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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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