Problem
Research questions and friction points this paper is trying to address.
mineral exploration
heterogeneous evidence
transparent scores
prospectivity maps
natural language
MineTRACE: An Evidence-Grounded Interactive Reasoning System for Mineral Prospectivity
🤖 AI Summary
MineTRACE系统通过整合多源地质证据并提供透明的评分机制，解决了矿物勘探中数据不透明的问题，支持用户交互式查询和自然语言交流。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
evidence-grounded
interactive reasoning
transparent prospectivity scores
natural language interaction
public geoscience data
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