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Table of Contents
Intro
Preface
Reference
Contents
1 Intelligent Methods and Motivations to Use in Volcanology and Seismology
Abstract
1 Introduction
1.1 Brief List of Intelligent Methods Applications in Volcanology and Seismology
1.1.1 In Volcanology
1.1.2 In Seismology
1.2 Metaheuristic Algorithms
1.3 Intelligent Methods
1.3.1 Motivations
1.3.2 Machine Learning
an Overview
1.4 The Role of Intelligent Methods Toward Big Volcano Science
References
2 Machine Learning: The Concepts
Abstract
1 Introduction
2 An Overview of the State Estimation Problem
3 Parametric and Non-parametric Estimation of Densities
3.1 A Parametric Approach
3.2 Parametric Density Estimation: A Numerical Example
3.3 A Non-parametric Approach
3.4 Estimation of the Prior Probabilities
4 Supervised Classification
4.1 The Bayes Minimum Risk Classification
4.2 An Example of Bayesian Minimum Risk Classifier
4.3 Naive Bayes Classifiers
4.4 Parzen Classifiers
4.5 K-Nearest Neighbor (KNN) Classification
4.6 Classification Based on Discriminant Functions
4.7 The Support Vector Classifier
4.8 Decision Trees
4.9 Combining Models: Boosting and Bagging
4.9.1 Boosting
4.9.2 Bagging
4.10 Error-Correcting Output Codes (ECOC)
4.11 Hidden Markov Models
5 Classification Metrics for Model Validation
6 Unsupervised Classification
6.1 Hierarchical Clustering
6.2 K-Means Clustering
6.3 Fuzzy c-means
6.4 Mixture of Gaussians
7 Methods to Reduce the Dimensionality of a Dataset
7.1 The Principal Component Analysis (PCA)
7.2 Self-organizing Maps
8 Software Tools for Machine Learning
8.1 The MATLAB Statistical and Machine Learning Toolbox
8.2 The Python Scikit-Learn Package
8.3 The R Language
8.4 The PRTools Library
References
3 Machine Learning Applications in Volcanology and Seismology
Abstract
1 Introduction
2 ML to Classify Seismic Data
3 Hidden Markov Model to Classify Volcanic Activity
4 Earthquake Detection and Phase Picking
5 Earthquake and Early Warning
6 Ground Motion Prediction
7 ML for Volcanic Activity Monitoring Based on Images
8 Multi-parametric Approaches to Classify the Volcanic Activity
9 Unsupervised Classification of Volcanic Activity
10 Clustering Multivariate Geophysical Data by Using SOM
References
4 Deep Learning: The Concepts
Abstract
1 Introduction
2 Deep Learning, an Overview
3 Deep Learning, Pros and Cons
4 Layers in a Deep Learning Models
5 Deep Learning Models
5.1 Supervised Deep Learning Methods
5.2 Deep Convolutional Neural Network
5.3 Image Classification by CNN: Fault Detection in Synthetic Seismic Data
5.4 Recurrent Neural Networks
5.5 Long Short Term Memory Network
5.6 Gated Recurrent Unit Network
Preface
Reference
Contents
1 Intelligent Methods and Motivations to Use in Volcanology and Seismology
Abstract
1 Introduction
1.1 Brief List of Intelligent Methods Applications in Volcanology and Seismology
1.1.1 In Volcanology
1.1.2 In Seismology
1.2 Metaheuristic Algorithms
1.3 Intelligent Methods
1.3.1 Motivations
1.3.2 Machine Learning
an Overview
1.4 The Role of Intelligent Methods Toward Big Volcano Science
References
2 Machine Learning: The Concepts
Abstract
1 Introduction
2 An Overview of the State Estimation Problem
3 Parametric and Non-parametric Estimation of Densities
3.1 A Parametric Approach
3.2 Parametric Density Estimation: A Numerical Example
3.3 A Non-parametric Approach
3.4 Estimation of the Prior Probabilities
4 Supervised Classification
4.1 The Bayes Minimum Risk Classification
4.2 An Example of Bayesian Minimum Risk Classifier
4.3 Naive Bayes Classifiers
4.4 Parzen Classifiers
4.5 K-Nearest Neighbor (KNN) Classification
4.6 Classification Based on Discriminant Functions
4.7 The Support Vector Classifier
4.8 Decision Trees
4.9 Combining Models: Boosting and Bagging
4.9.1 Boosting
4.9.2 Bagging
4.10 Error-Correcting Output Codes (ECOC)
4.11 Hidden Markov Models
5 Classification Metrics for Model Validation
6 Unsupervised Classification
6.1 Hierarchical Clustering
6.2 K-Means Clustering
6.3 Fuzzy c-means
6.4 Mixture of Gaussians
7 Methods to Reduce the Dimensionality of a Dataset
7.1 The Principal Component Analysis (PCA)
7.2 Self-organizing Maps
8 Software Tools for Machine Learning
8.1 The MATLAB Statistical and Machine Learning Toolbox
8.2 The Python Scikit-Learn Package
8.3 The R Language
8.4 The PRTools Library
References
3 Machine Learning Applications in Volcanology and Seismology
Abstract
1 Introduction
2 ML to Classify Seismic Data
3 Hidden Markov Model to Classify Volcanic Activity
4 Earthquake Detection and Phase Picking
5 Earthquake and Early Warning
6 Ground Motion Prediction
7 ML for Volcanic Activity Monitoring Based on Images
8 Multi-parametric Approaches to Classify the Volcanic Activity
9 Unsupervised Classification of Volcanic Activity
10 Clustering Multivariate Geophysical Data by Using SOM
References
4 Deep Learning: The Concepts
Abstract
1 Introduction
2 Deep Learning, an Overview
3 Deep Learning, Pros and Cons
4 Layers in a Deep Learning Models
5 Deep Learning Models
5.1 Supervised Deep Learning Methods
5.2 Deep Convolutional Neural Network
5.3 Image Classification by CNN: Fault Detection in Synthetic Seismic Data
5.4 Recurrent Neural Networks
5.5 Long Short Term Memory Network
5.6 Gated Recurrent Unit Network