Object-based high contrast travel time tomography

TL;DR

This paper introduces a model-based travel time tomography method that uses elementary objects to detect high contrast, high velocity structures, providing probabilistic maps that improve upon traditional grid-based approaches.

Contribution

It proposes a novel object-based modeling approach and a new reconstruction algorithm that efficiently estimates the probability of high velocity structures in the region.

Findings

Efficient sampling of model parameter space

Generation of probability maps indicating high velocity structures

Improved detection accuracy over grid-based methods

Abstract

We consider travel time tomography problems involving detection of high contrast, discrete high velocity structures. This results in a discrete nonlinear inverse problem, for which traditional grid-based models and iterative linearized least-squares reconstruction algorithms are not suitable. This is because travel paths change significantly near the high contrast velocity structure, making it more difficult to inversely calculate the travel path and infer the velocity along the path. We propose a model-based approach to describe the high velocity structure using pre-defined elementary objects. Compared to a grid-based model, our approach has complexity that increases as a function of the number of objects, rather than increasing with the number of cells (usually very large). A new reconstruction algorithm is developed that provides estimates of the probability that a high velocity structure appears at any point in the region of interest. Simulation results show that our method can efficiently sample the model parameter space, and we map the model parameters into the high velocity structures in spatial domain to generate a "probability map", which represent the appearance of the high velocity structure in different regions. We show the probability map not only gives the highest probability to the optimal solution, but also includes other possible models as well.