Titlebook: Machine Learning for Earth Sciences; Using Python to Solv Maurizio Petrelli Textbook 2023 The Editor(s) (if applicable) and The Author(s),

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Unsupervised Machine Learning MethodsThis chapter introduces unsupervised machine learning methods. It starts by describing the algorithms for dimensionality reduction, which include principal component analysis and manifold learning. It then describes clustering methods, such as hierarchical clustering, DBSCAN, mean shift, K-means, spectral clustering, and Gaussian-mixture models.

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Clustering and Dimensionality Reduction in PetrologyChapter . describes how to apply unsupervised machine learning methods in petrology. It focuses on analyzing the clinopyroxene erupted by Mt. Etna during the sequence of lava fountains that occurred between February and April of 2021. The application of clustering and dimensionality reduction techniques is described in detail.

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恫吓

Classification of Well Log Data Facies by Machine LearningThis chapter focuses on the classification by machine learning of facies in well-log data. It progressively develops a machine learning workflow that includes descriptive statistics, algorithm selection, model optimization, model training, and application to blind observations. Each step is discussed in detail.

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Machine Learning Regression in PetrologyThis chapter applies machine-learning regression to petrology. It explains how to calibrate machine-learning thermo-barometers based on orthopyroxene crystals in equilibrium with the melt in a volcanic plumbing system. It also describes the calibration of a thermo-barometer based on orthopyroxenes crystals.

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DNR215

Scale Your Models in the CloudThis chapter shows how to scale machine-learning models in the cloud. In the context of cloud computing, the term “scaling” refers to the ability to quickly and efficiently change the capability of a computational resource to handle a model that no longer fits the available resources.

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Introduction to Deep LearningThis chapter is about deep learning. It starts by introducing the basics of deep learning and then introduces PyTorch, a Python deep learning library. It also describes how to set up and train feedforward networks. Finally, it provides an example application dealing with deep learning potentials in the Earth Sciences.

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