Primer on Machine Learning in Subsurface Geosciences by Shuvajit Bhattacharya (2021, Trade Paperback) online kaufen

Über dieses Produkt

Product Identifiers

PublisherSpringer International Publishing A&G

ISBN-103030717674

ISBN-139783030717674

eBay Product ID (ePID)19050406345

Product Key Features

Book TitlePrimer on Machine Learning in Subsurface Geosciences

Number of PagesXvii, 172 Pages

LanguageEnglish

Publication Year2021

TopicCivil / Soil & Rock, Intelligence (Ai) & Semantics, Power Resources / Fossil Fuels

IllustratorYes

GenreComputers, Technology & Engineering

AuthorShuvajit Bhattacharya

Book SeriesSpringerbriefs in Petroleum Geoscience and Engineering Ser.

FormatTrade Paperback

Dimensions

Item Weight16 Oz

Item Length9.3 in

Item Width6.1 in

Additional Product Features

TitleLeadingA

Number of Volumes1 vol.

Table Of ContentIntroduction.- Brief Review of Statistical Measures.- Basic Steps in Machine Learning and Deep Learning Models.- Brief Review of Popular Machine Learning and Deep Learning Algorithms.- Applications of ML/DL in Geophysics and Petrophysics Domain.- Applications of ML/DL in Geology Domain.- Multi-scale Data Integration and Analytics.- The Road Ahead.

SynopsisThis book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences.

LC Classification NumberTK1041-1078