# Probabilistic Neural-Network Based 2D Travel Time Tomography

## TL;DR

This paper introduces a fast, probabilistic 2D travel time tomography method using neural networks, which significantly reduces computational costs compared to traditional Monte Carlo approaches and highlights the impact of prior information on uncertainty estimates.

## Contribution

It presents the first fully non-linear, rapid Bayesian inversion technique for 2D travel time tomography using mixture density networks, demonstrating the importance of prior information.

## Key findings

- Neural networks enable rapid probabilistic tomography after initial training.
- Prior information significantly influences uncertainty estimates in the inversion.
- The method achieves high-dimensional inversions in about a second on a standard desktop.

## Abstract

Travel time tomography for the velocity structure of a medium is a highly non-linear and non-unique inverse problem. Monte Carlo methods are becoming increasingly common choices to provide probabilistic solutions to tomographic problems but those methods are computationally expensive. Neural networks can often be used to solve highly non-linear problems at a much lower computational cost when multiple inversions are needed from similar data types. We present the first method to perform fully non-linear, rapid and probabilistic Bayesian inversion of travel time data for 2D velocity maps using a mixture density network. We compare multiple methods to estimate probability density functions that represent the tomographic solution, using different sets of prior information and different training methodologies. We demonstrate the importance of prior information in such high dimensional inverse problems due to the curse of dimensionality: unrealistically informative prior probability distributions may result in better estimates of the mean velocity structure, however the uncertainties represented in the posterior probability density functions then contain less information than is obtained when using a less informative prior. This is illustrated by the emergence of uncertainty loops in posterior standard deviation maps when inverting travel time data using a less informative prior, which are not observed when using networks trained on prior information that includes (unrealistic) a priori smoothness constraints in the velocity models. We show that after an expensive program of training the networks, repeated high-dimensional, probabilistic tomography is possible on timescales of the order of a second on a standard desktop computer.