Chapter Machine Learning In Volcanology A Review
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Applications Of Machine Learning In Volcanology
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Author : Bellina Di Lieto
language : en
Publisher: Frontiers Media SA
Release Date : 2025-04-29
Applications Of Machine Learning In Volcanology written by Bellina Di Lieto and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-29 with Science categories.
The characterization of volcano state is not a simple task due the complexity of physics processes underway. Understanding their evolution prior to and during eruptions is a critical point for identifying transitions in volcanic state. Permanent monitoring networks are developed for such a purpose. With the increase of the number of monitoring sites, the amount of available continuous data coming from different sources (infrasonic, seismic, GPS, geochemical, etc.) has increased exponentially and extracting the huge amount of information this data brings, represents a non-trivial task for researchers, who are always more often looking at the potentiality of computer algorithms to find correlations among them. Recent developments in the field of Machine Learning (ML) have proven to be very useful and efficient for automatic discrimination, decision, prediction, clustering and information extraction in many fields, including volcanology. In recent times, Deep Learning has seen rapid growth in its popularity along with other supervised strategies, such as Support Vectors Machines and Recurrent neural networks (RNN), which have consistently been applied with success to broader and broader sets of applications and fields. However, supervised machine learning requires labels for training, and obtaining these labels for large volumes of seismic and volcanic data is a very demanding and challenging task. Therefore, semi-supervised and unsupervised methods, such as Self-organized Maps, have been applied with success, to extract relevant information from huge amounts of unlabelled data. In seismic and deformative data processing, these techniques are used for waveform inversion, automatic picking of first arrivals, and interpretation of peculiar characteristics of transients. ML is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations between volcanic signals and the chemico-physical composition of erupted materials. Other applications of ML in volcanology include the analysis and classification of geological, geochemical and petrological “static” data to infer for example, the possible source and mechanism of observed deposits, the analysis of satellite imagery to quickly classify vast regions difficult to investigate on the ground or, again, to detect changes that could indicate an unrest. The results obtained with the help of these algorithms would otherwise represent for researchers’ tasks hard to be solved with the usual standard methodologies.
Chapter Machine Learning In Volcanology A Review
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Author : Roberto Carniel
language : en
Publisher:
Release Date : 2020
Chapter Machine Learning In Volcanology A Review written by Roberto Carniel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application of machine learning techniques, that are able to process big data in an efficient way. Other applications of machine learning in volcanology include the analysis and classification of geological, geochemical and petrological “static” data to infer for example, the possible source and mechanism of observed deposits, the analysis of satellite imagery to quickly classify vast regions difficult to investigate on the ground or, again, to detect changes that could indicate an unrest. Moreover, the use of machine learning is gaining importance in other areas of volcanology, not only for monitoring purposes but for differentiating particular geochemical patterns, stratigraphic issues, differentiating morphological patterns of volcanic edifices, or to assess spatial distribution of volcanoes. Machine learning is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations of volcanic units, being particularly helpful in tephrochronology, etc. In this chapter we will review the relevant methods and results published in the last decades using machine learning in volcanology, both with respect to the choice of the optimal feature vectors and to their subsequent classification, taking into account both the unsupervised and the supervised approaches.
A Comprehensive Study Of Volcanic Phenomena
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Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2025-02-26
A Comprehensive Study Of Volcanic Phenomena written by and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-26 with Science categories.
This book is a new addition to research works exploring the diversity of volcanic geology. This book has collected works that approach volcanic phenomena via traditional methods and highlights the significance of volcanic geology in correct and realistic volcanic reconstruction through geological mapping and material science. This important and commonly less respected area of volcanology provides the fundamental basis for understanding volcanoes. The book has attracted works that show advanced technologies and their usage in volcano science. Research subjects such as hidden volcanoes deep beneath the sea surface are common targets of complex technology-aided mapping. Geophysical methods that use approaches to locate potential eruptible magma or magmatic fluids are among the most dynamically evolving methods. As one of the ultimate goals of volcanology is to provide geology-based models that identify volcanic hazards to offer science-based approaches for volcanic hazard mitigation, this book also contains some ideas to show eruption scenarios and historical record–based approaches to understand volcanoes and how to live with them. Overall, this book is a nice collection representing the broad and colorful nature of volcano science.
Intelligent Methods With Applications In Volcanology And Seismology
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Author : Alireza Hajian
language : en
Publisher: Springer Nature
Release Date : 2023-03-01
Intelligent Methods With Applications In Volcanology And Seismology written by Alireza Hajian and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-01 with Science categories.
This book presents intelligent methods like neural, neuro-fuzzy, machine learning, deep learning and metaheuristic methods and their applications in both volcanology and seismology. The complex system of volcanoes and also earthquakes is a big challenge to identify their behavior using available models, which motivates scientists to apply non-model based methods. As there are lots of seismology and volcanology data sets, i.e., the local and global networks, one solution is using intelligent methods in which data-based algorithms are used.
Muography
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Author : László Oláh
language : en
Publisher: John Wiley & Sons
Release Date : 2022-01-25
Muography written by László Oláh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-25 with Science categories.
A technique for visualizing Earth's subsurface at high resolution Hidden out of sight in Earth’s subsurface are a range of geophysical structures, processes, and material movements. Muography is a passive and non-destructive remote sensing technique that visualizes the internal structure of solid geological structures at high resolution, similar in process to X-ray radiography of human bodies. Muography: Exploring Earth's Subsurface with Elementary Particles explores the application of this imaging technique in the geosciences and how it can complement conventional geophysical observations. Volume highlights include: Principles of muography and pioneering works in the field Different approaches for muographic image processing Observing volcanic structures and activity with muography Using muography for geophysical exploration and mining engineering Potential environmental applications of muography Latest technological developments in muography The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
Reviews In Computational Chemistry
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Author : Abby L. Parrill
language : en
Publisher: John Wiley & Sons
Release Date : 2017-03-07
Reviews In Computational Chemistry written by Abby L. Parrill and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-07 with Science categories.
The Reviews in Computational Chemistry series brings together leading authorities in the field to teach the newcomer and update the expert on topics centered on molecular modeling. • Provides background and theory, strategies for using the methods correctly, pitfalls to avoid, applications, and references • Contains updated and comprehensive compendiums of molecular modeling software that list hundreds of programs, services, suppliers and other information that every chemist will find useful • Includes detailed indices on each volume help the reader to quickly discover particular topics • Uses a tutorial manner and non-mathematical style, allowing students and researchers to access computational methods outside their immediate area of expertise
Machine Learning For Earth Sciences
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Author : Maurizio Petrelli
language : en
Publisher: Springer Nature
Release Date : 2023-09-22
Machine Learning For Earth Sciences written by Maurizio Petrelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-22 with Science categories.
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.
Machine Learning Using R
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Author : Karthik Ramasubramanian
language : en
Publisher: Apress
Release Date : 2016-12-22
Machine Learning Using R written by Karthik Ramasubramanian and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-22 with Computers categories.
Examine the latest technological advancements in building a scalable machine learning model with Big Data using R. This book shows you how to work with a machine learning algorithm and use it to build a ML model from raw data. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. For every machine learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All the images are available in color and hi-res as part of the code download. This new paradigm of teaching machine learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in this book makes it easy for someone to connect the dots.. What You'll Learn Use the model building process flow Apply theoretical aspects of machine learning Review industry-based cae studies Understand ML algorithms using R Build machine learning models using Apache Hadoop and Spark Who This Book is For Data scientists, data science professionals and researchers in academia who want to understand the nuances of machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.
An Anthology Of Global Risk
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Author : SJ Beard
language : en
Publisher: Open Book Publishers
Release Date : 2024-09-03
An Anthology Of Global Risk written by SJ Beard and has been published by Open Book Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-03 with Social Science categories.
This anthology brings together a diversity of key texts in the emerging field of Existential Risk Studies. It serves to complement the previous volume The Era of Global Risk: An Introduction to Existential Risk Studies by providing open access to original research and insights in this rapidly evolving field. At its heart, this book highlights the ongoing development of new academic paradigms and theories of change that have emerged from a community of researchers in and around the Centre for the Study of Existential Risk. The chapters in this book challenge received notions of human extinction and civilization collapse and seek to chart new paths towards existential security and hope. The volume curates a series of research articles, including previously published and unpublished work, exploring the nature and ethics of catastrophic global risk, the tools and methodologies being developed to study it, the diverse drivers that are currently pushing it to unprecedented levels of danger, and the pathways and opportunities for reducing this. In each case, they go beyond simplistic and reductionist accounts of risk to understand how a diverse range of factors interact to shape both catastrophic threats and our vulnerability and exposure to them and reflect on different stakeholder communities, policy mechanisms, and theories of change that can help to mitigate and manage this risk. Bringing together experts from across diverse disciplines, the anthology provides an accessible survey of the current state of the art in this emerging field. The interdisciplinary and trans-disciplinary nature of the cutting-edge research presented here makes this volume a key resource for researchers and academics. However, the editors have also prepared introductions and research highlights that will make it accessible to an interested general audience as well. Whatever their level of experience, the volume aims to challenge readers to take on board the extent of the multiple dangers currently faced by humanity, and to think critically and proactively about reducing global risk.
Learning Tensorflow Js
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Author : Gant Laborde
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-05-10
Learning Tensorflow Js written by Gant Laborde and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-10 with Computers categories.
Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde (Google Developer Expert in machine learning and the web) provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers. You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-readydeep learning systems with TensorFlow.js. Explore tensors, the most fundamental structure of machine learning Convert data into tensors and back with a real-world example Combine AI with the web using TensorFlow.js Use resources to convert, train, and manage machine learning data Build and train your own training models from scratch