Big Data and AI propel geoscience into new digital era

The search for mineral resources is entering a new era, one powered less by hammers and field notebooks and more by algorithms capable of scanning vast geological datasets in seconds. Across exploration sites and research labs, artificial intelligence is rapidly becoming a primary force in how scientists detect ore systems, model subsurface structures, and interpret complex Earth processes.

A new study published in the journal Minerals sheds light on this sweeping shift. Titled Big Data and AI in Geoscience: From Data to Discovery—A Thematic Overview, the study compiles 17 research papers that map the rapid integration of machine learning, deep learning, and knowledge graph technologies into modern geoscience

Machine learning drives a new era in mineral prospectivity prediction

Mineral exploration has long relied on empirical judgment, sparse drilling data, and traditional statistical models. However, rising global demand for critical minerals and the increasing complexity of ore systems have exposed the limitations of these methods. The overview highlights a major shift toward machine learning–based mineral prospectivity mapping, in which algorithms analyze high-dimensional geological, geochemical, and geophysical datasets to detect nonlinear mineralization patterns.

Supervised learning models such as random forests, support vector machines, and convolutional neural networks are now widely deployed to integrate multi-source evidence layers. These approaches generate predictive maps that surpass traditional techniques in both accuracy and reproducibility. Deep learning architectures, including graph convolutional networks and transformer-based models, are particularly effective at capturing spatial dependencies and contextual relationships across large geological domains.

Several studies within the Special Issue demonstrate advances in hybrid model design. Transformer–GCN fusion frameworks combine global attention mechanisms with topological feature extraction to improve predictive performance in complex ore districts. Data imbalance, a persistent challenge in mineral exploration due to limited ore deposit samples, is addressed through generative adversarial networks capable of synthesizing high-fidelity geological samples for model training. Ensemble learning methods further enhance prediction by optimizing feature selection across dozens of geological variables.

The overview also traces the evolution of machine learning in mineral prospectivity over the past decade. A review of 255 scientific studies between 2016 and 2025 shows a clear disciplinary shift toward deep learning, automatic feature extraction, transfer learning, and few-shot learning. These tools enable scalable prediction even in data-scarce terrains and structurally complex geological settings. Collectively, the research signals a new paradigm in predictive mineral system analysis, offering data-informed frameworks that support sustainable resource exploration.

Knowledge graphs and  LLMs transform geological knowledge engineering

AI is changing the way geological knowledge itself is structured and retrieved. The authors identify knowledge graphs and large language models as a powerful dual-engine framework for intelligent geoscientific research.

Knowledge graphs organize geological entities such as tectonic units, mineralization processes, and deposit types into structured networks that capture spatial and causal relationships. These systems enable interpretable reasoning across heterogeneous datasets, but traditional construction methods are labor-intensive and difficult to scale.

Large language models are emerging as a solution to this bottleneck. Domain-adapted models automate ontology definition, entity recognition, and relation extraction from vast corpora of geological literature. When integrated with retrieval-augmented generation frameworks, they enable dynamic, context-aware question answering and high-precision knowledge graph construction.

Case studies in the Special Issue illustrate how these integrated systems outperform general-purpose language models in terminology understanding and causal chain modeling within metallogenic domains. Semantic-aware fusion frameworks address challenges arising from multilingual and heterogeneous data sources, resolving inconsistencies and preserving provenance in dynamic geological knowledge graphs.

The authors emphasize that this convergence of symbolic reasoning and generative AI could support intelligent decision-making systems for mineral exploration, resource evaluation, and digital geoscience research. Yet challenges remain. Unified semantic ontologies, physical consistency checks, and integration with numerical models are necessary to ensure interpretability and reliability.

Deep learning revolutionizes geological modeling and data inversion

Geological data inversion and subsurface modeling have historically been constrained by sparse observations and ill-posed mathematical formulations. Traditional linear inversion techniques often struggle to capture complex, nonlinear geological patterns. The overview details how deep learning architectures are transforming this landscape.

Convolutional neural networks excel at extracting spatial features from gridded seismic and geochemical data, enabling high-resolution property prediction. Generative adversarial networks and variational autoencoders facilitate realistic geological model generation and uncertainty quantification. Physics-informed neural networks integrate governing physical equations directly into the learning process, ensuring that predictions respect geological principles.

Transformer-based models and graph neural networks extend these capabilities to irregularly sampled datasets such as borehole logs and fault networks. A transformer-based 3D geological modeling approach discussed in the Special Issue demonstrates superior predictive accuracy and uncertainty estimation under sparse borehole conditions compared with traditional interpolation methods.

AI is also advancing real-time mineral identification and drilling analysis. Object detection models based on YOLOv8 algorithms have achieved high precision in classifying mineral samples from image datasets. Machine learning regression models using measure-while-drilling data show strong performance in predicting geophysical signatures such as density and resistivity. Neural network approaches optimized with particle swarm algorithms offer reliable alternatives for vertical electrical sounding inversion.

In multivariate resource modeling, ensemble Kalman filter frameworks combined with multi-Gaussian transformations enable efficient updating of complex geological models as new data become available. These advances collectively signal a shift toward data-driven, physics-aware inversion workflows capable of handling the scale and complexity of modern exploration datasets.

Geological databases and big data mining anchor the digital transition

The authors argue that the success of AI in geoscience ultimately depends on robust data infrastructures. Geological databases now serve as centralized repositories for lithological descriptions, geochemical assays, geophysical surveys, mineral deposit records, and remote sensing imagery. As sensor technologies and automated drilling systems expand data volume and velocity, traditional processing approaches are no longer sufficient.

Big data mining techniques enable extraction of hidden correlations and patterns from multi-source datasets, supporting mineral exploration, reservoir characterization, and geohazard assessment. Interoperable databases enhance cross-disciplinary collaboration and reproducibility, key factors in sustainable scientific advancement.

One highlighted example is the development of standardized gemstone databases built on FAIR principles and blockchain-supported architectures. These systems aim to overcome limitations in data interoperability and completeness, facilitating the integration of machine learning tools into gemological research and authentication practices.

Clustering methods capable of handling both categorical and continuous variables further expand the analytical toolkit. Two-step clustering approaches applied to mineral prediction demonstrate the value of combining geophysical discretization with geochemical factor analysis to identify favorable exploration zones.

The overview acknowledges persistent challenges. Data heterogeneity, inconsistent standards, computational scalability, and semantic inconsistencies across multilingual sources complicate integration. Addressing these barriers will require coordinated efforts in data governance, ontology standardization, and cross-institutional collaboration.