Assessing the value of combined use of distributed acoustic sensing and multicomponent vertical seismic profiling data with network-based full-waveform inversion and uncertainty quantification: A case study in Alberta, Canada

ABSTRACT Distributed acoustic sensing (DAS) technology, deployed in a vertical seismic profiling (VSP) experimental configuration, has emerged as a candidate for nondisruptive and low-cost seismic monitoring of CO2 geostorage and plume evolution. As a means of extracting information from these data, full-waveform inversion (FWI) has likewise received significant attention because it uses relatively complete physical models of wave propagation and because of its sample-by-sample incorporation of data information. Recent artificial neural network-based FWI algorithms (built with, for instance, recursive neural networks [RNN]) have added to FWI a range of flexible and efficient tools for gradient computation and options for uncertainty assessment and initial model proxies. An important current research area for the use of DAS data is to better understand how they change our confidence levels in the inversion results for FWI. In particular, we seek to understand whether DAS data or conventional geophone data alone are optimal for FWI in the CO2 problem, and if not, to what degree they complement each other. The Snowflake 4D VSP data set, which includes multioffset and multiazimuth broadband sources illuminating fiber-optic cable and densely sampled accelerometer in the borehole, has been acquired by our group to directly address these questions. In this study, we quantify uncertainty by evaluating the posterior model covariance matrix from the inverse Hessian matrix at the end of RNN-FWI runs on the Snowflake baseline data, invoking a velocity-density parameterization, and involving mixtures of accelerometer and DAS data. In this uncertainty quantification context, the complementary effect of combining accelerometer and DAS data is evident in the VP and ρ models. In the pursuit of reliable and low-cost monitoring tools, this supports continued consideration of a multicomponent sensors supported by DAS approach.