Regularizing velocity differences in time-lapse FWI using gradient mismatch information

Abstract

We present a method for recovering time-lapse velocity changes using full waveform inversion (FWI). In a preprocessing step we invert for a single intermediate model by simultaneously minimizing the data misfit in the baseline and the monitor surveys. We record the individual FWI gradients corresponding to the baseline and the monitor datasets at each iteration of the inversion. Regions where these gradients consistently have opposing sign are likely to correspond to locations of time-lapse change. This insight is used to generate a spatially varying confidence map for time-lapse change. In a subsequent joint inversion we invert for baseline and monitor models while regularizing the difference between the models with this spatially varying confidence map. Unlike double difference full waveform inversion (DDFWI) we do not require identical source and receiver positions in the baseline and monitor surveys.