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 …
the forefront of this movement. One area where AI shows great promise is the earth …