Impact of adaptive data selection on full waveform inversion

Abstract

Summary Research has shown that full waveform inversion (FWI) has the potential to provide high-resolution velocity parameters. However, for most cases, FWI suffers from an objective function with a local minimum instead of a global minimum because of cycle skipping between the real data and the predicted data. This cycle-skipped data may cause an inaccurate velocity update and serious artifacts. An FWI workflow was proposed to address this issue. Offset stripping and inner mutes are used to limit the input for FWI to near offsets for the initial inversion iteration, and then farther offsets are gradually incorporated in subsequent iterations as the velocity model accuracy improves with depth. However, determination of the appropriate offset and inner mute requires extensive manual work. In addition, this workflow is computationally expensive and cannot effectively avoid the cycle skipping issue. We propose a data-selection algorithm for FWI that ensures all input data is within a half-cycle difference compared with the predicted data. This data-selection process is implemented in each iteration of the inversion to generate a velocity model with higher accuracy and minimal artifacts.