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[2602.17784] QueryPlot: Generating Geological Evidence Layers using Natural Language Queries for Mineral Exploration
Summary
QueryPlot introduces a framework for generating geological evidence layers using natural language queries, enhancing mineral exploration through semantic retrieval and mapping.
Why It Matters
This research addresses the challenges in mineral prospectivity mapping by leveraging natural language processing to synthesize geological data, making the exploration process more efficient and accessible. It has implications for geologists and data scientists in improving mineral discovery methods.
Key Takeaways
- QueryPlot integrates geological text corpora with geospatial data for enhanced mineral exploration.
- The framework supports natural language queries, improving user accessibility to geological data.
- High recall of known mineral occurrences demonstrates the effectiveness of embedding-based retrieval.
- The system allows for multi-criteria prospectivity analysis through compositional querying.
- Source code and datasets are publicly available, supporting future research and development.
Computer Science > Computation and Language arXiv:2602.17784 (cs) [Submitted on 19 Feb 2026] Title:QueryPlot: Generating Geological Evidence Layers using Natural Language Queries for Mineral Exploration Authors:Meng Ye, Xiao Lin, Georgina Lukoczki, Graham W. Lederer, Yi Yao View a PDF of the paper titled QueryPlot: Generating Geological Evidence Layers using Natural Language Queries for Mineral Exploration, by Meng Ye and 4 other authors View PDF HTML (experimental) Abstract:Mineral prospectivity mapping requires synthesizing heterogeneous geological knowledge, including textual deposit models and geospatial datasets, to identify regions likely to host specific mineral deposit types. This process is traditionally manual and knowledge-intensive. We present QueryPlot, a semantic retrieval and mapping framework that integrates large-scale geological text corpora with geologic map data using modern Natural Language Processing techniques. We curate descriptive deposit models for over 120 deposit types and transform the State Geologic Map Compilation (SGMC) polygons into structured textual representations. Given a user-defined natural language query, the system encodes both queries and region descriptions using a pretrained embedding model and computes semantic similarity scores to rank and spatially visualize regions as continuous evidence layers. QueryPlot supports compositional querying over deposit characteristics, enabling aggregation of multiple similarity-derived layers f...
