arXiv AI
TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology
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arXiv AI
Jul 28
GeoDecider: An Evidence-Grounded Agent for Geological Interpretation via Deliberative Reasoning
arXiv:2605. 03383v2 Announce Type: replace Abstract: Geological interpretation infers subsurface properties and structures from indirect geophysical observations.
arXiv AI
Aug 20
SeisEvo: Evolution of Seismic Data Reconstruction Algorithms by Agents
SeisEvo is a method that uses a large language model (LLM) and multi‑agent search to evolve seismic data reconstruction algorithms rather than optimize a single result. Starting from a classical algorithm, the agents modify only user‑opened components, rejecting candidates that violate physical constraints and scoring the rest by execution. The resulting white‑box algorithms—such as a residual‑gated, phase‑aligned dip‑consistency projection for interpolation and a reliability‑grouped singular‑value shrinkage for simultaneous interpolation and denoising—outperform classic methods by several decibels and generalize to unseen data.
arXiv AI
Jul 29
Automatic Knowledge Graph Construction and Query for Earthquake Catalogs
arXiv:2607. 24984v1 Announce Type: cross Abstract: In recent years, the number of events in earthquake catalogs has significantly increased due to the utilization of more effective deep learning based detectors and phase pickers but answering open ended questions such as what characterizes this sequence?
arXiv AI
Aug 25
EarthVerse: Benchmarking Scientific Agents Across Dynamic Earth Systems and Natural Hazards
arXiv:2608.23525v1 Announce Type: new Abstract: Earth-system analysis reconstructs changing physical processes from observations that differ in source, scale, timing, and modality. Natural hazards ma...
arXiv Machine Learning
Aug 26
SeisMamba: Low-Latency Single-Station Seismic Magnitude Estimation for Spatially Distributed Earthquake Early Warning
arXiv:2608.24561v1 Announce Type: new Abstract: Rapid earthquake magnitude estimation is central to earthquake early warning, yet many operational systems depend on dense regional seismic networks an...
arXiv Machine Learning
Jun 25
Geo-Strat-RL: Learning Geological Event Reasoning from Verifiable Tasks
arXiv:2606. 25000v1 Announce Type: new Abstract: To evaluate whether vision-language models can reason about geological histories, it is necessary to construct observations for which the underlying process history is known.