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.

Looking for a high-quality, original digital edition of Machine Learning for Subsurface Characterization ? This official electronic version is published by Gulf Professional Publishing and offers a seamless reading experience, perfect for professionals, students, and enthusiasts in Chemical Engineering.
Unlike EPUB files, this is the authentic digital edition with complete formatting, images, and original content as intended by the author ,,.
Enjoy the convenience of digital reading without compromising on quality. Order Machine Learning for Subsurface Characterization today and get instant access to this essential book!