SeisBind: Physics-Aware Tri-Modal Representation Binding for Seismic Data via Contrastive Learning

TL;DR

This paper introduces SeisBind, a physics-aware contrastive learning framework that aligns seismic data, velocity models, and physical descriptors in a shared space to improve interpretability and physical consistency in seismic inversion.

Contribution

It presents a novel multi-modal contrastive learning approach incorporating physical descriptors as a third modality for better geological semantics understanding.

Findings

Achieves robust seismic-to-velocity retrieval on OpenFWI dataset.

Preserves meaningful physical semantics in learned representations.

Enables cross-modal inference of interpretable seismic attributes.

Abstract

This letter proposes a physics-aware multi-modal contrastive learning framework designed to transform complex seismic wavefields into human-readable physical representations. Traditional data-driven inversion methods often focus on pixel-wise mapping, which lacks physical grounding and interpretability. To address this, we introduce a novel framework that jointly aligns seismic shot gathers, subsurface velocity models, and explicit physical descriptors (e.g., mean velocity and gradients) in a shared latent space. By introducing these descriptors as a third modality, our approach encourages the learned embeddings to capture intrinsic geological semantics rather than superficial signal correlations. Experiments on the OpenFWI dataset demonstrate that the proposed method not only achieves robust seismic-to-velocity retrieval but also preserves meaningful physical semantics, enabling cross-modal inference of interpretable attributes. This representation-centric perspective provides a flexible foundation for expert-guided subsurface characterization.