Overview
- Presents a step-by-step guide to Machine Learning for Earth Scientists
- Introduces Geologists to Machine Learning
- Contains example applications
Part of the book series: Springer Textbooks in Earth Sciences, Geography and Environment (STEGE)
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Table of contents (12 chapters)
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Front Matter
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Basic Concepts of Machine Learning for Earth Scientists
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Front Matter
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Unsupervised Learning
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Front Matter
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Supervised Learning
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Front Matter
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Scaling Machine Learning Models
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Front Matter
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Next Step: Deep Learning
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Front Matter
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Keywords
About this book
This textbook introduces the reader to Machine Learning (ML) applications in Earth Sciences. In detail, it starts by describing the basics of machine learning and its potentials in Earth Sciences to solve geological problems. It describes the main Python tools devoted to ML, the typical workflow of ML applications in Earth Sciences, and proceeds with reporting how ML algorithms work. The book provides many examples of ML application to Earth Sciences problems in many fields, such as the clustering and dimensionality reduction in petro-volcanological studies, the clustering of multi-spectral data, well-log data facies classification, and machine learning regression in petrology. Also, the book introduces the basics of parallel computing and how to scale ML models in the cloud. The book is devoted to Earth Scientists, at any level, from students to academics and professionals.
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Authors and Affiliations
About the author
Maurizio Petrelli is an associate professor in petrology and volcanology at the Department of Physics and Geology, University of Perugia. In 2001, he graduated in Geology and obtained his Ph.D. in February 2006 at the University of Perugia. His current studies are focused on the petrological, volcanological, and geochemical characterization of magmatic systems with particular emphasis on time-scales estimates of magmatic processes. He combines the use of numerical simulations, experimental petrology, and the study of natural samples. Since 2016, he has developed a new line of research at the Department of Physics and Geology (University of Perugia) focused on the application of Machine Learning techniques to petrological and volcanological studies.
Bibliographic Information
Book Title: Machine Learning for Earth Sciences
Book Subtitle: Using Python to Solve Geological Problems
Authors: Maurizio Petrelli
Series Title: Springer Textbooks in Earth Sciences, Geography and Environment
DOI: https://doi.org/10.1007/978-3-031-35114-3
Publisher: Springer Cham
eBook Packages: Earth and Environmental Science, Earth and Environmental Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-35113-6Published: 23 September 2023
Softcover ISBN: 978-3-031-35116-7Due: 24 October 2023
eBook ISBN: 978-3-031-35114-3Published: 22 September 2023
Series ISSN: 2510-1307
Series E-ISSN: 2510-1315
Edition Number: 1
Number of Pages: XVI, 209
Number of Illustrations: 3 b/w illustrations, 99 illustrations in colour
Topics: Earth Sciences, general, Machine Learning, Artificial Intelligence, Applications of Mathematics, Computer Applications