Where Machines Look for Nothing: Smarter Negative Samples Transform AI Mineral Mapping

One of the most stubborn problems in artificial intelligence–driven mineral exploration has nothing to do with finding ore. It has to do with finding nothing. Supervised machine learning models that predict where copper deposits are likely to occur need two kinds of training data: locations of known mineral occurrences, and locations where mineralization is confidently absent. The first category is well documented in exploration databases. The second is notoriously elusive, because geologists rarely spend time cataloguing places where they did not find anything. A new study published in Earth Science Informatics tackles this quiet crisis head-on, proposing a framework that combines geological reasoning with statistical point pattern analysis to choose non-deposit locations in a way that is defensible rather than arbitrary.

The research, led by Mobin Saremi of Amirkabir University of Technology together with colleagues at the University of Tehran, University Malaysia Terengganu, Damghan University, and Hamedan University of Technology, focuses on hydrothermal copper mineralization in the Feizabad district of northeastern Iran. This region sits within the Khaf-Kashmar-Bardaskan magmatic belt, a setting where Eocene granitoid intrusions, volcanic units, and fault networks have combined to produce a variety of copper occurrences. The team’s central insight is that the choice of negative training samples is not a technical afterthought but a first-order source of uncertainty that can reshape an entire prospectivity model.

Mineral prospectivity mapping, or MPM, is the practice of converting layers of geological evidence—geochemical surveys, structural maps, proximity to intrusive bodies, remote sensing signatures of hydrothermal alteration—into a continuous surface that ranks every pixel of a landscape by its likelihood of hosting a deposit. In supervised versions of this exercise, an algorithm such as a random forest learns the multivariate fingerprint of known deposits and then applies that fingerprint everywhere else. But a random forest, like any discriminative classifier, needs counterexamples. If the negative samples are drawn randomly from the map, they may accidentally include undiscovered mineralization or, conversely, exclude terrain that is geologically almost identical to productive ground. Either mistake teaches the model the wrong lesson.

The authors’ solution is a two-part strategy. First, they impose geological constraints: non-deposit points are only permitted in areas where the geology itself argues against hydrothermal copper systems, with the position of intrusive rocks serving as a key control on where negatives may and may not be placed. Second, they apply point pattern analysis, a family of spatial statistics developed to test whether events are clustered, dispersed, or randomly distributed, to ensure that the chosen negatives are spatially coherent and do not sit suspiciously close to known mineral occurrences. From this framework, the team generated ten distinct negative datasets, each representing a different plausible answer to the question of where copper is not.

Each of those ten datasets was then fed into a random forest classifier whose hyperparameters were tuned by a gray wolf optimizer, a metaheuristic algorithm inspired by the social hunting behavior of wolf packs. The result was ten full prospectivity models for the same district, differing only in the spatial distribution of their negative training points. That experimental design turns a methodological nuisance into a measurable quantity: by comparing the ten maps, the researchers could quantify exactly how sensitive the predicted mineral potential is to a decision that most studies make once, silently, and never test.

The findings are striking. Many of the high-potential zones predicted by the optimized random forest show clear spatial association with known mineral occurrences and with geological features favorable for hydrothermal copper mineralization, confirming that the models are learning genuine geological signal. Yet the spatial patterns of the ten prospectivity maps vary visibly depending on where the negative points were placed. Model performance, measured by normalized density values, ranged from 4.88 to 13.28 across the ten models, with an average of 8.018. In practical terms, a mining company using one of the weaker negative datasets would be looking at a materially different—and less reliable—map of exploration targets than a colleague using a stronger one, even with identical geology, identical algorithms, and identical deposit data.

Feature importance analysis added another layer of insight. Across all ten models, proximity to intrusive rocks and the geochemical concentrations of copper and molybdenum emerged as the most influential predictors, a result that aligns neatly with the porphyry-style mineral systems expected in the belt. Intriguingly, intrusive rocks were not only the top predictor; they were also embedded as a geological constraint during the negative sample selection stage, creating a consistency between how the model was trained and what the model learned to look for. Although the relative importance of individual variables shifted somewhat from model to model as negative locations changed, the ranking of the most influential variables remained generally stable, suggesting that the underlying geological signal is robust even when the training noise is not.

Rather than forcing a choice among ten imperfect maps, the team turned to an ensemble strategy. Using a confidence index, they fused the outputs of all ten models into a single integrated surface that highlights areas where high prospectivity is predicted consistently regardless of which negative dataset was used. This approach, which echoes the majority voting and confidence index methods developed in recent exploration information systems literature, suppresses the uncertainty introduced by any single arbitrary choice of negatives. The resulting high-confidence targets carry substantially lower uncertainty than the targets of any individual model, and several of them are new—areas never previously flagged as priority ground.

The geography of those new targets tells a coherent geological story. They commonly sit on or around intrusive rocks and their associated volcanic units, along faults, at fault intersections, and where faults cut intrusive bodies. These are precisely the settings in which hydrothermal fluids, channeled by structural permeability and heated by magmatic sources, are expected to deposit copper. The convergence between the machine-learned targets and first-principles mineral system theory is the study’s strongest validation: the algorithm, guided by carefully chosen negatives, independently rediscovered the logic of the ore-forming process.

The broader implications reach well beyond one district in northeastern Iran. As exploration companies increasingly hand targeting decisions to machine learning pipelines, the invisible choices embedded in training data curation become financial decisions. A model trained on carelessly placed negatives can direct millions of dollars of drilling toward the wrong ground or, worse, write off prospective terrain before a single core is pulled. By demonstrating that negative sample selection can be constrained geologically, tested statistically, and integrated through ensembles, this work offers a template for making one of exploration data science’s quietest uncertainties explicit, measurable, and manageable. In a discipline racing to apply ever more sophisticated deep learning architectures, the study is a reminder that sometimes the biggest gains come from asking a deceptively simple question with rigor: where, exactly, should the machine learn that there is nothing to find?

Subject of Research: Selection of non-deposit training samples in supervised machine learning-based mineral prospectivity mapping for hydrothermal copper exploration

Article Title: Incorporation of geological constraint-based strategy and point pattern analysis for selection of non-deposit points in supervised mineral prospectivity mapping

Article References: Saremi, M., Mirzabozorg, S. A. A. S., Maghsoudi, A., Hezarkhani, A., Pour, A. B., & Hoseinzade, Z. (2026). Incorporation of geological constraint-based strategy and point pattern analysis for selection of non-deposit points in supervised mineral prospectivity mapping. Earth Science Informatics, 19(10), Article 168. https://doi.org/10.1007/s12145-026-02231-6

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02231-6

Keywords: mineral prospectivity mapping, machine learning, random forest, gray wolf optimizer, non-deposit samples, copper exploration, hydrothermal mineralization, point pattern analysis, geological constraints, Feizabad district, Iran, exploration targeting