About the Book
Big Data Mining and Machine Learning in Geoscience offers a comprehensive overview of techniques and applications of data science in the geosciences. Sections cover essential, foundational concepts in data cleaning and preprocessing, thus ensuring the quality and reliability of geoscientific data. Next, the book explores dimensionality reduction methods designed to simplify high-dimensional data without losing critical information. The text covers classification and prediction techniques that enable the identification of patterns and forecasting of geological phenomena, and graphical data processing and handling of infinite stream data and time series are highlighted, along with their importance in real-time monitoring and dynamic systems analysis.
In addition, the book explores advanced machine learning and deep learning methods, showcasing their transformative impact on geoscientific research. It also introduces knowledge graphs and large language models as emerging tools that enhance data integration, interpretation, and discovery. AI-driven geology is presented as a forward-looking approach that leverages artificial intelligence to revolutionize traditional geological practices, offering improved accuracy and insight. Throughout, practical examples and case studies illustrate how these technologies can be applied to solve complex problems in geoscience.
Table of Contents:
1. Introduction
2. Data Cleaning and Preprocessing
3. Dimensionality Reduction for High-Dimensional Data
4. Classification and Prediction
5. Graphical Data Processing
6. Infinite Stream Data and Time Series
7. Machine Learning and Deep Learning
8. Knowledge Graphs
9. Large Language Models
10. AI-Driven Geology
About the Author :
Zhou Yongzhang, Ph.D. supervisor and professor at Sun Yat-sen University, a foreign academician of the Russian Academy of Engineering, Ph.D. from the University of Quebec, Canada. His main research areas include geochemistry and big data geoscience, big data and intelligent mineral exploration, as well as intelligent monitoring, early warning, and prediction of carbon emissions, carbon sinks, and resource-environment IoT. He has published authored monographs Big Data Mining and Machine Learning in Earth Science (2018) and Mathematical Geoscience (2012) in Chinese, and over 300 academic papers in Chinese or in English. He has supervised nearly 200 doctoral and master's students. His accolades include the IAMG Felix Chayes Award from the International Association for Mathematical Geosciences and the First Prize from the China Invention Association.
His original contributions include proposing a new paradigm for big data and intelligent mineral exploration, achieving groundbreaking results in the metallogenic geological background and intelligent prospecting of the Qin-Hang metallogenic belt, as well as in soil environmental geochemistry and IoT-based intelligent monitoring and early warning of environmental flux in the Pearl River Delta region. He served as Deputy Director of the Ore Deposit Geochemistry Laboratory at the Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, and held leadership roles at Sun Yat-sen University, including Vice Director of the Office of Science and Technology, Director of the Department of Earth Science, Director of the Research Center for Earth Environment and Resources at Sun Yat-sen University. Additionally, he holds concurrent positions such as Council Member and Chair of the Artificial Intelligence & Big Data Geoscience Committee of the Chinese Society for Mineralogy, Petrology and Geochemistry; Chair of the Spatial Big Data Committee of the International Society for Digital Earth (China National Committee).
Fan Xiao, Associate Professor at Sun Yat-sen University. He is a lifetime member of the International Association for Mathematical Geosciences (IAMG) and a core member of the innovation research team on Big Data and Mathematical Geoscience. He has been selected for the High-Level Innovative Talent Program of the Ministry of Natural Resources. His primary research focuses on mineral exploration and mathematical geoscience, particularly computational and data-driven geoscience. To date, he has led six research projects, including the National Natural Science Foundation of China (NSFC), sub-projects of the National Key R&D Program, and the Guangdong Natural Science Foundation. Additionally, he has participated as a key researcher in multiple major projects, such as the Guangdong Pearl River Talent Program, NSFC, National Key R&D Program, and National Science and Technology Planning Projects. He has also overseen one Guangdong provincial teaching reform project. He has published 45 papers, including 32 first/corresponding-author papers (22 SCI, 5 EI, and 5 CSCD-indexed) . His research in geoscience process numerical simulation and data-driven geoscience has yielded significant academic impact, with findings included in international reviews and encyclopedias on mathematical geoscience. His notable awards include: 2015 Hubei Province Outstanding Doctoral Dissertation Award; 2015 Excellent Academic Paper Award, Annual Conference of the Geological Society of China; 2014 Outstanding Youth Paper Award, 13th Symposium on Mathematical Geology and Geoscience Information. Currently, he holds several academic positions, including: Committee Member and Secretary of the Big Data & Mathematical Geoscience Committee, Chinese Society for Mineralogy, Petrology and Geochemistry, Committee Member of the Mathematical Geology and Geoscience Information Committee, Geological Society of China.
Hui Yang, Professor is Professor and doctoral supervisor at China University of Mining and Technology (CUMT), also the Director of the Department of Earth Information Science at the School of Resources and Geosciences, CUMT, the Deputy Director of the Big Data Research Center at the Artificial Intelligence Research Institute. She holds the following academic and professional memberships: Member of the Virtual Geographic Environment Committee, Chinese National Committee of the International Society for Digital Earth (ISDE), Member of the Big Data and Digital Earth Science Committee, Chinese Society for Mineralogy, Petrology and Geochemistry, Senior Member of the China Computer Federation (CCF), Member of the Association for Computing Machinery (ACM). She obtained her Ph.D. in Cartography and Geographic Information Systems from Nanjing Normal University in June 2009 and subsequently joined the faculty of the School of Resources and Geosciences at CUMT. From 2010 to 2014, she conducted postdoctoral research at the Geological Resources and Geological Engineering Postdoctoral Research Station of CUMT. In 2016–2017, she was a visiting scholar at Ryerson University, Canada, under the sponsorship of the China Scholarship Council.
Wenjia Li is a post-doc research fellow at Sun Yat-sen University. Lunhao He is a post-doc research fellow at Sun Yat-sen University.
