发表机构
The University of Western Australia; Wilfrid Laurier University（西澳大利亚大学; 威尔弗里德·劳里埃大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MineTRACE是一款基于网络的交互式成矿潜力推理系统，通过透明专家树整合多源地质证据，实现了最高0.917的空间AUC值，提升了矿产勘探的效率与透明度。
AI 中文摘要
矿产勘探需要整合异源的地球化学、地球物理和地质证据，但现有的成矿潜力系统往往仅提供不透明的分数或热图。我们提出了MineTRACE，这是一种基于网络的系统，用于勘探8种矿产：铜（Cu）、金（Au）、镍（Ni）、钨（W）、锡（Sn）、钴（Co）、钽（Ta）和锰（Mn）。用户可探索成矿潜力图、查询位置或区域、检查支持证据，并通过自然语言交互。受地质知识和已知矿床启发的透明专家树将多源证据组合为可解释的成矿潜力分数。对于新位置，对话助手从分析流水线中检索分数和支持证据，并以自然语言呈现。该评分器在不同测试场景下实现了最高0.917的空间AUC值，同时端到端评估对查询准确性和响应依据进行了评估。MineTRACE使公共地球科学数据更易于访问、解释和验证，支持更高效、透明的矿产勘探。
英文摘要
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.
CommentsEMNLP 2026 Demo
