With the advent of the era of big data and artificial intelligence (AI), scientific research in geoscience has entered the fourth research paradigm—data-intensive science. Big data, machine learning, and advanced AI models are revolutionizing the study of earth resources and environmental monitoring by enabling unprecedented scale, accuracy, and efficiency. The integration of these technologies facilitates new paradigms for multi-scale ore deposit exploration and comprehensive environmental observation, leading to enhanced decision-making capabilities. As a result, geoscientists are now addressing more complex questions by leveraging massive datasets, sophisticated algorithms, and interdisciplinary approaches. This Research Topic is devoted to highlighting recent progress in the research and application of big data, machine learning, and AI in the broad field of earth science, focusing on new theories, models, and case studies.

This Research Topic aims to bring together innovative, AI-driven research and ideas addressing both current and future challenges in big data mining and machine learning in geoscience. The objective is to showcase the latest advancements in data science, machine learning, and AI as applied to the integration and analysis of multidisciplinary geoscientific data, improving sustainability and efficiency in ore deposit exploration and environmental studies. Contributors are encouraged to present new data-driven methodologies and hybrid approaches for geochemical data analysis, geological modeling, geophysical inversion, mineral prospectivity mapping, and decision-making under geological uncertainty. Furthermore, we seek research that bridges knowledge discovery and explainability in AI models, and that explores the rapidly-evolving landscape of large language models and knowledge graphs. The ultimate goal is to foster the development of robust, interpretable, and impactful solutions for the next generation of geoscience challenges.

We invite original contributions, reviews, methodologies, and case studies that explore, apply, or advance big data and machine learning in geosciences. Areas of interest include, but are not limited to:
• Machine learning for data-driven discovery in geoscience;
• Mineral prospectivity mapping based on neural networks;
• Graph inference algorithms for detecting geochemical anomalies from exploration data;
• Identification of ore targets using deep convolutional neural networks;
• Rock image classification using deep convolutional neural networks;
• Construction and application of knowledge graphs for soil pollution management;
• Interpretable landslide prediction with knowledge-guided deep learning;
• Retrieval-augmented large language models and their geoscience applications;
• Unifying large language models and knowledge graphs;
• Information extraction and knowledge graph construction from geoscience literature;
• Machine learning-based field geological mapping;
• Exploring large language models for knowledge graph completion;
• Data-driven mineral prospectivity mapping using association rules and known deposits;
• Machine learning strategies for lithostratigraphic classification from geochemical data;
• Physically constrained hybrid deep learning models for mining geochemical data cubes;
• Application of graph convolutional networks in geoscience;
• Data-Knowledge dual-driven models coupling AI with mineral systems approaches;
• Explainable AI models for mineral prospectivity mapping;
• Monitoring mining activity and vegetation recovery in mining areas.

We dedicate this Research Topic to the 10th anniversary of the Committee on Big Data and Mathematical Geosciences of the Chinese Society for Mineralogy, Petrology, and Geochemistry.

Are you interested in contributing a manuscript to this Research Topic?

Big Data Mining & Artificial Intelligence in Earth Science