SeisCoDE: 3D Seismic Interpretation Foundation Model with Contrastive Self-Distillation Learning

TL;DR

SeisCoDE introduces a self-supervised vision transformer-based foundation model for 3D seismic interpretation, enabling knowledge transfer and improved generalization without labeled data, thus advancing subsurface exploration.

Contribution

The paper develops a novel pretraining framework using contrastive self-distillation for 3D seismic data, addressing the lack of domain-specific foundation models in geophysics.

Findings

SeisCoDE effectively captures seismic features and structures.

It outperforms traditional supervised methods like UNet.

Demonstrates strong generalization across interpretation tasks.

Abstract

Seismic interpretation is vital for understanding subsurface structures but remains labor-intensive, subjective, and computationally demanding. While deep learning (DL) offers promise, its success hinges on large, high-quality datasets, often scarce in geophysics. Foundation Models (FMs), which have shown significant success in fields like natural language processing and computer vision, offer a transformative opportunity for seismic interpretation by enabling knowledge transfer and generalization across interpretation tasks. However, the application of FMs in this domain remains limited, especially at the 3D scale, due to the absence of a domain-specific pretraining workflow. Here, our study sought to develop a pretraining strategy for 3D seismic interpretation by introducing a vision transformer-based Seismic Contrastive Self-Distillation Encoder (SeisCoDE), a novel self-supervised learning (SSL) framework that leverages seismic signal processing and attribute analysis, preserving seismic structural integrity during pretraining. By leveraging contrastive learning and self-distillation, SeisCoDE learns meaningful latent representations without the need for labeled data (zero-shot approach). Results indicate that SeisCoDE effectively captures critical seismic features and characteristics, producing robust latent feature representations that drive downstream seismic interpretation. It demonstrates enhanced generalization abilities across different seismic interpretation tasks, outperforming the conventional supervised learning UNet method. Overall, this research emphasizes the potential of FMs informed by seismic image processing and attribute analysis principles, paving the way for continued innovation integrating FMs for seismic interpretation, with the potential to revolutionize subsurface characterization and geophysical seismic exploration.