This multidisciplinary reference takes the reader through all four major phases of interdisciplinary inquiry: adequate conceptualization, rigorous formulation, substantive interpretation, and innovative implementation. The text introduces a novel synthetic paradigm of public health reasoning and epidemic modelling, and implements it with a study of the infamous 14th century AD Black Death disaster that killed at least one-fourth of the European population.

Geostatistics for Engineers and Earth Scientists

Geostatistics for Engineers and Earth Scientists

This multidisciplinary reference takes the reader through all four major phases of interdisciplinary inquiry: adequate conceptualization, rigorous formulation, substantive interpretation, and innovative implementation. The text introduces a novel synthetic paradigm of public health reasoning and epidemic modelling, and implements it with a study of the infamous 14th century AD Black Death disaster that killed at least one-fourth of the European population.

Geostatistics is a branch of mathematics that deals with the sampling, analysis, interpretation and display of phenomena that fluctuate in space. Although not all geostatistical methods are probabilistic in nature, the most important accomplishments in the field have been in estimation and forecasting, extending probabilistic methods of stochastic processes and time series analysis to the spatial domain. This book gives the only available comprehensive collection of definitions of geostatistical terms. It lists more than 600 entries selected from the book and journal literature through the end of 1989. Where appropriate, multiple meanings have been included for specific terms. Cross-references abound to help the reader when the definitions of a word leads to the search for other terms. Although all the definitions are in English, equivalent terms are provided in alphabetized lists in Chinese, French, German, Greek, Portuguese, Russian and Spanish. This volume will provide a useful reference for students, scientists, engineers, and others concerned with the meaning of terms found in the geostatistical literature and heard in professional practice. Definitions and entries have been prepared with multiple levels of rigor and complexity, in order to provide help to a readership that may range from inexperienced practitioners to advanced researchers.

THE CHALLENGE Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer. THE AIM OF GEOSTATISTICS The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps. GEOSTATISTICAL METHODS Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods. DIFFERENCES WITH TIME SERIES On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has sig