Misra, Siddharth Primary Author He, Jiabo Primary Author Li, Hao Primary Author mixed material bibliography England Gulf Professional Publishing 2020 en English

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xxvii, 442 pages, illust 23 cm 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. Machine learning subsurface characterization 808.6 9780128177365 e-Library Universitas Pertamina Be Global Leader 808.6 MIS m 9620/PUP/2020 Perpustakaan Universitas Pertamina 808.6 MIS m c.1 9622/PUP/2020 Perpustakaan Universitas Pertamina 808.6 MIS m c.2 machine_learning_for_subsurface.jpg 5162 2023-03-03 16:11:00 2023-03-03 16:11:54 machine generated