A Primer on Machine Learning in Subsurface Geosciences, Bhattacharya Shuvajit
Автор: Crockett, Robin (university Of Northampton) Название: A Primer on Fourier Analysis for the Geosciences ISBN: 1316600246 ISBN-13(EAN): 9781316600245 Издательство: Cambridge Academ Рейтинг: Цена: 40130.00 T Наличие на складе: Поставка под заказ. Описание: An intuitive introduction to basic Fourier theory, with an emphasis on geoscience applications. Numerous worked examples from R are used to illustrate the theory, making this an ideal practical guide for graduate students and researchers who are using time-series analysis to quantify periodic features in geoscience data.
Автор: Abdel Razik Ahmed Zidan, Mohammed Ahmed Abdel Hady Название: Constructed Subsurface Wetlands: Case Study and Modeling ISBN: 1771884630 ISBN-13(EAN): 9781771884631 Издательство: Taylor&Francis Рейтинг: Цена: 75030.00 T Наличие на складе: Есть Описание:
With a sharp focus on environmental pollution and its impact on life and nature, scientists and engineers have studied the water treatment effect of natural wetlands for many years, resulting in the development of constructed wetlands (CWs) for treating wastewater. This informative new book provides current information and guidance on the construction, performance, operation, and maintenance of subsurface flow constructed wetlands of domestic and municipal wastewater.
The focus of the volume is to evaluate the performance of horizontal subsurface flow constructed wetlands in treating domestic wastewater to establish the limit that can be safely discharged to agricultural drains. Two-step procedures were used for the preparation of this book. Using modeling and statistical analyses of treated water samples showed a significant difference between different media for the treatment of most pollutants. The authors went on to design artificial neural network models (ANNs) using Matlab software to simulate some of the experimental data and to anticipate the parameters of output concentration.
The wetland systems have the ability to deal with various pollutants with different concentrations and to decrease the treated water to the standard limits. This volume presents the main role of emergent plants for treatment performance in the constructed wetlands and will be a very important resource for engineers in this field as well as for both undergraduate and graduate students.
Автор: Misra, Siddharth Название: Machine Learning for Subsurface Characterization ISBN: 0128177365 ISBN-13(EAN): 9780128177365 Издательство: Elsevier Science Рейтинг: Цена: 123520.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
To continue to meet demand while keeping costs down, petroleum and reservoir engineers know it is critical to utilize their asset's data through more complex modeling methods, and machine learning and data analytics is the known alternative approach to accurately represent the complexity of fluid-filled rocks. With a lack of training resources available, Machine Learning for Subsurface Characterization focuses on the development and application of neural networks, deep learning, unsupervised learning, reinforcement learning, and clustering methods for subsurface characterization under constraints. Such constraints are encountered during subsurface engineering operations due to financial, operational, regulatory, risk, technological, and environmental challenges.
This reference teaches how to do more with less. Used to develop tools and techniques of data-driven predictive modelling and machine learning for subsurface engineering and science, engineers will be introduced to methods of generating subsurface signals and analyzing the complex relationships within various subsurface signals using machine learning. Algorithmic procedures in MATLAB, R, PYTHON, and TENSORFLOW are displayed in text and through online instructional video to assist training and learning. Field cases are also presented to understand real-world applications, with a particular focus on examples involving shale reservoirs.
Explaining the concept of machine learning, advantages to the industry, and applications applied to complex subsurface rocks, Machine Learning for Subsurface Characterization delivers a missing piece to the reservoir engineer's toolbox needed to support today's complex operations.
Focus on applying predictive modelling and machine learning from real case studies and Q&A sessions at the end of each chapter
Learn how to develop codes such as MATLAB, PYTHON, R, and TENSORFLOW with step-by-step guides included
Visually learn code development with video demonstrations included
Автор: Naderpour, Novid Kordmahleh, F.h. Название: Petroleum geosciences ISBN: 8189741659 ISBN-13(EAN): 9788189741655 Издательство: Amazon Internet Рейтинг: Цена: 60560.00 T Наличие на складе: Невозможна поставка. Описание: Petroleum Geology is principally concerned with the evaluation of seven key elements in sedimentary basins namely, Source, Reservoir, Seal, Trap, Timing, Maturation and Migration. This book studies geology and geophysics in determining the origin, distribution and properties of petroleum and petroleum bearing rocks.
Автор: Moseley, Benjamin Название: Machine Learning And Artificial Intelligence In Geosciences,61 ISBN: 0128216697 ISBN-13(EAN): 9780128216699 Издательство: Elsevier Science Рейтинг: Цена: 185270.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including Marchenko imaging, Machine learning and inversion, A review of reduced-order modelling approaches based on machine-learning and graphs for simulation of flow and transport through fractured media, and more.
Автор: John F. Hubert Название: An Informal History of Geosciences at UMass Amherst ISBN: 1523473304 ISBN-13(EAN): 9781523473304 Издательство: Неизвестно Рейтинг: Цена: 17240.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Y. Zee Ma Название: Quantitative Geosciences ISBN: 3030178595 ISBN-13(EAN): 9783030178598 Издательство: Springer Рейтинг: Цена: 101630.00 T Наличие на складе: Поставка под заказ. Описание: As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making.
Автор: Institute For Career Research Название: Careers in Geology: Geosciences ISBN: 1717285023 ISBN-13(EAN): 9781717285027 Издательство: Неизвестно Рейтинг: Цена: 12070.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Tomasz Niedzielski (Editor) Название: Applications of unmanned aerial vehicles in geosciences. ISBN: 3030031705 ISBN-13(EAN): 9783030031701 Издательство: Springer Рейтинг: Цена: 79190.00 T Наличие на складе: Поставка под заказ. Описание: Geophysicists use UAVs to observe underground features, geologists and geomorphologists utilize drones to carry out detailed survey of Earth`s surface, hydrologists apply UAVs to observe water bodies and conduct hydrometric measurements, and meteorologists use drones to measure weather characteristics and air quality.
Автор: Richard Howarth Название: Dictionary of Mathematical Geosciences ISBN: 331986131X ISBN-13(EAN): 9783319861319 Издательство: Springer Рейтинг: Цена: 260870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This dictionary includes a number of mathematical, statistical and computing terms and their definitions to assist geoscientists and provide guidance on the methods and terminology encountered in the literature.
Название: Molecular modeling theory: aplications in the geosciences, vol. 42 ISBN: 0939950545 ISBN-13(EAN): 9780939950546 Издательство: Mineralogical Society of America Рейтинг: Цена: 47810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Volume 42 of Reviews in Mineralogy and Geochemistry covers the Applications in the Geosciences via Molecular Modeling Theory. We hope the content of this review volume will help the interested reader to quickly develop an appreciation for the fundamental theories behind the molecular modeling tools and to become aware of the limits in applying these state-of-the-art methods to solve geosciences problems.The review chapters in this volume were the basis for a short course on molecular modeling theory jointly sponsored by the Geochemical Society (GS) and the Mineralogical Society of America (MSA) May 18-20, 2001 in Roanoke, Virginia which was held prior to the 2001 Goldschmidt Conference in nearby Hot Springs, Virginia.
Автор: Akif A. Alizadeh and Ibrahim S. Guliyev Название: Geosciences of Azerbaijan: Volume II ISBN: 3319821172 ISBN-13(EAN): 9783319821177 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Поставка под заказ. Описание: This book provides a review of Azerbaijan's water reserves and main economic deposits (both hydrocarbon and hard) and describes the integrated application of geophysical methods (land, airborne, shipborne and satellite) for studying near-surface and environmental features and regional tectonic-geophysical zonation as well as the study of deep structures in the search for hydrocarbon and hard (polymetallic, copper, gold-bearing, iron-ore, magnetite, etc.) deposits. It particularly focuses on the geophysical examination of seismic activity in the region related to the interaction of the Afro-Arabian and Eurasian lithospheric plates. It is aimed at scientists, engineers and students interested in the commercial potential of Azerbaijan's deposits and the application of different geophysical methodologies (gravity, magnetic, seismic, thermal, electric, electromagnetic, etc.) for analyzing mud volcanism, identifying subsurface structures (including the analysis of hydrogeological problems, the examination of past climates and archaeological inspection) revealing the deep tectono-structural peculiarities of the region under study, mining and oil & gas geophysics, development of 3D physical-geological models and advanced seismological prognosis.
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