A Primer on Machine Learning in Subsurface Geosciences

This book explores the success, promise, and perils of machine learning in geosciences, highlighting its disruption of traditional workflows and its potential benefits for practitioners, researchers, and academics. It emphasizes the need to embrace new computational tools and develop fit-for-purpose machine learning models to solve complex problems in subsurface geosciences.

Format: Paperback / softback
Length: 172 pages
Publication date: 04 May 2021
Publisher: Springer Nature Switzerland AG

This comprehensive book delves into the realm of machine learning in geosciences, offering a timely and insightful examination of its successes, potential, and challenges. It explores the fundamental principles of data science and machine learning, highlighting how these advancements have revolutionized traditional workflows employed in the industry and academia, encompassing geology, geophysics, petrophysics, geomechanics, and geochemistry. The book then showcases real-world applications, emphasizing that while top-level executives and field operators may initially face disruptions, the ultimate beneficiaries will be geoscientists and field operators alike. Authored by a practitioner with expertise in machine learning and statistics, the book underscores the importance of moving beyond the confines of STAT 101 courses and embracing innovative computational tools to tackle complex problems in geosciences. It provides valuable insights for practitioners, researchers, and academics, guiding them in identifying, developing, deploying, and recommending fit-for-purpose machine learning models to address real-world challenges in subsurface geosciences.

This comprehensive book delves into the realm of machine learning in geosciences, offering a timely and insightful examination of its successes, potential, and challenges. It explores the fundamental principles of data science and machine learning, highlighting how these advancements have revolutionized traditional workflows employed in the industry and academia, encompassing geology, geophysics, petrophysics, geomechanics, and geochemistry. The book then showcases real-world applications, emphasizing that while top-level executives and field operators may initially face disruptions, the ultimate beneficiaries will be geoscientists and field operators alike. Authored by a practitioner with expertise in machine learning and statistics, the book underscores the importance of moving beyond the confines of STAT 101 courses and embracing innovative computational tools to tackle complex problems in geosciences. It provides valuable insights for practitioners, researchers, and academics, guiding them in identifying, developing, deploying, and recommending fit-for-purpose machine learning models to address real-world challenges in subsurface geosciences.

Weight: 454g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030717674
Edition number: 1st ed. 2021