An encoder-decoder deep surrogate for reverse time migration in seismic imaging under uncertainty

TL;DR

This paper introduces a deep learning surrogate model for reverse time migration in seismic imaging, significantly reducing computational costs while accurately capturing uncertainty propagation in imaging results.

Contribution

The work presents a novel encoder-decoder deep learning approach to efficiently approximate RTM under uncertainty, enabling faster seismic imaging with uncertainty quantification.

Findings

The surrogate accurately reproduces seismic images.

It effectively propagates uncertainty from velocity fields to images.

The method reduces computational time for RTM under uncertainty.

Abstract

Seismic imaging faces challenges due to the presence of several uncertainty sources. Uncertainties exist in data measurements, source positioning, and subsurface geophysical properties. Reverse time migration (RTM) is a high-resolution depth migration approach useful for extracting information such as reservoir localization and boundaries. RTM, however, is time-consuming and data-intensive as it requires computing twice the wave equation to generate and store an imaging condition. RTM, when embedded in an uncertainty quantification algorithm (like the Monte Carlo method), shows a many-fold increase in its computational complexity due to the high input-output dimensionality. In this work, we propose an encoder-decoder deep learning surrogate model for RTM under uncertainty. Inputs are an ensemble of velocity fields, expressing the uncertainty, and outputs the seismic images. We show by numerical experimentation that the surrogate model can reproduce the seismic images accurately, and, more importantly, the uncertainty propagation from the input velocity fields to the image ensemble.