Maurizio Petrelli
Machine Learning for Earth Sciences: Using Python to Solve Geological Problems
Machine Learning for Earth Sciences: Using Python to Solve Geological Problems
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- More about Machine Learning for Earth Sciences: Using Python to Solve Geological Problems
This textbook provides an introduction to Machine Learning (ML) applications in Earth Sciences, covering basics, tools, workflows, and examples in various fields. It also introduces parallel computing and cloud scaling for ML models. It is designed for Earth Scientists at any level.
Format: Hardback
Length: 209 pages
Publication date: 23 September 2023
Publisher: Springer International Publishing AG
This comprehensive textbook delves into the realm of Machine Learning (ML) applications in Earth Sciences, providing a thorough introduction to the field. It begins by exploring the fundamental principles of machine learning and its immense potential in addressing geological challenges. The book then delves into the various Python tools commonly employed in ML, outlining the typical workflow involved in Earth Sciences applications. It proceeds to explain the workings of ML algorithms, offering valuable insights into their mechanisms.
Throughout the text, numerous examples of ML applications are presented, showcasing their significance in various Earth Science domains. These include topics such as petro-volcanological studies, multi-spectral data clustering, well-log data facies classification, and machine learning regression in petrology. Additionally, the book introduces the fundamentals of parallel computing and demonstrates how to scale ML models in the cloud, enabling efficient and scalable solutions for complex Earth Science problems.
Designed with Earth Scientists of all levels in mind, from students to academics and professionals, this textbook serves as a valuable resource for those seeking to leverage ML techniques in their research and practice. It equips readers with the knowledge and skills necessary to apply ML to real-world Earth Science problems, fostering a deeper understanding of the Earth's natural systems and contributing to the advancement of scientific knowledge.
Weight: 512g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031351136
Edition number: 1st ed. 2023
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