Geochemical Mechanics And Deep Neural Network Modeling

Author : Mitsuhiro Toriumi
Publisher : Springer Nature
Page : 283 pages
File Size : 46,7 Mb
Release : 2022-08-19
Category : Science
ISBN : 9789811936593

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Geochemical Mechanics and Deep Neural Network Modeling by Mitsuhiro Toriumi Pdf

The recent understandings about global earth mechanics are widely based on huge amounts of monitoring data accumulated using global networks of precise seismic stations, satellite monitoring of gravity, very large baseline interferometry, and the Global Positioning System. New discoveries in materials sciences of rocks and minerals and of rock deformation with fluid water in the earth also provide essential information. This book presents recent work on natural geometry, spatial and temporal distribution patterns of various cracks sealed by minerals, and time scales of their crack sealing in the plate boundary. Furthermore, the book includes a challenging investigation of stochastic earthquake prediction testing by means of the updated deep machine learning of a convolutional neural network with multi-labeling of large earthquakes and of the generative autoencoder modeling of global correlated seismicity. Their manifestation in this book contributes to the development of human society resilient from natural hazards. Presented here are (1) mechanics of natural crack sealing and fluid flow in the plate boundary regions, (2) large-scale permeable convection of the plate boundary, (3) the rapid process of massive extrusion of plate boundary rocks, (4) synchronous satellite gravity and global correlated seismicity, (5) Gaussian network dynamics of global correlated seismicity, and (6) prediction testing of plate boundary earthquakes by machine learning and generative autoencoders.

Author : P. S. Neelakanta,Dolores DeGroff
Publisher : Unknown
Page : 256 pages
File Size : 47,7 Mb
Release : 2018
Category : Computers
ISBN : OCLC:1104034746

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Neural Network Modeling by P. S. Neelakanta,Dolores DeGroff Pdf

Author : Mikhail Kanevski,Vadim Timonin,Alexi Pozdnukhov
Publisher : CRC Press
Page : 384 pages
File Size : 53,7 Mb
Release : 2009-06-09
Category : Computers
ISBN : 9780849382376

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Machine Learning for Spatial Environmental Data by Mikhail Kanevski,Vadim Timonin,Alexi Pozdnukhov Pdf

This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and mapping of spatially distributed data. It presents basic geostatistical algorithms as well. The authors describe new trends in machine learning and their application to spatial data. The text also includes real case studies based on environmental and pollution data. It includes a CD-ROM with software that will allow both students and researchers to put the concepts to practice.

Author : Osvaldo Gervasi,Beniamino Murgante,Ana Maria A. C. Rocha,Chiara Garau,Francesco Scorza,Yeliz Karaca,Carmelo M. Torre
Publisher : Springer Nature
Page : 653 pages
File Size : 44,9 Mb
Release : 2023-06-28
Category : Computers
ISBN : 9783031371141

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Computational Science and Its Applications – ICCSA 2023 Workshops by Osvaldo Gervasi,Beniamino Murgante,Ana Maria A. C. Rocha,Chiara Garau,Francesco Scorza,Yeliz Karaca,Carmelo M. Torre Pdf

This nine-volume set LNCS 14104 – 14112 constitutes the refereed workshop proceedings of the 23rd International Conference on Computational Science and Its Applications, ICCSA 2023, held at Athens, Greece, during July 3–6, 2023. The 350 full papers and 29 short papers and 2 PHD showcase papers included in this volume were carefully reviewed and selected from a total of 876 submissions. These nine-volumes includes the proceedings of the following workshops: Advances in Artificial Intelligence Learning Technologies: Blended Learning, STEM, Computational Thinking and Coding (AAILT 2023); Advanced Processes of Mathematics and Computing Models in Complex Computational Systems (ACMC 2023); Artificial Intelligence supported Medical data examination (AIM 2023); Advanced and Innovative web Apps (AIWA 2023); Assessing Urban Sustainability (ASUS 2023); Advanced Data Science Techniques with applications in Industry and Environmental Sustainability (ATELIERS 2023); Advances in Web Based Learning (AWBL 2023); Blockchain and Distributed Ledgers: Technologies and Applications (BDLTA 2023); Bio and Neuro inspired Computing and Applications (BIONCA 2023); Choices and Actions for Human Scale Cities: Decision Support Systems (CAHSC-DSS 2023); and Computational and Applied Mathematics (CAM 2023).

Author : Srikanta Mishra
Publisher : CRC Press
Page : 388 pages
File Size : 46,6 Mb
Release : 2022-12-27
Category : Technology & Engineering
ISBN : 9781000823899

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Machine Learning Applications in Subsurface Energy Resource Management by Srikanta Mishra Pdf

The utilization of machine learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-energy. Motivated by these developments, Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for ML applications to manage subsurface energy resources (e.g., oil and gas, geologic carbon sequestration, and geothermal energy). Covers ML applications across multiple application domains (reservoir characterization, drilling, production, reservoir modeling, and predictive maintenance) Offers a variety of perspectives from authors representing operating companies, universities, and research organizations Provides an array of case studies illustrating the latest applications of several ML techniques Includes a literature review and future outlook for each application domain This book is targeted at practicing petroleum engineers or geoscientists interested in developing a broad understanding of ML applications across several subsurface domains. It is also aimed as a supplementary reading for graduate-level courses and will also appeal to professionals and researchers working with hydrogeology and nuclear waste disposal.

Author : Taesam Lee,Vijay P. Singh,Kyung Hwa Cho
Publisher : Springer Nature
Page : 215 pages
File Size : 53,9 Mb
Release : 2021-01-27
Category : Science
ISBN : 9783030647773

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Deep Learning for Hydrometeorology and Environmental Science by Taesam Lee,Vijay P. Singh,Kyung Hwa Cho Pdf

This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.

Author : Daniel Asante Otchere
Publisher : CRC Press
Page : 368 pages
File Size : 45,8 Mb
Release : 2024-02-06
Category : Science
ISBN : 9781003860228

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Data Science and Machine Learning Applications in Subsurface Engineering by Daniel Asante Otchere Pdf

This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments. This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.

Author : Alok Prasad Das,Eric D. van Hullebusch,Ata Akçil
Publisher : CRC Press
Page : 359 pages
File Size : 55,7 Mb
Release : 2024-06-25
Category : Technology & Engineering
ISBN : 9781040031407

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Sustainable Management of Mining Waste and Tailings by Alok Prasad Das,Eric D. van Hullebusch,Ata Akçil Pdf

Integrating waste management, environmental sustainability, and economic development is a prime milestone in the circular economy. Critical metals recovery from mining tailings and secondary resources has significant potential, with widespread applications in high-tech industries that are critical to modern society and sustainable development. This book discusses technological advances for managing industrial and mining waste through circular economy approaches and successful critical metal recovery from secondary resources. It highlights how reprocessing of mine waste and tailings results in development of critical raw materials that significantly reduce the mining burden and ensure the lucrative use of waste materials. Features: Describes advances in remediation and valorization technologies for mining wastes Details biotechnological methods, cutting edge research, and applications Covers use of waste mining resources for economic growth and novel opportunities Discusses IR4.0 and machine learning methods Includes reports and case studies on mining waste in value-added products and recovery of strategically important critical minerals This book will be of value to researchers and advanced students working in the mining, chemical and environmental engineering, and renewable energy sectors.

Author : Vladimir P. Kolotov,Natalia S. Bezaeva
Publisher : Springer Nature
Page : 660 pages
File Size : 49,8 Mb
Release : 2023-02-28
Category : Science
ISBN : 9783031098833

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Advances in Geochemistry, Analytical Chemistry, and Planetary Sciences by Vladimir P. Kolotov,Natalia S. Bezaeva Pdf

This book presents 41 selected articles written by leading researchers from the Vernadsky Institute of Geochemistry and Analytical Chemistry, part of the Russian Academy of Sciences. The articles are grouped by the following topics: (1) Geochemistry, (2) Meteoritics, Cosmochemistry, Lunar and Planetary Sciences, (3) Biogeochemistry and Ecology, and (4) Analytical Chemistry, Radiochemistry, and Radioecology. The articles present recent experimental data, theoretical investigations, critical reviews, the results of computer modeling in the above-mentioned fields. Intended to provide a scientific “snapshot” of the institute, the book also includes content on its history, main scientific achievements and current goals, together with detailed descriptions of its 25 laboratories and three museums so as to promote new international collaborations. Given its scope, the book will be of interest to all scientists and graduate students working in the areas of geochemistry, analytical chemistry and radiochemistry, earth and environmental sciences, biogeosciences, meteoritics and planetary science, and to those seeking new collaboration opportunities in these areas in Russia.

Author : Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein
Publisher : John Wiley & Sons
Page : 436 pages
File Size : 43,9 Mb
Release : 2021-08-18
Category : Technology & Engineering
ISBN : 9781119646167

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Deep Learning for the Earth Sciences by Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein Pdf

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Author : Saro Lee
Publisher : MDPI
Page : 229 pages
File Size : 47,5 Mb
Release : 2018-04-09
Category : Electronic book
ISBN : 9783038427421

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Application of Artificial Neural Networks in Geoinformatics by Saro Lee Pdf

This book is a printed edition of the Special Issue "Application of Artificial Neural Networks in Geoinformatics" that was published in Applied Sciences

Author : Martin Erdmann,Jonas Glombitza,Gregor Kasieczka,Uwe Klemradt
Publisher : World Scientific
Page : 340 pages
File Size : 43,9 Mb
Release : 2021-06-25
Category : Science
ISBN : 9789811237478

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Deep Learning For Physics Research by Martin Erdmann,Jonas Glombitza,Gregor Kasieczka,Uwe Klemradt Pdf

A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.

Author : M. Arif Wani,Bhiksha Raj,Feng Luo,Dejing Dou
Publisher : Springer Nature
Page : 328 pages
File Size : 48,8 Mb
Release : 2021-11-12
Category : Technology & Engineering
ISBN : 9789811633577

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Deep Learning Applications, Volume 3 by M. Arif Wani,Bhiksha Raj,Feng Luo,Dejing Dou Pdf

This book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.

Author : Zenon Waszczysznk
Publisher : Springer Science & Business Media
Page : 324 pages
File Size : 46,8 Mb
Release : 1999
Category : Computers
ISBN : 3211833226

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Neural Networks in the Analysis and Design of Structures by Zenon Waszczysznk Pdf

Neural Networks are a new, interdisciplinary tool for information processing. Neurocomputing being successfully introduced to structural problems which are difficult or even impossible to be analysed by standard computers (hard computing). The book is devoted to foundations and applications of NNs in the structural mechanics and design of structures.

Author : Hitoshi Iba,Nasimul Noman
Publisher : Springer Nature
Page : 437 pages
File Size : 47,7 Mb
Release : 2020-05-20
Category : Computers
ISBN : 9789811536854

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Deep Neural Evolution by Hitoshi Iba,Nasimul Noman Pdf

This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automatic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL. EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research —from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.