Advances in subsurface data analytics: traditional and physics-based machine learning
by Bhattacharya, Shuvajit Di, Haibin
Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorith…
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- Full title :Advances in subsurface data analytics : traditional and physics-based machine learning / edited by Shuvajit Bhattacharya, Haibin Di.
Summary
Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume.
Read More ↓Edition Statements
- Publication Date:©2022
Publication Statements
- Publisher(s):Elsevier
- Place of Publication:Amsterdam
- Language:eng
- Publication Date:©2022
The Physical Object
- Description:xix, 355 : : illustrations, maps ; : 24 cm
Edition Identifiers
- ISBN:9780128222959, 0128222956
- oclc:on1295107017
- LC Classification:QE539.2.D36
- Dewey Decimal:006.31
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