Skip to content

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

Sep 2026 · 0 citations · 22 references
Computer Science

TL;DR

MineTRACE makes public geoscience data easier to access, interpret, and verify, supporting more efficient and transparent mineral exploration.

Abstract

Mineral exploration requires integrating heterogeneous geochemical, geophysical, and geological evidence, yet existing prospectivity systems often provide only opaque scores or heatmaps. We present MineTRACE, a web-based system for evidence-grounded exploration of eight commodities: Cu, Au, Ni, W, Sn, Co, Ta, and Mn. Users can explore prospectivity maps, query locations or regions, inspect supporting evidence, and interact through natural language. A transparent expert tree, informed by geological knowledge and known deposits, combines multi-source evidence into interpretable prospectivity scores. For a new location, the conversational assistant retrieves the score and supporting evidence from the analysis pipeline and presents them in natural language. The scorer achieves spatial AUC values of up to 0.917 across different test scenarios, while end-to-end evaluation assesses query accuracy and response grounding. MineTRACE makes public geoscience data easier to access, interpret, and verify, supporting more efficient and transparent mineral exploration.

View source

Similar papers

Review Open access Sep 2026

Explainable Machine Learning in Mineral Prospectivity Mapping: A Critical Review of Methods, Geological Knowledge Embedding, Validation, and Future Directions

Mineral prospectivity mapping (MPM) increasingly integrates geological, geochemical, geophysical, remote-sensing, and structural evidence through machine-learning workflows. Predictive accuracy alone, however, does not establish that a prospectivity map is geologically credible or useful for exploration decisions. This...

Mei-Qu Lu, Lian-Fa Zhong, Wen-Qiang He et al. · 0 citations
Preprint Aug 2026

EarthVerse: Benchmarking Scientific Agents Across Dynamic Earth Systems and Natural Hazards

This work evaluates 25 model and agent systems under a controlled tool-using protocol, then uses controlled studies to locate failures in evidence access, tool selection, memory, reasoning, interaction, and scientific execution.

Zhiqing Cui, Xinxiang Yin, Yihong Tang et al. · 0 citations
Review Sep 2026

Geography as the Organizing Grammar of Geospatial Models

GeoAI increasingly produces reusable Earth representation s, physical forecasts, multimodal systems, and reasoning a gents. Their progress exposes two distinct limitations. Geo graphic completeness asks whether the represented world ex tends beyond readily observed land surfaces and atmospher ic fields to oceans, bioge...

Rajiv Ranjan, Shashank Tamaskar · 0 citations
Review Open access Aug 2026

Geostatistical and Machine-learning Methodologies for Predicting Recoverable Mineral Value in U.S. Critical-mineral Deposits: A Critical Narrative Review

Critical minerals underpin energy, defence, digital and advanced-manufacturing systems, yet the economic value of a deposit depends not only on in situ grade but also on how much mineral value can be recovered under variable geological, mineralogical and operating conditions. In the United States, prediction is particu...

Pierre Mensah, Eunice Abena Lettu · 0 citations
#artificial intelligence Review Open access Sep 2026

Artificial Intelligence for Mineral Exploration Imagery: A Systematic Mapping Review of Data, Tasks, and Methods

Artificial intelligence (AI) is increasingly applied to image-based and image-formatted geoscientific evidence across mineral exploration, from regional and surface surveys to drilling/core analysis and laboratory characterization. However, the literature remains distributed across different research communities, makin...

Yun-Ke Xiao, C. Kong, Kai Xu et al. · 0 citations

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.