Generative Diffusion Model for Seismic Imaging Improvement of Sparsely Acquired Data and Uncertainty Quantification

TL;DR

This paper introduces a generative diffusion model for seismic imaging from sparse data, improving image quality, reducing artifacts, and enabling uncertainty quantification, with a novel patch fusion strategy for large-scale images.

Contribution

The paper presents a new generative diffusion model tailored for seismic imaging that outperforms existing CNN methods and incorporates uncertainty assessment.

Findings

Enhanced seismic image quality from sparse data

Effective artifact removal and discontinuity reduction

Reliable uncertainty quantification demonstrated on synthetic and field data

Abstract

Seismic imaging from sparsely acquired data faces challenges such as low image quality, discontinuities, and migration swing artifacts. Existing convolutional neural network (CNN)-based methods struggle with complex feature distributions and cannot effectively assess uncertainty, making it hard to evaluate the reliability of their processed results. To address these issues, we propose a new method using a generative diffusion model (GDM). Here, in the training phase, we use the imaging results from sparse data as conditional input, combined with noisy versions of dense data imaging results, for the network to predict the added noise. After training, the network can predict the imaging results for test images from sparse data acquisition, using the generative process with conditional control. This GDM not only improves image quality and removes artifacts caused by sparse data, but also naturally evaluates uncertainty by leveraging the probabilistic nature of the GDM. To overcome the decline in generation quality and the memory burden of large-scale images, we develop a patch fusion strategy that effectively addresses these issues. Synthetic and field data examples demonstrate that our method significantly enhances imaging quality and provides effective uncertainty quantification.