Approaching geoscientific inverse problems with vector-to-image domain transfer networks

TL;DR

This paper introduces vec2pix, a deep neural network approach for solving geoscientific inverse problems by predicting 2D subsurface properties from 1D measurement data, demonstrating effectiveness on synthetic datasets.

Contribution

The paper presents a novel deep learning method, vec2pix, for inferring 2D subsurface models from 1D data, with extensive testing on synthetic geophysical inverse problems.

Findings

vec2pix accurately recovers 2D subsurface models from synthetic data.

Performance remains robust with smaller training datasets.

Uncertainty can be partially quantified using deep ensembles.

Abstract

We present vec2pix, a deep neural network designed to predict categorical or continuous 2D subsurface property fields from one-dimensional measurement data (e.g., time series), thereby offering a new approach to solve inverse problems. The performance of the method is investigated through two types of synthetic inverse problems: (a) a crosshole ground penetrating radar (GPR) tomography experiment with GPR travel times being used to infer a 2D velocity field, and (2) a multi-well pumping experiment within an unconfined aquifer with time series of transient hydraulic heads being used to retrieve a 2D hydraulic conductivity field. For each type of problem, both a multi-Gaussian and a binary channelized subsurface domain with long-range connectivity are considered. Using a training set of 20,000 examples (implying as many forward model evaluations), the method is found to recover a 2D model that is in much closer agreement with the true model than the closest training model in the forward-simulated data space. Further testing with smaller training sample sizes shows only a moderate reduction in performance when using 5000 training examples only. Even if the recovered models are visually close to the true ones, the data misfits associated with their forward responses are generally larger than the noise level used to contaminate the true data. Uncertainty of the inverse solution is partially assessed using deep ensembles, in which the network is trained repeatedly with random initialization. Overall, this study advances understanding of how to use deep learning to infer subsurface models from indirect measurement data.