Authors:
- Explores the benefits, problems, and applications of machine learning in a geosciences context
- Presents numerous applications of machine learning models, allowing readers to transpose methods to their real-world scenarios
- Provides an accessible review of algorithms, statistical measures and deep learning models
Part of the book series: SpringerBriefs in Petroleum Geoscience & Engineering (BRIEFSPGE)
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Table of contents (6 chapters)
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Front Matter
About this book
This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences.
Authors and Affiliations
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Bureau of Economic Geology, The University of Texas at Austin, Austin, USA
Shuvajit Bhattacharya
About the author
Bibliographic Information
Book Title: A Primer on Machine Learning in Subsurface Geosciences
Authors: Shuvajit Bhattacharya
Series Title: SpringerBriefs in Petroleum Geoscience & Engineering
DOI: https://doi.org/10.1007/978-3-030-71768-1
Publisher: Springer Cham
eBook Packages: Energy, Energy (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Softcover ISBN: 978-3-030-71767-4Published: 04 May 2021
eBook ISBN: 978-3-030-71768-1Published: 03 May 2021
Series ISSN: 2509-3126
Series E-ISSN: 2509-3134
Edition Number: 1
Number of Pages: XVII, 172
Number of Illustrations: 12 b/w illustrations, 118 illustrations in colour
Topics: Fossil Fuels (incl. Carbon Capture), Geoengineering, Foundations, Hydraulics, Computational Intelligence, Quantitative Geology, Earth System Sciences