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Big Data Mining and Machine Learning in Geoscience (eBook)

Artikelnummer: 978-0-443-51005-2
Einband: Adobe Digital Editions
Verfügbarkeit: Download, sofort verfügbar (Link per E-Mail)
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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. - Offers a systematic introduction to the fundamental framework and principles of big data mining and machine learning in geoscience - Examines machine learning and deep learning applications, along with algorithms specifically for geoscience - Provides relevant Python-based case studies for key applications

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. - Offers a systematic introduction to the fundamental framework and principles of big data mining and machine learning in geoscience - Examines machine learning and deep learning applications, along with algorithms specifically for geoscience - Provides relevant Python-based case studies for key applications

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VerlagElsevier Science & Techn.
EinbandAdobe Digital Editions
Erscheinungsjahr2026
Seitenangabe825 S.
AusgabekennzeichenEnglisch
Masse29'254 KB
PlattformEPUB
AutorZhou, Yongzhang / Xiao, Fan / Yang, Hui / Li, Wenjia / He, Luhao

Über den Autor Yongzhang Zhou

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).

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