Uncertainty quantification in elastic full-waveform inversion as a means to access complementary aspects in DAS and accelerometer data

Abstract Multiparameter elastic full-waveform inversion (EFWI) is of interest because of its use of a relatively complete physical wave propagation model (in contrast to acoustic FWI) in inversion and because of its potential to deliver high-resolution subsurface estimates that directly support interpretation. For EFWI to be a fully realized technology, uncertainty quantification (UQ) for the inversion results is essential, as it provides a confidence measure for the derived outcomes. Being a data-matching optimization method, type, quality, and coverage of a data set must be expected to impact the accuracy of EFWI results and should be reflected in a robust UQ. The purpose of this paper is to use a particular UQ strategy to explore the impact of data type on the reliability of models generated through FWI. Specifically, we assess whether isolated use of either distributed acoustic sensing (DAS) or accelerometer (AC) data suffices for optimal model determination in CO2 monitoring scenarios, or to what extent combining these data types enhances model accuracy. We carry this out on a well-characterized vertical seismic profile (VSP) data set, the baseline component of the “Snowflake” 4D VSP, which includes broadband sources across diverse offsets and azimuths, illuminating both fiber-optic and densely deployed AC within the well. Our analysis evaluates UQ by extracting the posterior model covariance matrix from the inverse Hessian matrix, subsequent to recurrent neural network-based EFWI runs. This supports the general conclusion that a multisensor strategy supported by DAS is, in the sense of postinversion confidence, optimal for VSP monitoring. Integrated AC and DAS data appear to be particularly important for reducing uncertainties associated with P-wave velocity model recovery.