9780128177365-0128177365-Machine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization

ISBN-13: 9780128177365
ISBN-10: 0128177365
Edition: 1
Author: Misra, Siddharth, Li, Hao, He, Jiabo
Publication date: 2019
Publisher: Gulf Professional Publishing
Format: Paperback 440 pages
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Book details

ISBN-13: 9780128177365
ISBN-10: 0128177365
Edition: 1
Author: Misra, Siddharth, Li, Hao, He, Jiabo
Publication date: 2019
Publisher: Gulf Professional Publishing
Format: Paperback 440 pages

Summary

Acknowledged authors Misra, Siddharth, Li, Hao, He, Jiabo wrote Machine Learning for Subsurface Characterization comprising 440 pages back in 2019. Textbook and eTextbook are published under ISBN 0128177365 and 9780128177365. Since then Machine Learning for Subsurface Characterization textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface.

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