Search

This IMA Volume in Mathematics and its Applications TIME SERIES ANALYSIS AND APPLICATIONS TO GEOPHYSICAL SYSTEMS contains papers presented at a very successful workshop on the same title. The event which was held on November 12-15, 2001 was an integral part of the IMA 2001-2002 annual program on'Mathematics in the Geosciences.'We would like to thank David R. Brillinger (Department of Statistics, Uni­ versity of California, Berkeley), Enders Anthony Robinson (Department of Earth and Environmental Engineering, Columbia University), and Fred­ eric Paik Schoenberg (Department of Statistics, University of California, Los Angeles) for their superb role as workshop organizers and editors of the proceedings. We are also grateful to Robert H. Shumway (Department of Statistics, University of California, Davis) for his help in organizing the four-day event. We take this opportunity to thank the National Science Foundation for its support of the IMA. Series Editors Douglas N. Arnold, Director of the IMA Fadil Santosa, Deputy Director of the IMA v PREFACE This volume contains a collection of papers that were presented dur­ ing the Workshop on Time Series Analysis and Applications to Geophysical Systems at the Institute for Mathematics and its Applications (IMA) at the University of Minnesota from November 12-15, 2001. This was part of the IMA Thematic Year on Mathematics in the Geosciences, and was the last in a series of four Workshops during the Fall Quarter dedicated to Dynamical Systems and Ergodic Theory.

This book constitutes the refereed proceedings of the 4th International Conference on GeoSpatial Semantics, GeoS 2011, held in Brest, France, in May 2011. The 13 papers presented together with 1 invited talk were carefully reviewed and selected from 23 submissions. The papers focus on formal and semantic approaches, time and activity-based patterns, ontologies, as well as quality, conflicts and semantic integration. They are organized in topical sections on ontologies and gazetteers, activity-based and temporal issues, models, quality and semantic similarities, and retrieval and discovery methods.

The GIScience conference series was founded in 2000 with the goal of providing a forum for researchers interested in advancing the fundamental aspects of the prod- tion, dissemination, and use of geographic information. The conference is held bi- nually and attracts people from academia, industry, and government across a host of disciplines including cognitive science, computer science, engineering, geography, information science, mathematics, philosophy, psychology, social science, and stat- tics. Following a very successful conference in Münster, Germany in 2006, this year's conference was held in Park City, Utah, USA, the prior site of the 2002 Winter Ol- pics and home to the annual Sundance Film Festival. There are two forms of submission to the conference: full papers of 6000 words or less and extended abstracts of 500-1000 words for either a presentation or poster. This format was originally designed to capture the cultural difference between researchers who prefer to publish a peer-reviewed conference paper and those who would rather submit an abstract covering work in progress. This year 77 full papers were submitted and reviewed by 3 Program Committee members, of which 24 were selected for pr- entation and inclusion in this volume. Of the 115 extended abstracts that were subm- ted and reviewed by 2 Program Committee members, 47 were accepted for an oral presentation and 25 were accepted for presentation as a poster. The abstracts were published in a second booklet and are available on the GIScience website (http://www. giscience. org).

The return of the congress to North America after 20 years of absence could not have been in a more ideal location. The beauty of Banff and the many offerings of the Rocky Mountains was the perfect background for a week of interesting and innovative discussions on the past, present and future of geostatistics. The congress was well attended with approximately 200 delegates from 19 countries across six continents. There was a broad spectrum of students and seasoned geostatisticians who shared their knowledge in many areas of study including mining, petroleum, and environmental applications. You will find 119 papers in this two volume set. All papers were presented at the congress and have been peer-reviewed. They are grouped by the different sessions that were held in Banff and are in the order of presentation. These papers provide a permanent record of different theoretical perspectives from the last four years. Not all of these ideas will stand the test of time and practice; however, their originality will endure. The practical applications in these proceedings provide nuggets of wisdom to those struggling to apply geostatistics in the best possible way. Students and practitioners will be digging through these papers for many years to come. Oy Leuangthong Clayton V. Deutsch ACKNOWLEDGMENTS We would like to thank the industry sponsors who contributed generously to the overall success and quality of the congress: De Beers Canada Earth Decision Sciences Maptek Chile Ltda. Mira Geoscience Nexen Inc. Petro-Canada Placer Dome Inc.

For researchers and professionals in the field of databases, this is a must-read text that gives them right up-to-the-minute information on their field. It represents the refereed proceedings of the 10th International Symposium on Spatial and Temporal Databases, held in Boston, USA in July 2007. The 26 revised full papers were thoroughly reviewed and selected from a total of 76 submissions. The papers are classified in numerous categories, each corresponding to a conference session. These include continuous monitoring; indexing and query processing; and mining.

Within the realm of quantitative geography, systems modelling is specifically concerned with understanding those relationships that influence the attributes of phenomena located in space and time. The intention is to replicate the main processes influencing a system's behaviour and, thereby, assist its manage­ ment through a capability to estimate future change. Over the last few decades, one of the major institutional initiatives for promoting such research has been provided by specialised Study Groups and Commissions established by the International Geographical Union (IGU). These scholarly networks have aimed to co-ordinate international research agendas for geographical systems model­ ling and their activities have been recorded in both edited volumes (Fischer and Getis, 1997) and special issues of learned journals (Wilkinson and Boots, 2000; Leung and Okabe, 2001). Presently, this facilitative task is the charge of the Commission on Modelling Geographical Systems (CMGS) appointedat the IGU Hague Congress in 1996 and chaired by Barry Boots (1996-2000) and Richard Thomas (2000-present). Set against this background, this book provides a perspective on the work of the CMGS from 1996 until the IGU Seoul Congress in August 2000 through a collection of papers first presented to our sessions at this event. Moreover, a number of Japanese delegates were attracted to this Asian venue and their con­ tributions provide many new ideas concerning the implementation of systems analysis.

Self-organized criticality (SOC) has become a magic word in various scientific disciplines; it provides a framework for understanding complexity and scale invariance in systems showing irregular fluctuations. In the first 10 years after Per Bak and his co-workers presented their seminal idea, more than 2000 papers on this topic appeared. Seismology has been a field in earth sciences where the SOC concept has already deepened the understanding, but there seem to be much more examples in earth sciences where applying the SOC concept may be fruitful. After introducing the reader into the basics of fractals, chaos and SOC, the book presents established and new applications of SOC in earth sciences, namely earthquakes, forest fires, landslides and drainage networks.

Simulation is the fastest developing branch of geostatistics and simulating facies inside reservoirs and orebodies is the most exciting part of this. Several methods have been developed to do this (sequential indicator simulations, Boolean simulations, Markov chains and plurigaussian simulations). This book focuses on the last type of simulations. It presents the theory required to understand the method, along practical examples of applications in mining and the oil industry as well as tutorial exercises.Demonstration software to illustrate how these simulations work is available on http://pluridemo.geosciences.mines-paristech.fr Since the publication of the first edition, enormous numbers of papers have appeared in the literature on the subject. Plurigaussian simulations are now the preferred method for simulating facies in both mining & the oil industry. The new edition contains new case studies in both mining & petroleum, together with an extensively updated theory section.

This book constitutes the refereed proceedings of the Second International Conference on GeoSpatial Semantics, GeoS 2007, held in Mexico City, Mexico, in November 2007. The papers are organized in topical sections on models and languages for geo-ontologies, alignment and integration of geo-ontologies, ontology-based spatial information retrieval, formal representation for geospatial data, and integration of semantics into spatial query processing.

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.

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.

This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows.

This richly illustrated book describes statistical extreme value theory for the quantification of natural hazards, such as strong winds, floods and rainfall, and discusses an interdisciplinary approach to allow the theoretical methods to be applied. The approach consists of a number of steps: data selection and correction, non-stationary theory (to account for trends due to climate change), and selecting appropriate estimation techniques based on both decision-theoretic features (e.g., Bayesian theory), empirical robustness and a valid treatment of uncertainties. It also examines and critically reviews alternative approaches based on stochastic and dynamic numerical models, as well as recently emerging data analysis issues and presents large-scale, multidisciplinary, state-of-the-art case studies. Intended for all those with a basic knowledge of statistical methods interested in the quantification of natural hazards, the book is also a valuable resource for engineers conducting risk analyses in collaboration with scientists from other fields (such as hydrologists, meteorologists, climatologists).

Das Buch beschreibt die Einschätzung des Risikos und der Wahrscheinlichkeit des Eintretens von Schäden gemäß Richterskala. Es erläutert die Verbindung der Wahrscheinlichkeitstheorie extremwertiger Prozesse mit Beispielen aus den Wissenschaften der Erdbebenbeobachtungen.

Earth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. 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. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development.

This second edition is an intensively revised and updated version of the book MATLAB® and Design Recipes for Earth Sciences. It aims to introduce students to the typical course followed by a data analysis project in earth sciences. A project usually involves searching relevant literature, reviewing and ranking published books and journal articles, extracting relevant information from the literature in the form of text, data, or graphs, searching and processing the relevant original data using MATLAB, and compiling and presenting the results as posters, abstracts, and oral presentations using graphics design software. The text of this book includes numerous examples on the use of internet resources, on the visualization of data with MATLAB, and on preparing scientific presentations. As with the book MATLAB Recipes for Earth Sciences–4rd Edition (2015), which demonstrates the use of statistical and numerical methods on earth science data, this book uses state-of-the art software packages, including MATLAB and the Adobe Creative Suite, to process and present geoscientific information collected during the course of an earth science project. The book's supplementary electronic material (available online through the publisher's website) includes color versions of all figures, recipes with all the MATLAB commands featured in the book, the example data, exported MATLAB graphics, and screenshots of the most important steps involved in processing the graphics.

This book is published open access under a CC BY 4.0 license.Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites.This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. Inaddition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites.

This textbook covers the main applications of statistical methods in hydrology. It is written for upper undergraduate and graduate students but can be used as a helpful guide for hydrologists, geographers, meteorologists and engineers. The book is very useful for teaching, as it covers the main topics of the subject and contains many worked out examples and proposed exercises. Starting from simple notions of the essential graphical examination of hydrological data, the book gives a complete account of the role that probability considerations must play during modelling, diagnosis of model fit, prediction and evaluating the uncertainty in model predictions, including the essence of Bayesian application in hydrology and statistical methods under nonstationarity.The book also offers a comprehensive and useful discussion on subjective topics, such as the selection of probability distributions suitable for hydrological variables. On a practical level, it explains MS Excel charting and computing capabilities, demonstrates the use of Winbugs free software to solve Monte Carlo Markov Chain (MCMC) simulations, and gives examples of free R code to solve nonstationary models with nonlinear link functions with climate covariates.

This is an extensive revision of a book that I wrote over ten years ago. My purpose then has remained unchanged: to introduce the concepts and methods of spatial statistics to geologists and engineers working with oil and gas data. I believe I have accomplished more than that; just as I learned the basics of variography and kriging from books for mining engineers, this book could be used by scientists from many fields to learn the basics of the subject. I have tried to adopt an introductory and practical approach to the subject, knowing that books that detail the theory are available. What I say and write comes from my own experience. As a geologist working in the public sector, I have had the privilege of using geostatistics in funded research, in answering service requests from industry, and in short courses. I have taught geostatistics in the university classroom, and advised graduate students in theses and dissertations. I have attempted to anticipate the needs and questions of theenquiring scientist because I was there myself, and know the kind of questions and concerns I had at the time I was trying to learn the subject.

Simulation is the fastest developing branch in geostatistics, and simulating the facies inside reservoirs and orebodies is the most exciting part of this. Several methods have been developed to do this (sequential indicator simulations, Boolean methods, Markov chains and plurigaussian simulations). This book focuses on the last type of simulation. It presents the theory required to understand the method, along with the practical examples of applications in mining and and the oil industry as well as tutorial examples. An accompanying CD-ROM featuring demonstration software and color images complement the written text.

In seismology an earthquake source is described in terms of a fault with a particular rupture size. The faulting process of large earthquakes has been investigated in the last two decades through analyses of long-period seismo­ grams produced by advanced digital seismometry. By long-period far-field approximation, the earthquake source has been represented by physical parameters such as s~ismic moment, fault dimension and earthquake mag­ nitude. Meanwhile, destruction often results from strong ground motion due to large earthquakes at short distances. Since periods of strong ground motion are far shorter than those of seismic waves at teleseismic distances, the theory of long-period source process of earthquakes cannot be applied directly to strong ground motion at short distances. The excitation and propagation of high-frequency seismic waves are of special interest in recent earthquake seismology. In particular, the descrip­ tion and simulation of strong ground motion are very important not only for problems directly relevant to earthquake engineering, but also to the frac­ ture mechanics of earthquake faulting. Understanding of earthquake sources has been developed by investigating the complexity of faulting processes for the case of large earthquakes. Laboratory results on rock failures have also advanced the understanding of faulting mechanisms. Various attempts have been made to simulate, theoretically and empirically, the propagation of short-period seismic waves in the heterogeneous real earth.

Fractal geometry allows the description of natural patterns and the establishment and testing of models of pattern formation. In particular, it is a tool for geoscientists. The aim of this volume is to give an overview of the applications of fractal geometry and the theory of dynamic systems in the geosciences. The state of the art is presented and the reader obtains an impression of the variety of fields for which fractal geometry is a useful tool and of the different methods of fractal geometry which can be applied. In addition to specific information about new applications of fractal geometry in structural geology, physics of the solid earth, and mineralogy, proposals and ideas about how fractal geometry can be applied in the reader's field of studies will be put forward.

Geostatistics Rio 2000 includes fifteen contributions, five of which are on applications in petroleum science and ten are on mining geostatistics. These contributions were presented at the 31st International Geological Congress, held in Rio de Janeiro, Brazil, from 6-17 August, 2000. Stochastic simulation was the key theme of these case studies. A wide range of methods was used: truncated gaussian and plurigaussian, SIS and SGS, boolean methods and multi-point attractors. Audience: The volume will be of value to scientists, researchers, and professionals in geology, mining engineering, petroleum engineering, mathematics and statistics, as well as those working for mining and oil companies.

This book presents an introduction to geostatistics stressing the multivariate aspects for scientists, engineers or statisticians. Geostatistics offers a variety of models, methods and techniques for the analysis, estimation and display of multivariate data distributed in space or time. This book presents a brief review of statistical concepts, a detailed introduction to linear geostatistics, and an account of 3 basic methods of multivariate analysis. Applications from very different areas of science, as well as exercises with solutions, are provided to help convey the general ideas. In the second edition, the introductory chapter has been divided into two separate chapters for clarity. Furthermore, the chapters regarding normal kriging and cokriging have been restructured and the section on non-stationary geostatistics has been entirely rewritten.

Chance discovery means discovering chances - the breaking points in systems, the marketing windows in business, etc. It involves determining the significance of some piece of information about an event and then using this new knowledge in decision making. The techniques developed combine data mining methods for finding rare but important events with knowledge management, groupware, and social psychology. The reader will find many applications, such as finding information on the Internet, recognizing changes in customer behavior, detecting the first signs of an imminent earthquake, etc. This first book dedicated to chance discovery covers the state of the art in the theory and methods and examines typical scenarios, and it thus appeals to researchers working on new techniques and algorithms and also to professionals dealing with real-world applications.

The NATO Advanced Research Workshop “Mineral Resource Base of the Southern Caucasus and Systems for its Management in the XXI Century” was held in Tbilisi on April 3-6, 2001. The workshop was financed by the NATO Science Programme and financially supported by the Open Society – Georgia Foundation. By the end of the XX century the new states of the Southern Caucasus (Azerbaijan, Armenia and Georgia) found themselves in the lowest phase of a severe structural crisis. Belonging to the same geopolitical space and having the common economic history during the XIX and XX centuries, these countries will be able to overcome this crisis and to integrate into the world community only step-by-step, exploiting their resource bases and developing competitive branches of industries. Moreover, in our opinion, such conception is the only alternative to the spontaneous economical chaos. Solution of this problem seems to be impossible without close international collaboration. From this point of view, the idea of regional alliances where regional actors would be linked by a general, non-contradictory macroeconomic model seems to be the only possibility for sustainable development. Creation of The Southern Caucasus Alliance (SCA) where Georgia, Azerbaijan, Armenia and Turkey would find their place could be possible only in case if the economic reality and economic interests would weigh down nationalistic doctrines of political isolationism.

The book focuses on the development of advanced functions for field-based temporal geographical information systems (TGIS). These fields describe natural, epidemiological, economical, and social phenomena distributed across space and time. The book is organized around four main themes:'Concepts, mathematical tools, computer programs, and applications'.Chapters I and II review the conceptual framework of the modern TGIS and introduce the fundamental ideas of spatiotemporal modelling. Chapter III discusses issues of knowledge synthesis and integration. Chapter IV presents state-of-the-art mathematical tools of spatiotemporal mapping. Links between existing TGIS techniques and the modern Bayesian maximum entropy (BME) method offer significant improvements in the advanced TGIS functions. Comparisons are made between the proposed functions and various other techniques (e.g., Kriging, and Kalman-Bucy filters). Chapter V analyzes the interpretive features of the advanced TGIS functions, establishing correspondence between the natural system and the formal mathematics which describe it. In Chapters IV and V one can also find interesting extensions of TGIS functions (e.g., non-Bayesian connectives and Fisher information measures). Chapters VI and VII familiarize the reader with the TGIS toolbox and the associated library of comprehensive computer programs. Chapter VIII discusses important applications of TGIS in the context of scientific hypothesis testing, explanation, and decision making.

Geostatistics for Engineers and Earth Scientists

Linear Geostatistics covers basic geostatistics from the underlying statistical assumptions, the variogram calculation and modelling through to kriging. The underlying philosophy is to give the students an indepth understanding of the relevant theory and how to put it into practice. This means going into the theory in more detail than most books do, and also linking it with applications. It is assumed that readers, students and professionals alike, are familiar with basic probability and statistics, and matrix algebra needed for solving linear systems. Some reminders on these are given in an appendix at the end of the book. A set of exercises is integrated into the text.

1.1 Overview V ARIOWIN 2.2 is a collection of four Windows™ programs - Prevar2D, Vari02D with PCF, Model, and Grid Display - that are used for spatial data analysis and variogram modeling of irregularly spaced data in two dimensions. Prevar2D builds a pair comparison file (PCF), that is, a binary file containing pairs of data sorted in terms of increasing distance. Pair comparison files can be built from subsets in order to reduce memory requirements. Vari02D with PCF is used for spatial data analysis of 2D data. It uses an ASCII data file and a binary pair comparison file produced by Prevar2D. Features implemented in Vari02D with PCF include: • the possibility to characterize the spatial continuity of one variable or the joined spatial continuity of two variables, • variogram surfaces for identifying directions of anisotropies, • directional variograms calculated along any direction, • several measures of spatial continuity. Not only the variogram but also the standardized variogram, the covariance, the correlogram, and the madogram are used to measure spatial continuity. • h-scatterplots to assess the meaning of these measures, • the identification and localization of pairs of data adversely affecting the measure of spatial continuity. Once identified, these pairs can be masked from the calculation interactively. • variogram clouds for identifying pairs of data values having the most influence on the measure of spatial continuity. Those pairs can also be located on the sample map.

For decades experiments conducted on space stations like MIR and the ISS have been gathering data in many fields of research in the natural sciences, medicine and engineering. The EU-sponsored Ulisse Internet Portal provides metadata from space experiments of all kinds and links to the data. Complementary to the portal, this book will serve as handbook listing space experiments by type of infrastructure, area of research in the life and physical sciences, data type, what their mission was, what kind of data they have collected and how one can access this data through Ulisse for further research.The book will provide an overview of the wealth of space experiment data that can be used for research, and will inspire academics (e.g. those looking for topics for their PhD thesis) and research departments in companies for their continued development.

The availability of geographic and geospatial information and services, especially on the open Web has become abundant in the last several years with the proliferation of online maps, geo-coding services, geospatial Web services and geospatially enabled applications. The need for geospatial reasoning has significantly increased in many everyday applications including personal digital assistants, Web search applications, local aware mobile services, specialized systems for emergency response, medical triaging, intelligence analysis and more. Geospatial Semantics and the Semantic Web: Foundations, Algorithms, and Applications, an edited volume contributed by world class leaders in this field, provides recent research in the theme of geospatial semantics. This edited volume presents new information systems applications that have potential for high impact and commercialization. Also, special effort was made by the contributors to focus on geospatial ontology development, related standards, geospatial ontology alignment and integration, and algorithmic techniques for geospatial semantics. Case studies and examples will be provided throughout this book as well as possibilities for future research.

Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environmental and earth sciences, epidemiology, image analysis and more. This book covers the best-known spatial models for three types of spatial data: geostatistical data (stationarity, intrinsic models, variograms, spatial regression and space-time models), areal data (Gibbs-Markov fields and spatial auto-regression) and point pattern data (Poisson, Cox, Gibbs and Markov point processes). The level is relatively advanced, and the presentation concise but complete. The most important statistical methods and their asymptotic properties are described, including estimation in geostatistics, autocorrelation and second-order statistics, maximum likelihood methods, approximate inference using the pseudo-likelihood or Monte-Carlo simulations, statistics for point processes and Bayesian hierarchical models. A chapter is devoted to Markov Chain Monte Carlo simulation (Gibbs sampler, Metropolis-Hastings algorithms and exact simulation).A large number of real examples are studied with R, and each chapter ends with a set of theoretical and applied exercises. While a foundation in probability and mathematical statistics is assumed, three appendices introduce some necessary background. The book is accessible to senior undergraduate students with a solid math background and Ph.D. students in statistics. Furthermore, experienced statisticians and researchers in the above-mentioned fields will find the book valuable as a mathematically sound reference. This book is the English translation of Modélisation et Statistique Spatiales published by Springer in the series Mathématiques & Applications, a series established by Société de Mathématiques Appliquées et Industrielles (SMAI).

The use of geospatial technologies has become ubiquitous since the leading Internet vendors delivered a number of popular map websites. Today, businesses are either migrating location-specific capabilities into their information systems, or expanding existing Geospatial Information Systems (GIS) implementation into enterprise-wide solutions. As enterprise information systems evolve toward service-oriented architecture (SOA), geospatial technologies also evolve along the same lines. Geospatial Services and Applications for the Internet covers a wide spectrum of techniques, algorithms and modeling methodologies that address the challenges in service-oriented architectural design for GIS, and intelligent processing for spatial queries. This book presents in-depth studies on performance improvement, data integration, service personalization and interoperation between geospatial services. These studies provide the reader with a wide spectrum of examples and case studies on the development of GIS for hydrological applications, electrical power supply applications, land usage and urban planning, and NASA's Earth image datasets dissemination. Geospatial Services and Applications for the Internet is designed for a professional audience composed of practitioners and researchers in industry. This book is also suitable as a secondary text book or reference for advanced-level students in computer science and geosciences.

We start with a fun puzzle in mathematics and mathematical methods. How many corners does a four-dimensional cube have? Does such a thing exist, you ask? You may be a geoscientist or a philosopher. If your answer is: there are surely more than the eight corners there are for a three-dimensional cube, you are an engineer. If you know without hesitation that there are exactly sixteen corners and you can prove why, you are a mathematician. To explain the goal of this book, I refer to Hersh (1997): The United States suffers from “innumeracy” in its general population, “math avoidance” amonghigh-schoolstudents,and50percentfailureamongcollegecalculusstudents.Causes include starvation budgets in the school, mental attrition by television, parents who don't like math. There's another, unrecognized cause of failure: misconception of the nature of mathematics. I think the speci c reference to the United States may be omitted. It is really a worldwide problem. Moreover, there is one more consequence of “math avoidance” and “misconception”: good mathematical approaches are sometimes applied inc- rectly. Particularly, the methods of statistics are often misused for different goals. Applying mathematical methods is similar to using nuclear power: the nal results depend on the competence of the user. I try to convince my readers to apply the “energy” of mathematics with consideration.

This book reviews popular data-assimilation methods, such as weak and strong constraint variational methods, ensemble filters and smoothers. The author shows how different methods can be derived from a common theoretical basis, as well as how they differ or are related to each other, and which properties characterize them, using several examples. Readers will appreciate the included introductory material and detailed derivations in the text, and a supplemental web site.

Prediction of a random field based on observations of the random field at some set of locations arises in mining, hydrology, atmospheric sciences, and geography. Kriging, a prediction scheme defined as any prediction scheme that minimizes mean squared prediction error among some class of predictors under a particular model for the field, is commonly used in all these areas of prediction. This book summarizes past work and describes new approaches to thinking about kriging.