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 ...
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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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PLEASE NOTE, WE DO NOT SHIP TO DENMARK. New Book. Shipped from US in 4 to 14 days. Established seller since 2000. Please note we cannot offer an expedited shipping service from the US.
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Seller's Description:
PLEASE NOTE, WE DO NOT SHIP TO DENMARK. New Book. Shipped from US in 4 to 14 days. Established seller since 2000. Please note we cannot offer an expedited shipping service from the US.
Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
Fair. Softcover. Excellent condition but does contain a minimal amount of highlighting (mostly in chapter 1-all text is legible). Intact, complete. Tulsa's best used bookstore. Located on South Mingo Road since 1991. No-hassle return policy if not completely satisfied.
