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This paper presents Geo-Strat-RL, a synthetic environment that uses reinforcement learning with verifiable rewards (RLVR) to train vision-language models to reason about geological event histories from stratigraphic diagrams and seismic data, demonstrating improved reconstruction and cross-domain transfer.
This paper investigates whether sentence embeddings can serve as an inference-time interface for injecting geological knowledge into a learned Darcy-flow inverse solver, finding that text conditioning reduces reconstruction error by 81% relative to a no-text counterfactual, with most gains from categorical class-level constraints.
Researchers propose a Physics-Informed Machine Learning (PIML) framework that integrates hydrological constraints into an LSTM loss function to improve short-term flood forecasting, particularly in data-scarce regimes. A 'Trend Alignment' constraint enforcing consistency between precipitation and discharge trends improves Nash-Sutcliffe Efficiency and eliminates unphysical predictions during extreme events.
SubsurfaceGen is a GPU-accelerated generator for 3D velocity models and seismic data, releasing a dataset of 4,276 2D velocity slices and associated wavefields and shot gathers across six geological settings, aimed at advancing machine learning for full waveform inversion.