A Primer on Machine Learning in Subsurface Geosciences
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Beschreibung
This 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.
Explores the benefits, problems, and applications of machine learning in a geosciences context Presents numerous applications of machine learning models, allowing readers to transpose methods to their real-world scenarios Provides an accessible review of algorithms, statistical measures and deep learning models
Autor*in
Shuvajit Bhattacharya
Themen in »A Primer on Machine Learning in Subsurface Geosciences«
Machine learning Geosciences data classification and prediction Subsurface data analytics Deep learning in geosciences Data-driven modelling Geophysics quantitative geology