arXiv:2608. 09276v1 Announce Type: cross Abstract: Planetary geology relies on historical, interpretive reasoning to reconstruct past events from diverse observations.
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.
arXiv:2605. 03383v2 Announce Type: replace Abstract: Geological interpretation infers subsurface properties and structures from indirect geophysical observations.
MineTRACE is a web-based system that provides evidence‑grounded exploration for eight minerals (Cu, Au, Ni, W, Sn, Co, Ta, Mn). It allows users to view prospectivity maps, query specific locations or regions, inspect supporting evidence, and interact via natural language. The system uses a transparent expert tree that integrates heterogeneous geochemical, geophysical, and geological data to produce interpretable prospectivity scores, achieving spatial AUC values up to 0.917.
arXiv:2609.20978v2 Announce Type: replace Abstract: High-consequence subsurface decisions often rely on sparse data that permit competing geological interpretations. Determining consistency of these...
The paper introduces a workflow that ranks competing geological interpretations by converting them into spatial priors and assessing their consistency with hydraulic‑head observations. Using a text‑to‑image model to generate 1600 geologic images per interpretation, a variational autoencoder encodes them, and a supervised inverse network maps head data into this latent space, producing log‑conductivity fields for flow simulation. The method is validated on a synthetic Johansen Formation benchmark and applied to two conceptual models of the Culebra Dolomite, yielding compatibility scores that align with independent evidence.
arXiv:2607. 22408v1 Announce Type: new Abstract: The renewed global focus on lunar exploration, driven by the prospect of in-situ resource utilization and a sustained human presence on the Moon, has created growing demand for accurate, large-scale characterization of the lunar surface.
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:2605. 24844v2 Announce Type: replace Abstract: While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evolution, current AI in Earth sciences predominantly targets surface remote sensing and GIS.
Chinese ancient document understanding demands complex visual, linguistic, and historical reasoning. Current Large Vision-Language Models (LVLMs) typically rely on an opaque, single-pass generation pa...
The paper presents a deep‑learning crater detection algorithm (CDA) based on the OWLv2 Vision Transformer, fine‑tuned with Low‑Rank Adaptation on a manually labeled IMPACT dataset. It optimizes a combined loss of CIoU for localization and contrastive loss for classification, achieving a maximum recall of 92.6% and precision of 71.4% on lunar images. The method demonstrates reliable crater detection under varied illumination and rugged terrain, supporting safer lunar landings.
arXiv:2605. 00972v2 Announce Type: replace-cross Abstract: Earth system science is producing increasingly large, high-dimensional datasets from both physics-based and AI-driven models.
arXiv:2609.13277v1 Announce Type: cross Abstract: Lunar orbital missions, such as Lunar Reconnaissance Orbiter, Kaguya/SELENE, Gravity Recovery and Interior Laboratory, and Lunar Prospector, among ot...