GeoChemAD: Benchmarking Unsupervised Geochemical Anomaly Detection for Mineral Exploration

TL;DR

This paper introduces GeoChemAD, an open-source benchmark dataset for geochemical anomaly detection across multiple regions, and proposes GeoChemFormer, a transformer-based model that outperforms existing methods in accuracy and generalization.

Contribution

The paper provides the first multi-region benchmark dataset for geochemical anomaly detection and introduces a novel transformer-based framework leveraging self-supervised learning.

Findings

GeoChemFormer outperforms existing methods in detection accuracy.

The dataset enables reproducible research across diverse geochemical scenarios.

Transformer-based models show superior generalization in anomaly detection.

Abstract

Geochemical anomaly detection plays a critical role in mineral exploration as deviations from regional geochemical baselines may indicate mineralization. Existing studies suffer from two key limitations: (1) single region scenarios which limit model generalizability; (2) proprietary datasets, which makes result reproduction unattainable. In this work, we introduce \textbf{GeoChemAD}, an open-source benchmark dataset compiled from government-led geological surveys, covering multiple regions, sampling sources, and target elements. The dataset comprises eight subsets representing diverse spatial scales and sampling conditions. To establish strong baselines, we reproduce and benchmark a range of unsupervised anomaly detection methods, including statistical models, generative and transformer-based approaches. Furthermore, we propose \textbf{GeoChemFormer}, a transformer-based framework that leverages self-supervised pretraining to learn target-element-aware geochemical representations for spatial samples. Extensive experiments demonstrate that GeoChemFormer consistently achieves superior and robust performance across all eight subsets, outperforming existing unsupervised methods in both anomaly detection accuracy and generalization capability. The proposed dataset and framework provide a foundation for reproducible research and future development in this direction.