LEADER 04086nam 22006855 450 001 9910746284003321 005 20251008152004.0 010 $a3-031-35114-2 024 7 $a10.1007/978-3-031-35114-3 035 $a(MiAaPQ)EBC30751909 035 $a(Au-PeEL)EBL30751909 035 $a(DE-He213)978-3-031-35114-3 035 $a(PPN)272737526 035 $a(CKB)28284169000041 035 $a(EXLCZ)9928284169000041 100 $a20230922d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Earth Sciences $eUsing Python to Solve Geological Problems /$fby Maurizio Petrelli 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (xvi, 209 pages) $cillustrations 225 1 $aSpringer Textbooks in Earth Sciences, Geography and Environment,$x2510-1315 311 08$aPrint version: Petrelli, Maurizio Machine Learning for Earth Sciences Cham : Springer International Publishing AG,c2023 9783031351136 320 $aIncludes bibliographical references. 327 $aPart 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. 330 $aThis textbook introduces the reader to Machine Learning (ML) applications in Earth Sciences. In detail, it starts by describing the basics of machine learning and its potentials in Earth Sciences to solve geological problems. It describes the main Python tools devoted to ML, the typical workflow of ML applications in Earth Sciences, and proceeds with reporting how ML algorithms work. The book provides many examples of ML application to Earth Sciences problems in many fields, such as the clustering and dimensionality reduction in petro-volcanological studies, the clustering of multi-spectral data, well-log data facies classification, and machine learning regression in petrology. Also, the book introduces the basics of parallel computing and how to scale ML models in the cloud. The book is devoted to Earth Scientists, at any level, from students to academics and professionals. 410 0$aSpringer Textbooks in Earth Sciences, Geography and Environment,$x2510-1315 606 $aEarth sciences 606 $aMachine learning 606 $aArtificial intelligence 606 $aMathematics 606 $aApplication software 606 $aEarth Sciences 606 $aMachine Learning 606 $aArtificial Intelligence 606 $aApplications of Mathematics 606 $aComputer and Information Systems Applications 615 0$aEarth sciences. 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 0$aMathematics. 615 0$aApplication software. 615 14$aEarth Sciences. 615 24$aMachine Learning. 615 24$aArtificial Intelligence. 615 24$aApplications of Mathematics. 615 24$aComputer and Information Systems Applications. 676 $a550.028557 700 $aPetrelli$b Maurizio$01024610 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910746284003321 996 $aMachine Learning for Earth Sciences$93568953 997 $aUNINA