Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Delivery and Mixing in the Subsurface
This volume is meant to provide the practitioner with information on the natural mixing processes occurring in aquifers as well as to describe basic strategies that can be implemented to enhance mixing in particular cases. For example, when it comes to mixing miscible liquids, one can speed up mixing in the formation by manipulating the flow such as through the use of recirculation wells. Furthermore, much of the mixing can be achieved partially within recirculation wells themselves, where contaminated water is admixed with additives, volatile products may be removed through a vapor mass exchanger, etc. Thus, adding mixing wells can significantly increase the performance of the delivery and mixing system and speed up the process of remediation.
Introduction to Geostatistics
Engineers and applied geophysicists routinely encounter interpolation and estimation problems when analysing data from field observations. Introduction to Geostatistics presents practical techniques for the estimation of spatial functions from sparse data. The author's unique approach is a synthesis of classic and geostatistical methods with a focus on the most practical linear minimum-variance estimation methods, and includes suggestions on how to test and extend the applicability of such methods. The author includes many useful methods (often not covered in other geostatistics books) such as estimating variogram parameters, evaluating the need for a variable mean, parameter estimation and model testing in complex cases (e.g. anisotropy, variable mean, and multiple variables), and using information from deterministic mathematical models. Well illustrated with exercises and worked examples taken from hydrogeology, Introduction to Geostatistics assumes no background in statistics and is suitable for graduate-level courses in earth sciences, hydrology, and environmental engineering, and also for self-study.
Contaminated Ground Water and Sediment
This book is the documented result of an expert workshop, Modeling and Management of Emerging Environmental Issues, held at Penn State University. This event assembled four panels of modeling experts from the U.S. and Canada to discuss modeling technology development and application in order to promote sound and cost-effective environmental decision-making. This thorough analysis provides an overview on the state-of-the-art in current practice and identifies emerging research and development trends within modeling technology. Each of the discussions considers not only technical issues, but regulatory and cost factors as well.
Dispersion in Heterogeneous Geological Formations
In spite of many years of intensive study, our current abilities to quantify and predict contaminant migration in natural geological formations remain severely limited. The heterogeneity of these formations over a wide range of scales necessitates consideration of sophisticated transport theories. The evolution of such theories has escalated to the point that a review of the subject seems timely. While conceptual and mathematical developments were crucial to the introduction of these new approaches, there are now too many publications that contain theoretical abstractions without regard to real systems, or incremental improvements to existing theories which are known not to be applicable. This volume brings together articles representing a broad spectrum of state-of-the-art approaches for characterization and quantification of contaminant dispersion in heterogeneous porous media. Audience: The contributions are intended to be as accessible as possible to a wide readership of academics and professionals with diverse backgrounds such as earth sciences, subsurface hydrology, petroleum engineering, and soil physics.
Knowledge Guided Machine Learning
Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and d...
Machine Learning in Geomechanics 2
Machine learning has led to incredible achievements in many different fields of science and technology. These varied methods of machine learning all offer powerful new tools to scientists and engineers and open new paths in geomechanics. The two volumes of Machine Learning in Geomechanics aim to demystify machine learning. They present the main methods and provide examples of its applications in mechanics and geomechanics. Most of the chapters provide a pedagogical introduction to the most important methods of machine learning and uncover the fundamental notions underlying them. Building from the simplest to the most sophisticated methods of machine learning, the books give several hands-on examples of coding to assist readers in understanding both the methods and their potential and identifying possible pitfalls.
Estimation, Optimization, and Value of Information in Groundwater Remediation
Solving groundwater problems involves a system of methods in characterization and optimization. However, no matter how theoretically sound a method may be, when it is applied in the field, uncertainty is always an important factor that cannot be neglected. Indeed, a good theory or method has to be validated in field applications, and uncertainty propagates from one stage (e.g. characterization) of the application to the next (e.g. remediation optimization). Thus, it is essential for such methods not only to include uncertainty but also to quantify uncertainty in a practical sense. This dissertation covers three important topics in groundwater remediation: site characterization, remediation o...
Encyclopedia of Mathematical Geosciences
The Encyclopedia of Mathematical Geosciences is a complete and authoritative reference work. It provides concise explanation on each term that is related to Mathematical Geosciences. Over 300 international scientists, each expert in their specialties, have written around 350 separate articles on different topics of mathematical geosciences including contributions on Artificial Intelligence, Big Data, Compositional Data Analysis, Geomathematics, Geostatistics, Geographical Information Science, Mathematical Morphology, Mathematical Petrology, Multifractals, Multiple Point Statistics, Spatial Data Science, Spatial Statistics, and Stochastic Process Modeling. Each topic incorporates cross-referencing to related articles, and also has its own reference list to lead the reader to essential articles within the published literature. The entries are arranged alphabetically, for easy access, and the subject and author indices are comprehensive and extensive.
Machine Learning in Geomechanics 1
Machine learning has led to incredible achievements in many different fields of science and technology. These varied methods of machine learning all offer powerful new tools to scientists and engineers and open new paths in geomechanics. The two volumes of Machine Learning in Geomechanics aim to demystify machine learning. They present the main methods and provide examples of its applications in mechanics and geomechanics. Most of the chapters provide a pedagogical introduction to the most important methods of machine learning and uncover the fundamental notions underlying them. Building from the simplest to the most sophisticated methods of machine learning, the books give several hands-on examples of coding to assist readers in understanding both the methods and their potential and identifying possible pitfalls.
