Misra S., Li H., He J. Machine Learning for Subsurface Characterization

Gulf Professional Publishing, 2020. — 420 p. — ISBN: 978-0-12-817736-5.This book 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.True PDF