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The Cartographer
MineTRACE: An Evidence-Grounded Interactive Reasoning System for Mineral Prospectivity
September 02, 2026 Β· Grace Period Β· π EMNLP 2026
Authors
Yiran Zhang, Jinwen Liu, Daniel Su, Yisu Chen, Qiang Sun, Chris Gonzalez, Eun-Jung Holden, Marco Fiorentini, Wei Liu, Yihao Ding
arXiv ID
2609.02060
Category
cs.AI: Artificial Intelligence
Citations
0
Venue
EMNLP 2026
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.
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