Data-driven Full-waveform Inversion Surrogate using Conditional Generative Adversarial Networks

TL;DR

This paper introduces a cGAN-based surrogate model for full-waveform inversion velocity modeling, significantly reducing computational costs while accurately capturing geological structures and velocity variations from real data.

Contribution

It presents a novel application of conditional GANs to generate detailed subsurface velocity models, bypassing expensive physics-based simulations in FWI.

Findings

GAN accurately matches real FWI outputs

Enables extraction of geological structures and velocity variations

Potential to accelerate reservoir characterization

Abstract

In the Oil and Gas industry, estimating a subsurface velocity field is an essential step in seismic processing, reservoir characterization, and hydrocarbon volume calculation. Full-waveform inversion (FWI) velocity modeling is an iterative advanced technique that provides an accurate and detailed velocity field model, although at a very high computational cost due to the physics-based numerical simulations required at each FWI iteration. In this study, we propose a method of generating velocity field models, as detailed as those obtained through FWI, using a conditional generative adversarial network (cGAN) with multiple inputs. The primary motivation of this approach is to circumvent the extremely high cost of full-waveform inversion velocity modeling. Real-world data were used to train and test the proposed network architecture, and three evaluation metrics (percent error, structural similarity index measure, and visual analysis) were adopted as quality criteria. Based on these metrics, the results evaluated upon the test set suggest that the GAN was able to accurately match real FWI generated outputs, enabling it to extract from input data the main geological structures and lateral velocity variations. Experimental results indicate that the proposed method, when deployed, has the potential to increase the speed of geophysical reservoir characterization processes, saving on time and computational resources.