Machine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization
by: Siddharth Misra,Hao Li,et al.
Paperback: 440 pages
Publisher: Gulf Professional Publishing; 1 edition (October 27,2019)
Language: English
ISBN-10: 0128177365
ISBN-13: 9780128177365
Book Description
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.