Generative Modeling of Seismic Data Using Score-Based Generative Models

Abstract

Summary The availability of seismic data has specific implications for inversion and interpretation methods, especially for learning-based methods such as deep learning. Forward modeling or acquisition of field data are common means of obtaining seismic data, which can be costly, time-consuming and labor-intensive. This paper provides a generative modeling method for generating seismic data. We use a score-based generative model to learn the distribution of target seismic data set. Specifically, we learn the gradient of the target data set distribution through score matching using the noise conditional score network (NCSN). Then, we sample high-quality samples similar to the target data set through Langevin dynamics with learned NCSN. We take the seismic records synthesized by Marmousi as an example to show the powerful generative modeling capabilities of the generative model. By sampling from a prior distribution (Gaussian distribution), the generative model can generate diverse samples and have good interpretability. For example, by interpolating two random data points from the prior distribution, the generated data has manifold continuity in certain features (such as amplitude, inclination, polarity, number of events, etc.).