GEOLOGICAL PATTERN RECOGNITION USING GENERATIVE AI: A NOVEL APPLICATION

Recent advances in artificial intelligence have significantly transformed geoscientific research, particularly in the analysis and interpretation of complex geological data. Traditional geological pattern recognition relies heavily on manual interpretation, rule-based models, and conventional machine learning techniques, which often struggle with high-dimensional, noisy, and incomplete datasets. Generative Artificial Intelligence (GenAI), including Generative Adversarial Networks, Variational Autoencoders, and diffusion-based models, offers a novel paradigm by learning underlying data distributions and generating realistic representations of geological structures. This chapter conceptually examines the application of Generative AI for geological pattern recognition, focusing on subsurface imaging, mineral exploration, fault and fracture detection, stratigraphic modelling, and seismic interpretation. It discusses how GenAI enhances feature extraction, anomaly detection, data augmentation, and uncertainty quantification in geoscience workflows. The chapter synthesizes existing literature to highlight methodological advancements, practical benefits, and emerging challenges related to data quality, model interpretability, and domain adaptation. By integrating geological knowledge with generative modelling, this chapter demonstrates how GenAI can support more accurate, efficient, and scalable geological analysis. The discussion provides insights for researchers, geologists, and decision-makers seeking to adopt next-generation AI tools for sustainable resource exploration, hazard assessment, and earth system understanding.