Machine Learning for Earth Sciences Using Python to Solve Geological Problems /
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...
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| Formato: | Electrónico eBook |
| Idioma: | inglés |
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Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| Edición: | 1st ed. 2023. |
| Series: | Springer Textbooks in Earth Sciences, Geography and Environment,
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| Acceso en liña: | https://doi.org/10.1007/978-3-031-35114-3 |
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Table of Contents:
- 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.