Editorial for Special Issue “Smart Exploration of Critical Minerals: Integrating Multi-Source Data for Enhanced Mineral Prospectivity Mapping”

1. Background and Scope of the Special Issue

The accelerating global demand for critical minerals, driven by the energy transition, electrification, and advanced technologies, has intensified the need for more efficient and reliable mineral exploration strategies. Conventional exploration approaches, often reliant on single datasets or linear interpretations, face increasing limitations when applied to deeply buried, structurally complex, or underexplored regions. In response, smart mineral exploration has emerged as an integrative paradigm that combines geological knowledge with multi-source data analytics, artificial intelligence (AI), and machine learning (ML) techniques to enhance mineral prospectivity mapping (MPM).

Recent advances in geophysical methods, geochemical analysis, remote sensing, and computational power have enabled the integration of heterogeneous datasets at multiple spatial scales. When coupled with data-driven and knowledge-driven modeling approaches, these developments provide new opportunities to detect subtle mineralization signals, reduce exploration risk, and improve decision-making in both greenfield and brownfield settings.

The objective of this Special Issue is to showcase methodological advances in, and practical applications of, smart exploration approaches for critical minerals, with a particular focus on (i) integrating multi-source geological information, (ii) applying AI- and ML-based techniques to MPM, and (iii) balancing predictive performance with geological interpretability. Between 2024 and 2025, five peer-reviewed contributions were accepted that collectively address these objectives across a range of geological environments, deposit types, and analytical frameworks.

2. Overview of Contributions

The five papers published in this Special Issue reflect the methodological diversity and conceptual breadth of modern mineral prospectivity mapping, ranging from unsupervised anomaly detection and mineral systems-based modeling to advanced deep learning and graph-based approaches.

Saremi et al. (Contribution 1) investigated the application of unsupervised anomaly detection algorithms, specifically isolation forest (IForest) and deep isolation forest (DIF), for a mineral prospectivity mapping of hydrothermal polymetallic mineralization in northeastern Iran. By avoiding reliance on labeled training data, this study directly addressed a key limitation of supervised ML methods in data-scarce environments. The authors demonstrated that the DIF algorithm, which leverages neural networks to capture non-linear relationships in high-dimensional data, outperformed the traditional IForest approach. Their results highlight the potential of unsupervised learning techniques as effective tools for both greenfield and brownfield exploration.

Mami Khalifani et al. (Contribution 2) applied a mineral systems framework to epithermal gold exploration in northern New Brunswick, Canada, integrating knowledge-driven and data-driven MPM approaches. Key components of the mineral system, such as metal sources, fluid pathways, traps, and geological controls, were translated into mappable criteria and modeled using fuzzy logic, geometric averaging, and logistic regression. The results demonstrated high predictive accuracy, particularly for the fuzzy logic model, and showed strong spatial correspondence between high-favorability zones and known mineralization. This contribution illustrates how mineral systems concepts can be effectively combined with quantitative modeling to improve exploration outcomes in covered and structurally complex terrains.

Karbalaeiramezanali et al. (Contribution 3) addressed the critical challenge of distinguishing fertile from barren intrusive rocks prior to their use in MPM. Focusing on adakites associated with porphyry Cu–Au systems, the authors employed multiple ML classification algorithms to discriminate fertile and barren adakites based on geochemical data. Support vector machine models achieved the highest predictive performance, while feature selection analyses identified key trace elements and element ratios linked to fertility. This study emphasizes that not all intrusions are inherently prospective and demonstrates how ML-based geochemical screening can refine input layers for prospectivity mapping.

Tang et al. (Contribution 4) presented an integrated ML-based framework for targeting porphyry Cu–polymetallic deposits in the Narigongma district of the Tibetan Plateau. Remote sensing-derived alteration information was combined with geological, geophysical, and geochemical datasets to enrich the data environment in an underexplored region. Multiple ML algorithms were evaluated, with artificial neural networks achieving the highest classification accuracy and random forest models showing strong targeting efficiency. The study underscores the value of remote sensing and multi-source data fusion for exploration in regions with limited prior exploration.

Sheng et al. (Contribution 5) introduced a Knowledge–Data Collaboration Graph Attention Network (KDCGAT) for copper prospectivity prediction in the eastern Tien Shan belt, China. By integrating geological structures, geochemical anomalies, and alteration patterns within a graph-based deep learning framework, the model captured spatial correlations and complex interactions that are difficult to represent using conventional ML approaches. The proposed method outperformed traditional statistical and deep learning models, highlighting the growing role of graph neural networks and knowledge–data collaboration in next-generation mineral exploration.

3. Emerging Themes and Methodological Insights

Several unifying themes emerge from the contributions to this Special Issue. First, the integration of multi-source data is consistently shown to enhance predictive performance and geological relevance. Whether through the incorporation of alteration mapping, geochemical fertility indicators, or structural information, data fusion plays a central role in smart exploration workflows.

Second, the studies highlight a clear shift beyond traditional supervised ML toward more flexible learning strategies, including unsupervised anomaly detection and graph-based deep learning. These approaches are particularly well-suited to data-sparse or geologically complex environments, which are increasingly the focus of critical mineral exploration.

Third, there is a growing emphasis on geological interpretability and mineral systems’ consistency. High model accuracy alone is insufficient; models must also align with established geological knowledge to be trusted and adopted by exploration practitioners. Several contributions explicitly address this need by linking model outputs to known ore-forming processes and geological controls.

4. Future Research Directions

Building on the advances presented in this Special Issue, future research should prioritize the development of hybrid frameworks that more tightly couple mineral systems knowledge with AI-driven analytics. Explainable AI, uncertainty quantification, and 3D–4D prospectivity modeling represent particularly important areas for further investigation. In addition, transfer learning and foundation models offer promising opportunities to leverage global datasets for critical minerals while reducing exploration risk in underexplored regions.

Finally, smart exploration approaches should increasingly consider sustainability and responsible resource development, ensuring that improved targeting efficiency contributes not only to economic success but also to reduced environmental impact.

5. Concluding Remarks

The papers collected in this Special Issue demonstrate the rapid evolution of mineral prospectivity mapping (MPM) toward more integrated, intelligent, and geologically informed exploration strategies. By combining multi-source data, advanced machine learning techniques, and mineral systems thinking, these contributions provide valuable insights and practical tools for the exploration of critical minerals. We hope that this Special Issue will stimulate further innovation and interdisciplinary collaboration in smart mineral exploration.