SourceNet: Interpretable Sim-to-Real Inference on Variable-Geometry Sensor Arrays for Earthquake Source Inversion

Abstract

Inferring high-dimensional physical states from sparse, ad-hoc sensor arrays is a fundamental challenge across AI for Science, yet standard architectures like CNNs and DeepSets struggle to capture the irregular geometries and relational physics inherent to domains like seismology. To address this, we propose SourceNet, a Transformer-based framework that bridges the profound Sim-to-Real gap via Physics-Structured Domain Randomization (PSDR), a protocol that randomizes governing physical dynamics to enforce invariance to unmodeled environmental heterogeneity. By pre-training on 100,000 synthetic events and fine-tuning on ~2,500 real-world events, SourceNet achieves state-of-the-art precision on held-out real data, demonstrating exceptional data efficiency and real-time capability compared to classical solvers. Beyond prediction, interpretability analysis reveals that the model shows scientific-agent-like features: it autonomously discovers geometric information bottlenecks and learns an attention policy that prioritizes sparse sensor placements, effectively recovering principles of optimal experimental design from data alone.