Industry-Scale Uncertainty-Aware Full Waveform Inference with Generative Models

TitleIndustry-Scale Uncertainty-Aware Full Waveform Inference with Generative Models
Publication TypeConference
Year of Publication2025
AuthorsRafael Orozco, Huseyin Tuna Erdinc, Mathias Louboutin, Felix J. Herrmann
Conference NameSIAM Conference on Computational Science and Engineering (CSE25)
Month03
KeywordsBayesian sampling, FWI, Generative models, Inverse problems, Normalizing flows, SIAM
Abstract

In this talk, we address the challenge of Bayesian sampling for ill-posed inverse problems involving expensive PDE forward operators. To tackle this, we propose the use of generative models and discuss three key challenges along with our solutions. First, we address the constraints of GPU memory by introducing patch-based training and memory-efficient invertible networks, enabling the handling of large-scale data. Second, we demonstrate that these models can be effectively trained using only prior data available from acoustic observations and borehole well data, making the approach practical for real-world applications. Third, we explore various strategies to enhance the reliability of posterior samples by incorporating physics-based constraints through the wave operator. We conclude by presenting experiments that showcase the capabilities of these methods in field-data Full Waveform Inversion (FWI) workflows, scaling up to industry-scale problems with large 2D (512x7000 degrees of freedom) and 3D models.

Notes

(SIAM CSE25, Dallas)

URLhttps://slim.gatech.edu/Publications/Public/Conferences/SIAMCSE/2025/orozco2025SIAMisu
Citation Keyorozco2025SIAMisu