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

TL;DR

QueryPlot is a novel system that uses natural language processing to generate geological evidence layers for mineral exploration, integrating textual deposit models with geospatial data for improved prospectivity mapping.

Contribution

It introduces a semantic retrieval framework that combines geological text corpora with geospatial data using NLP, enabling natural language queries to produce evidence layers for mineral prospectivity.

Findings

High recall of known mineral deposit occurrences.

Prospective regions align with expert-defined areas.

Embedding scores improve supervised classification performance.

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.