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Locally sparse travel time tomography (LST) is a tomography algorithm which uses sparse modeling and dictionary learning to estimate 2D geophysical images based on wave travel times across sensor arrays. This repository is an implementation of the IEEE paper: M.J. Bianco and P. Gerstoft, "Travel time tomography with adaptive dictionaries," IEEE …

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Locally sparse tomography

This is a Matlab implementation of the locally sparse travel time tomography (LST) algorithm for seismic and acoustic data as presented in the paper: M.J. Bianco and P. Gerstoft, "Travel time tomography with adaptive dictionaries," IEEE Trans. Computational Imaging, Vol. 4, No. 4, 2018.(DOI: https://doi.org/10.1109/TCI.2018.2862644)

The other tomography methods in the paper, conventional and total variation-regularized tomography, are also implemented for comparison. Figures similar to those in the paper (Bianco and Gerstoft 2018) are generated in a series of synthetic tests. These tests include cases with and without noise.

The simulations are run from 'main_LST.m'. This script is configured for a noise-free simulation (noise can be added in line 51, for example).

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Locally sparse travel time tomography (LST) is a tomography algorithm which uses sparse modeling and dictionary learning to estimate 2D geophysical images based on wave travel times across sensor arrays. This repository is an implementation of the IEEE paper: M.J. Bianco and P. Gerstoft, "Travel time tomography with adaptive dictionaries," IEEE …

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