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.