QueryPlot: Generating Geological Evidence Layers using Natural Language Queries for Mineral Exploration

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 for multi-criteria prospectivity analysis. In a case study on tungsten skarn deposits, we demonstrate that embedding-based retrieval achieves high recall of known occurrences and produces prospective regions that closely align with expert-defined permissive tracts. Furthermore, similarity scores can be incorporated as additional features in supervised learning pipelines, yielding measurable improvements in classification performance. QueryPlot is implemented as a web-based system supporting interactive querying, visualization, and export of GIS-compatible prospectivity layers.To support future research, we have made the source code and datasets used in this study publicly available.

Figures (12)

• Figure 1: QueryPlot helps geologists accelerate mineral exploration workflow through automating document summarization, geologic maps database retrieval, and evidence layer visualization. It offloads the burden of interacting with multiple tools and managing different data products from geologists.
• Figure 2: System overview of QueryPlot. Upper left: Geologic map data are processed to generate long description for each polygon. Lower left: Long documents are summarized into descriptive deposit models using LLM. Lower right: Semantic similarity between textual queries and polygon descriptions are computed and visualized as evidence layers. Upper right: A prototype system with user-friendly UI helps geologists accelerate the mineral exploration workflow.
• Figure 3: Merging multiple individual polygons with same geologic signatures into one multi-polygon.
• Figure 4: Example prompt for summarizing documents into short and concise descriptive deposit models.
• Figure 5: An evidence layer generated for national scale tungsten skarn exploration. The layer was generated from query "tonalite, granodiorite, quartz monzonite and granite.", which includes the most common source rocks characteristic of tungsten skarn deposit model. Zoomed-in regions at the bottom shows the effect of different percentage threshold values, with higher threshold resulting in fewer polygons.