04086nam 22006855 450 991074628400332120251008152004.03-031-35114-210.1007/978-3-031-35114-3(MiAaPQ)EBC30751909(Au-PeEL)EBL30751909(DE-He213)978-3-031-35114-3(PPN)272737526(CKB)28284169000041(EXLCZ)992828416900004120230922d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMachine Learning for Earth Sciences Using Python to Solve Geological Problems /by Maurizio Petrelli1st ed. 2023.Cham :Springer International Publishing :Imprint: Springer,2023.1 online resource (xvi, 209 pages) illustrationsSpringer Textbooks in Earth Sciences, Geography and Environment,2510-1315Print version: Petrelli, Maurizio Machine Learning for Earth Sciences Cham : Springer International Publishing AG,c2023 9783031351136 Includes bibliographical references.Part 1: Basic Concepts of Machine Learning for Earth Scientists -- Chapter 1. Introduction to Machine Learning -- Chapter 2. Setting Up your Python Environments for Machine Learning -- Chapter 3. Machine Learning Workflow -- Part 2: Unsupervised Learning -- Chapter 4. Unsupervised Machine Learning Methods -- Chapter 5. Clustering and Dimensionality Reduction in Petrology -- Chapter 6. Clustering of Multi-Spectral Data -- Part 3: Supervised Learning -- Chapter 7. Supervised Machine Learning Methods -- Chapter 8. Classification of Well Log Data Facies by Machine Learning -- Chapter 9. Machine Learning Regression in Petrology -- Part 4: Scaling Machine Learning Models -- Chapter 10. Parallel Computing and Scaling with Dask -- Chapter 11. Scale Your Models in the Cloud -- Part 5: Next Step: Deep Learning -- Chapter 12. Introduction to Deep Learning.This textbook introduces the reader to Machine Learning (ML) applications in Earth Sciences. In detail, it starts by describing the basics of machine learning and its potentials in Earth Sciences to solve geological problems. It describes the main Python tools devoted to ML, the typical workflow of ML applications in Earth Sciences, and proceeds with reporting how ML algorithms work. The book provides many examples of ML application to Earth Sciences problems in many fields, such as the clustering and dimensionality reduction in petro-volcanological studies, the clustering of multi-spectral data, well-log data facies classification, and machine learning regression in petrology. Also, the book introduces the basics of parallel computing and how to scale ML models in the cloud. The book is devoted to Earth Scientists, at any level, from students to academics and professionals.Springer Textbooks in Earth Sciences, Geography and Environment,2510-1315Earth sciencesMachine learningArtificial intelligenceMathematicsApplication softwareEarth SciencesMachine LearningArtificial IntelligenceApplications of MathematicsComputer and Information Systems ApplicationsEarth sciences.Machine learning.Artificial intelligence.Mathematics.Application software.Earth Sciences.Machine Learning.Artificial Intelligence.Applications of Mathematics.Computer and Information Systems Applications.550.028557Petrelli Maurizio1024610MiAaPQMiAaPQMiAaPQBOOK9910746284003321Machine Learning for Earth Sciences3568953UNINA