Machine Learning and Artificial Intelligence in Mining Exploration: A Review of Applications and Potential

Ridwan Olalekan Alabi¹ , Moses Adondua Abah² , Micheal Abimbola Oladosu² , Nathan Rimamsanati Yohanna²

¹Department of Mining Engineering, Federal University of Technology, Akure, Nigeria

²ResearchHub Nexus Institute, Nigeria

Corresponding Author Email: m.abah@fuwukari.edu.ng

DOI : https://doi.org/10.51470/eSL.2025.6.4.22

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Introduction

Mining exploration is a fundamental activity that drives economic development by identifying and extracting mineral resources essential to modern society. It plays a crucial role in supplying the raw materials needed for a wide range of industries, from infrastructure and construction to electronics and energy production, thereby underpinning global economic growth and technological innovation [1, 2]. The mining sector supports employment, generates tax revenue, and stimulates local economies, with resource-rich countries often leveraging mining as a foundation for broader economic diversification and development [3, 4]. Despite its foundational importance, traditional mining exploration faces significant challenges related to high operational costs, lengthy timelines, and substantial environmental impacts, including habitat destruction and pollution [5].

Moreover, there is an increasing need for innovation driven by growing demand for critical resources such as rare earth elements and battery metals, which are essential for technologies including electric vehicles, renewable energy systems, and advanced electronics [6]. The global demand for rare earth elements is projected to exceed 220,000–250,000 metric tons by 2025, reflecting surging infrastructure development and the green energy transition. This increased demand, coupled with societal and regulatory pressure to reduce mining’s environmental footprint, intensifies the urgency to develop sustainable and efficient exploration methodologies [7]

Mining exploration traditionally relies on geological surveying, geophysical and geochemical techniques, and drilling programs, which are data-intensive and laborious processes. These methods often require significant financial investment, with global mineral exploration spending estimated at approximately $11 billion in 2021 [8]. Yet, the success rate of discovering new economically viable mineral deposits is below 1%, highlighting the inefficiencies and risks inherent in conventional exploration practices. Moreover, exploration activities contribute to environmental degradation through biodiversity loss, soil erosion, water contamination, and community disruption, necessitating greater emphasis on sustainable practices and social responsibility [9].

The traditional versus AI-enhanced exploration metrics further illustrate the transformative potential of emerging technologies. While traditional methods are characterized by high costs, long timelines, and low success rates, AI-enabled exploration can significantly improve prediction accuracy, reduce operational expenses, accelerate decision-making, and minimize environmental disturbances [10, 11].

The advent of Artificial Intelligence (AI) and Machine Learning (ML) offers a promising avenue to overcome these traditional challenges. AI refers to the broader concept of machines designed to simulate human intelligence, including reasoning, decision-making, and problem-solving abilities. ML, a subset of AI, specifically focuses on algorithms that enable machines to learn patterns from data and improve their performance without explicit programming [12, 13]. This distinction is important: while AI encompasses all intelligent system capabilities, ML is particularly data-driven and crucial for predictive analytics and pattern recognition.

The mining industry, characterized by massive and complex datasets from geological, geophysical, and geochemical sources, is well well-suited for AI and ML applications. These technologies can enhance exploration by integrating various data types to generate more accurate predictive models of mineral deposits, optimize optimizing drilling targets, and reduce reducing the uncertainty and costs associated with exploration [14, 15]. AI-driven analytics support faster decision-making and enable proactive management of environmental and operational risks, promoting safer and more sustainable mining practices.

This review delineates the current applications, benefits, and potential of AI and ML technologies in mining exploration. It will examine how these tools are being used to address the core problems of traditional exploration, including high operational costs, low discovery success rates, and environmental impacts. The discussion will be supported by statistical context, such as global exploration expenditures and discovery success rates, to underline the pressing need for innovation. Additionally, the review will highlight advancements in AI and ML-driven techniques that increase exploration efficiency and sustainability, drawing attention to promising research trends and the challenges that remain for widespread adoption. By synthesizing these insights, this review offers valuable perspectives for researchers, industry practitioners, and policymakers aiming to harness intelligent technologies for responsible and efficient mineral resource development [16].

Literature Review

Artificial Intelligence (AI) and Machine Learning (ML) underpin modern advances in mining exploration. A fundamental distinction lies between supervised and unsupervised learning. Supervised learning uses labeled datasets where input-output pairs guide the model to predict outcomes or classify new data, making it suitable for tasks like resource estimation or ore grade prediction. In contrast, unsupervised learning works with unlabeled data to uncover hidden patterns or structures, such as clustering geological features or anomaly detection [17, 18]. Typical ML models in mining include neural networks, decision trees, and clustering algorithms. Neural networks, inspired by the human brain, are effective for complex pattern recognition and nonlinear relations. Decision trees operate by recursive data splitting based on feature decisions, aiding interpretability. Clustering groups data points by similarity, instrumental in identifying mineralization zones without prior labels [19, 20].

Data Sources in Mining

Mining exploration leverages diverse data streams: geophysical surveys capture subsurface physical properties; geochemical analyses measure elemental concentrations in soils and rocks; satellite imagery provides spatial and temporal environmental context; and drill logs record lithological and structural details from boreholes. These heterogeneous data types, often large-scale and multi-modal, are fundamental inputs for ML models to predict mineral prospects and geological structures [1].

Integration Challenges

Despite these opportunities, integrating diverse mining data into ML presents challenges. Data quality issues such as noise, missing values, and inconsistencies can degrade model reliability. The heterogeneity across data types – spatial, temporal, and semantic differences complicates preprocessing and feature extraction. The sheer volume of exploration data demands scalable storage and computational solutions. Addressing these requires robust data cleaning, normalization, and harmonization methods alongside advanced modeling techniques capable of handling complex, multimodal inputs [22].

1 illustrates the workflow of machine learning (ML) integration in mining exploration as a continuous, iterative process. It begins with data acquisition, where geological, geophysical, geochemical, and satellite information is collected from diverse sensors and remote sources. The raw data then undergoes preprocessing, involving cleaning to remove inconsistencies, normalization to ensure comparability, and feature engineering to extract relevant attributes. Once prepared, the data is used for model selection and training, where supervised or unsupervised ML algorithms are applied to detect patterns and relationships. The trained models are subsequently evaluated through cross-validation and field verification to confirm predictive accuracy and reliability. Based on these validated outputs, decision-making is guided toward exploration strategies such as target prioritization and drilling planning. The workflow concludes with iterative improvement, where new exploration data are continuously incorporated to refine models, enhance accuracy, and reduce risks. This cyclical flow demonstrates how ML transforms exploration into a data-driven, adaptive, and efficient system, improving discovery success rates while lowering costs and minimizing environmental impacts [2, 3].

AI/ML in Geological Data Analysis

Artificial Intelligence (AI) and Machine Learning (ML) have become transformative tools in geological data analysis within mining exploration, especially for geospatial pattern recognition and predictive modeling. ML techniques facilitate the identification of mineralization zones by effectively analyzing complex geophysical, geochemical, and remote sensing datasets that are often too voluminous or intricate for traditional manual methods [4]. Geospatial pattern recognition, a central application, uses ML to detect spatial correlations and anomalies in geological data, enabling more precise mapping of prospective mineralized zones.

Remote sensing and satellite imagery are increasingly integrated with ML for anomaly detection, improving the resolution and accuracy of mineral prospecting in vast or inaccessible regions. ML models such as Random Forest and Support Vector Machines have been successfully applied to process multispectral and hyperspectral imagery for delineating alteration zones associated with mineral deposits [5]. These models impart predictive power to geological surveys by automating data fusion and reducing subjective interpretation errors.

Several case studies exemplify the benefits of ML in practice. Notably, Rio Tinto has employed ML algorithms for copper exploration to enhance drill targeting and reduce exploration costs by mining rich, concealed deposits more efficiently [6]. Their approach integrates diverse datasets, including geochemical assays and geophysical surveys within predictive modeling frameworks.

Predictive modeling and target generation are core benefits of ML, where models analyze historical and current datasets to generate prospectivity maps highlighting high-potential zones. Probabilistic models like Bayesian networks and ensemble methods such as Random Forests provide robust frameworks to evaluate uncertainties and improve prediction reliability [7]. Performance metrics, including accuracy, precision, and recall, are essential to assess how well these models predict mineralization zones, enabling continual refinement.

A typical predictive modeling pipeline begins with data acquisition, followed by preprocessing (cleaning and feature engineering), model training with historical known deposits data, validation using field data, and ultimately generating prospectivity maps to guide exploration [8].

Random Forest, an ensemble of decision trees, excels at handling large and noisy datasets and is widely applied for lithological classification and anomaly detection. Support Vector Machines (SVM) are valued for their ability to handle high-dimensional, nonlinear data, effectively classifying mineral alteration zones. Neural networks offer increased capacity for modeling complex geological relationships and 3D structures, beneficial for detailed rock type prediction. Clustering algorithms like K-means or DBSCAN are unsupervised methods that group spatially coherent mineral zones without prior labels, facilitating the discovery of new mineralization patterns [9]. The effective deployment of these algorithms, supported by adequate data preprocessing and integration, drives the enhanced accuracy and efficiency of modern geological data analysis.

Table 2 illustrates commonly used ML algorithms in geological data interpretation, each chosen for its specific strengths.

Predictive Modeling and Target Generation

Predictive modeling and target generation represent cornerstone applications of machine learning (ML) in mining exploration, particularly through mineral prospectivity mapping (MPM). MPM leverages ML algorithms to predict high-potential zones for undiscovered mineral deposits by systematically integrating geological, geophysical, geochemical, and remote sensing data alongside mineral systems theory [9, 10] Probabilistic models such as Bayesian networks and ensemble decision forests (e.g., Random Forest) provide powerful frameworks that handle uncertainty and complex relationships in spatial data, enhancing the reliability of prospectivity predictions. Performance metrics like accuracy, precision, and recall are vital to evaluate and refine the effectiveness of these predictive models in identifying mineralization zones [11].

 typically illustrates the predictive modeling pipeline in mining exploration. It begins with data collection (various geoscience datasets), followed by preprocessing steps including data cleaning and feature engineering. Subsequently, ML models are trained and validated using known deposit data. Final outputs are prospectivity maps that guide exploration decisions and drilling programs [14].

Robotics and Automation in Field Exploration

Robotics and automation have revolutionized field exploration in mining by introducing AI-driven drones and autonomous rovers that conduct high-resolution and extensive data collection autonomously. These technologies allow for comprehensive surveying of large and often remote or hazardous areas without the need for constant human presence, significantly reducing risks to personnel. The drones and rovers are equipped with advanced sensors, including LiDAR, hyperspectral and multispectral cameras, and geochemical sensors.

A crucial component of this technological advancement is sensor fusion, which integrates multiple data streams in real time. This integration enhances situational awareness and enables precise anomaly detection, environmental mapping, and efficient decision-making during exploration activities [15]. By combining diverse real-time data types, field teams receive a more detailed and actionable understanding of the geological environment compared to traditional methods.

compares manual field surveys with autonomous surveys. Manual surveys, while historically the norm, involve labor-intensive sampling and interpretation, limited coverage, longer durations, higher human risk, and more environmental disturbances. In contrast, autonomous surveys using AI-powered drones and rovers offer expanded coverage due to their ability to operate continuously over large terrains, reduced human exposure to dangerous conditions, increased data consistency through standardized sensor operation, and faster turnaround times, which accelerate exploration decision-making. This comparison underscores the benefits of adopting automation for more efficient, safer, and environmentally sustainable mining exploration [18, 19].

Environmental and Sustainability Applications

Artificial Intelligence (AI) plays an increasingly vital role in mining sustainability by enabling predictive assessment of ecological disruption. Machine learning (ML) models analyze complex environmental data to forecast potential impacts of mining activities on ecosystems, helping companies mitigate risks proactively [21]. In waste management, AI optimizes tailings disposal through predictive maintenance and spatial planning, reducing environmental hazards and improving operational safety (ScienceDirect, 2024). Additionally, AI-driven smart logistics streamlines energy use and material transport, contributing significantly to carbon footprint reduction [17].

depicts the AI-Driven Sustainability Model in Mining visually encapsulates this holistic sustainability integration. The model begins with continuous environmental data collection via sensors and remote monitoring. AI processes this data through predictive algorithms to assess ecological risks and optimize waste management strategies. Subsequently, smart logistics modules allocate resources efficiently, minimizing energy consumption and emissions. Feedback loops ensure adaptive adjustments in real time, enabling mining operations to balance productivity with environmental stewardship. This diagram illustrates how AI intertwines impact assessment, waste optimization, and carbon reduction into a unified, dynamic system for sustainable mining [22].

Economic and Operational Benefits

AI-driven technologies in mining exploration and drilling have revolutionized cost reduction and operational efficiency. By automating complex tasks such as data processing, mineral analysis, and drilling optimization, AI significantly lowers operational expenses. Companies report cost savings of up to 30%, driven by reduced labor costs, minimized downtime, and enhanced resource allocation [23]. Faster data processing and improved decision-making speed up exploration cycles, reducing the time between discovery and production.

Table 4 provides a clear comparison of the economic impact of AI adoption in mining companies. It breaks down key metrics such as cost reduction percentage, time savings in project phases, and return on investment (ROI). The table highlights that companies implementing AI technologies not only experience significant cost savings but also realize faster project completions, leading to improved profitability. For example, autonomous drilling and predictive maintenance have reduced downtime and maintenance costs by a notable margin, contributing directly to bottom-line improvements. Furthermore, data-driven decision systems enable more accurate targeting, reducing wasted expenditure on ineffective exploration efforts

Limitations and Ethical Considerations

AI integration in mining brings considerable ethical and practical challenges that must be carefully managed to ensure responsible deployment. Key concerns include data privacy and ownership, where sensitive geological and personal data must be protected from misuse, bias in algorithms  which that may propagate or exacerbate social or operational inequalities, workforce displacement resulting from automation, and regulatory gaps that struggle to keep pace with rapidly evolving technology [25].

Future Directions and Research Opportunities

Future directions and research opportunities in AI applications in mining exploration are rapidly evolving, with significant promise in hybrid models, open data initiatives, and emerging frontiers such as deep-sea and space mining [26].

Hybrid models represent an advanced approach by combining AI with expert systems to leverage the strengths of both symbolic reasoning and machine learning. These systems integrate human expert knowledge with data-driven algorithms, offering enhanced interpretability, robustness, and adaptability in complex geological problems. Such integration addresses the limitations of pure ML models by providing explanations for predictions, which is critical for trust and validation in mining applications [27]. Studies have noted the growing popularity and effectiveness of neuro-fuzzy, rough neural, and connectionist expert systems in industrial settings, where they tackle classification, prediction, and decision-making tasks with improved accuracy and reliability.

Open data initiatives foster collaborative platforms where geological data from multiple sources and stakeholders are shared openly, enhancing transparency and accelerating innovation. These platforms enable companies and researchers to access richer datasets, facilitating more comprehensive and validated ML models for mineral prospectivity mapping and resource evaluation. Collaboration across industry and academia via shared data repositories promotes standardization and scalability of AI applications, addressing current data fragmentation challenges [27].

AI’s application is extending into new frontiers such as deep-sea mining and space mineral exploration, representing emerging research opportunities. These domains involve unique challenges, including extreme environmental conditions, data scarcity, and operational constraints, demanding adaptations of AI algorithms for autonomous operation and remote decision-making. Utilization of AI here may enable the discovery and sustainable extraction of critical minerals beyond terrestrial sources, significantly impacting future resource security [28]

Conclusion and Recommendations

The integration of Artificial Intelligence (AI) and Machine Learning (ML) is significantly transforming the mining industry by enhancing operational efficiency, safety, and sustainability. Strategic investments in AI infrastructure, fostering interdisciplinary collaboration, and promoting ethical AI use are critical recommendations to maximize these technologies’ benefits. Mining companies investing in AI report improved forecasting, reduced equipment failures, and optimized exploration and production processes, thus creating new industry standards for operational excellence.

To fully leverage AI’s potential, it is essential to build a robust infrastructure that supports data integration, real-time analytics, and automation. Collaborative efforts among geologists, data scientists, environmental experts, and policymakers can accelerate innovation and ensure AI tools meet practical mining needs while addressing ethical concerns such as data privacy, algorithmic bias, and workforce impacts. Promoting ethical AI involves transparent algorithms, protecting sensitive data, and designing systems that augment rather than displace human expertise.

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