Machine learning-enabled velocity model building with uncertainty quantification

Rafael Orozco, Yunlin Zeng, Felix Johan Herrmann

Abstract:

Accurately characterizing migration velocity models is crucial for a
wide range of geophysical applications, from hydrocarbon exploration to
monitoring of CO\textsubscript{2} sequestration projects. Traditional
velocity model building methods such as Full-Waveform Inversion (FWI)
are powerful but often struggle with the inherent complexities of the
inverse problem, including noise, limited bandwidth, receiver aperture
and computational constraints. To address these challenges, we propose a
scalable methodology that integrates generative modeling, in the form of
Diffusion networks, with physics-informed summary statistics, making it
suitable for complicated imaging problems including field datasets. By
defining these summary statistics in terms of subsurface-offset image
volumes for poor initial velocity models, our approach allows for
computationally efficient generation of Bayesian posterior samples for
migration velocity models that offer a useful assessment of uncertainty.
To validate our approach, we introduce a battery of tests that measure
the quality of the inferred velocity models, as well as the quality of
the inferred uncertainties. With modern synthetic datasets, we reconfirm
gains from using subsurface-image gathers as the conditioning
observable. For complex velocity model building involving salt, we
propose a new iterative workflow that refines amortized posterior
approximations with salt flooding and demonstrate how the uncertainty in
the velocity model can be propagated to the final product reverse time
migrated images. Finally, we present a proof of concept on field
datasets to show that our method can scale to industry-sized problems.