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Editore: Elsevier Science Publishing Co Inc, 2022
ISBN 10: 0128222956 ISBN 13: 9780128222959
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ISBN 10: 0128222956 ISBN 13: 9780128222959
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Aggiungi al carrelloCondizione: New. Inhaltsverzeichnis1. Data Analytics and Machine Learning: A review2. Self-Organizing Map: a case study in geosciences and/or reservoir engineering3. Artificial Neural Network: a case study in geosciences and/or reservoir engineer.
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Editore: Elsevier Science Publishing Co Inc, 2022
ISBN 10: 0128222956 ISBN 13: 9780128222959
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Editore: Elsevier Science Publishing Co Inc Mai 2022, 2022
ISBN 10: 0128222956 ISBN 13: 9780128222959
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EUR 147,93
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - 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.
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Editore: Elsevier Science Publishing Co Inc, 2022
ISBN 10: 0128222956 ISBN 13: 9780128222959
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Aggiungi al carrellopaperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Editore: Elsevier Science Publishing Co Inc, 2022
ISBN 10: 0128222956 ISBN 13: 9780128222959
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EUR 155,64
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Elsevier Science Publishing Co Inc, 2022
ISBN 10: 0128222956 ISBN 13: 9780128222959
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 229,22
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 118,06
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