Propagating the prior from shallow to deep with a pre-trained velocity-model Generative Transformer network

TL;DR

This paper introduces VelocityGPT, a Transformer-based generative model that propagates shallow velocity information to deeper layers, improving seismic velocity model generation by leveraging spatial dependencies and prior data.

Contribution

VelocityGPT is the first Transformer-based autoregressive model for seismic velocity generation that effectively propagates shallow to deep layers using prior information.

Findings

VelocityGPT successfully generates realistic deep velocity models from shallow data.

The model effectively incorporates prior information like well data and migration images.

Synthetic experiments demonstrate VelocityGPT's potential in seismic velocity modeling.

Abstract

Building subsurface velocity models is essential to our goals in utilizing seismic data for Earth discovery and exploration, as well as monitoring. With the dawn of machine learning, these velocity models (or, more precisely, their distribution) can be stored accurately and efficiently in a generative model. These stored velocity model distributions can be utilized to regularize or quantify uncertainties in inverse problems, like full waveform inversion. However, most generators, like normalizing flows or diffusion models, treat the image (velocity model) uniformly, disregarding spatial dependencies and resolution changes with respect to the observation locations. To address this weakness, we introduce VelocityGPT, a novel implementation that utilizes Transformer decoders trained autoregressively to generate a velocity model from shallow subsurface to deep. Owing to the fact that seismic data are often recorded on the Earth's surface, a top-down generator can utilize the inverted information in the shallow as guidance (prior) to generating the deep. To facilitate the implementation, we use an additional network to compress the velocity model. We also inject prior information, like well or structure (represented by a migration image) to generate the velocity model. Using synthetic data, we demonstrate the effectiveness of VelocityGPT as a promising approach in generative model applications for seismic velocity model building.