Machine Learning for Subsurface Characterization

Language: English

Published by Elsevier Inc, 2019

0128177365 / 9780128177365

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nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9780128177365

Title
Machine Learning for Subsurface Characterization
Author
Jiabo He
Publisher
Elsevier Inc
Publication year
2019
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
0128177365
ISBN 13
9780128177365
Item weight
690 grams
Dimensions
230x157x30 mm

AHA-BUCH GmbH

Einbeck, Germany

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