| Titre : |
Machine learning for earth sciences:Using python to solve geological problems |
| Type de document : |
texte imprimé |
| Auteurs : |
Maurizio petrelli, Auteur |
| Editeur : |
Springer international pulishing AG |
| Année de publication : |
2023 |
| Collection : |
Text Book |
| Importance : |
209p. |
| Présentation : |
ill. |
| Format : |
23cm x 15cm |
| ISBN/ISSN/EAN : |
978-3-031-35116-7 |
| Langues : |
Anglais (eng) Langues originales : Anglais (eng) |
| Index. décimale : |
551 Géologie, météorologie, hydrologie générale |
| Résumé : |
.
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. |
| Note de contenu : |
.
Table of Contents:
Chapter 1: Introduction to Machine Learning
Chapter 2: Setting Up Your Python Environments for Machine Learning
Chapter 3: Machine Learning Workflow
Chapter 4: Unsupervised Machine Learning Methods]
Chapter 5: Clustering and Dimensionality Reduction in Petrology
Chapter 6: Clustering of Multi-Spectral Data
Chapter 7: Supervised Machine Learning Methods
Chapter 8: Classification of Well Log Data Facies by Machine Learning
Chapter 9: Machine Learning Regression in Petrology
Chapter 10: Parallel Computing and Scaling with Dask
Chapter 11: Scale Your Models in the Cloud
Chapter 12: Introduction to Deep Learning |
| En ligne : |
https://m.media-amazon.com/images/I/41UsHTtY0IL._SY445_SX342_FMwebp_.jpg |
Machine learning for earth sciences:Using python to solve geological problems [texte imprimé] / Maurizio petrelli, Auteur . - [S.l.] : Springer international pulishing AG, 2023 . - 209p. : ill. ; 23cm x 15cm. - ( Text Book) . ISBN : 978-3-031-35116-7 Langues : Anglais ( eng) Langues originales : Anglais ( eng)
| Index. décimale : |
551 Géologie, météorologie, hydrologie générale |
| Résumé : |
.
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. |
| Note de contenu : |
.
Table of Contents:
Chapter 1: Introduction to Machine Learning
Chapter 2: Setting Up Your Python Environments for Machine Learning
Chapter 3: Machine Learning Workflow
Chapter 4: Unsupervised Machine Learning Methods]
Chapter 5: Clustering and Dimensionality Reduction in Petrology
Chapter 6: Clustering of Multi-Spectral Data
Chapter 7: Supervised Machine Learning Methods
Chapter 8: Classification of Well Log Data Facies by Machine Learning
Chapter 9: Machine Learning Regression in Petrology
Chapter 10: Parallel Computing and Scaling with Dask
Chapter 11: Scale Your Models in the Cloud
Chapter 12: Introduction to Deep Learning |
| En ligne : |
https://m.media-amazon.com/images/I/41UsHTtY0IL._SY445_SX342_FMwebp_.jpg |
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