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SpringerBriefs in Petroleum Geoscience & Engineering

A Primer on Machine Learning in Subsurface Geosciences

Authors: Bhattacharya, Shuvajit

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  • 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
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eBook $54.99
price for USA in USD
  • ISBN 978-3-030-71768-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for USA in USD
  • ISBN 978-3-030-71767-4
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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. 

About the authors

Dr. Shuvajit Bhattacharya is a researcher at the Bureau of Economic Geology, the University of Texas at Austin. He is an applied geophysicist/petrophysicist specializing in seismic interpretation, petrophysical modeling, machine learning, and integrated subsurface characterization. He uses advanced computational technologies to solve complex problems in geosciences, which have societal and economic impacts. Dr. Bhattacharya has completed several projects in diverse geologic settings in the US, Norway, Australia, South Africa, and India. He worked in both academia and industry. He has published and presented more than 50 technical articles in peer-reviewed journals and conferences. His current research focuses on the pressing issues and frontier technologies in energy exploration, development, and subsurface fluid storage (carbon, hydrogen, and wastewater). He completed his Ph.D. at West Virginia University and an M.Sc. at the Indian Institute of Technology Bombay. 

Table of contents (6 chapters)

Table of contents (6 chapters)

Buy this book

eBook $54.99
price for USA in USD
  • ISBN 978-3-030-71768-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for USA in USD
  • ISBN 978-3-030-71767-4
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Bibliographic Information

Bibliographic Information
Book Title
A Primer on Machine Learning in Subsurface Geosciences
Authors
Series Title
SpringerBriefs in Petroleum Geoscience & Engineering
Copyright
2021
Publisher
Springer International Publishing
Copyright Holder
The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-71768-1
DOI
10.1007/978-3-030-71768-1
Softcover ISBN
978-3-030-71767-4
Series ISSN
2509-3126
Edition Number
1
Number of Pages
XVII, 172
Number of Illustrations
12 b/w illustrations, 118 illustrations in colour
Topics