Generative Adversarial Networks for Model Order Reduction in Seismic Full-Waveform Inversion

Abstract

I train a Generative Adversarial Network to produce realistic seismic wave speed models. I integrate the generator network into seismic Full-Waveform Inversion to reduce the number of model parameters and restrict the inverted models to only those that are plausible. Applying the method to a 2D section of the SEAM model, I demonstrate that it can produce more plausible results than conventional Full-Waveform Inversion.

arXiv comment10 pages, 2 figures

References in corpus (1)

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Cited by in corpus (4)

• Deep generative models in inversion: a review and development of a new approach based on a variational autoencoder
• Gradient-based deterministic inversion of geophysical data with Generative Adversarial Networks: is it feasible?
• DeepFlow: History Matching in the Space of Deep Generative Models
• Integrating Deep Neural Networks with Full-waveform Inversion: Reparametrization, Regularization, and Uncertainty Quantification