Machine Learning for Subsurface Characterization (Paperback)

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.

General

Imprint:	Gulf Professional Publishing
Country of origin:	United States
Release date:	October 2019
First published:	2020
Authors:	Siddharth Misra • Hao Li • Jiabo He
Dimensions:	229 x 152 x 22mm (L x W x T)
Format:	Paperback
Pages:	440
ISBN-13:	978-0-12-817736-5
Categories:	Books > Professional & Technical > Environmental engineering & technology > General Books > Professional & Technical > Energy technology & engineering > Fossil fuel technologies > Gas technology Books > Professional & Technical > Energy technology & engineering > Fossil fuel technologies > Petroleum technology Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	0-12-817736-5
Barcode:	9780128177365

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