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MineTRACE: An Evidence-Grounded Interactive Reasoning System for Mineral Prospectivity

arXiv cs.AI · 2d ago Cached

MineTRACE is a web-based evidence-grounded interactive reasoning system that integrates geochemical, geophysical, and geological data to provide transparent mineral prospectivity scores and natural language interaction, supporting efficient and verifiable mineral exploration.

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#geoscience

HERMES: a multi-agent framework for structured knowledge extraction from ultra-long documents in geoscience

arXiv cs.CL · 2026-08-17 Cached

HERMES is a scalable multi-agent framework for extracting structured knowledge from ultra-long scientific documents in geoscience, achieving high accuracy and sixfold efficiency improvement over manual methods.

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Neutrinos from Deep Inside Earth Provide a New Picture of the Mantle

Hacker News Top · 2026-08-11 Cached

A global network of neutrino detectors is revealing the radioactive elements inside Earth's mantle, offering a new view of the planet's interior heat engine. Experiments like SNO+ and JUNO are detecting geoneutrinos to map the mantle's composition.

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Geo-Strat-RL: Learning Geological Event Reasoning from Verifiable Tasks

arXiv cs.LG · 2026-06-25 Cached

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.

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What Do Language Priors Contribute to Darcy-Flow Inversion? A Mechanistic Audit

arXiv cs.LG · 2026-06-25 Cached

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.

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Physics-Informed Machine Learning for Short-Term Flood Prediction

arXiv cs.LG · 2026-06-04 Cached

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.

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SubsurfaceGen: Procedural Generation of Field-Scale Earth Models and Seismic Data

arXiv cs.LG · 2026-06-01 Cached

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.

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