Page 1
Page 1
MATLAB® Recipes for Earth Sciences
MATLAB® is used in a wide range of applications in geosciences, such as image processing in remote sensing, generation and processing of digital elevation models and the analysis of time series. This book introduces methods of data analysis in geosciences using MATLAB such as basic statistics for univariate, bivariate and multivariate datasets, jackknife and bootstrap resampling schemes, processing of digital elevation models, gridding and contouring, geostatistics and kriging, processing and georeferencing of satellite images, digitizing from the screen, linear and nonlinear time-series analysis and the application of linear time-invariant and adaptive filters. The revised and updated Second Edition includes new subchapters on windowed Blackman-Tukey, Lomb-Scargle and Wavelet powerspectral analysis, statistical analysis of point distributions and digital elevation models, and a full new chapter on the statistical analysis of directional data. The text includes a brief description of each method and numerous examples demonstrating how MATLAB can be used on data sets from earth sciences. All MATLAB recipes can be easily modified in order to analyse the reader's own data sets.
Innovations in 3D Geoinformation Science
Covers various aspects of spatial data modelling specifically regarding three-dimensional (3D) modelling and structuring. The realization of "true" 3D geoinformation spatial systems requires a high input, and the developmental process is taking place in various research centers and universities around the globe.
Geostatistics for environmental applications ; Proceedings of the Fifth European Conference on geostatistics for environmental applications
Once applied only to problems of mining-reserves assessment or petroleum-reservoir characterization, geostatistics is now being used in an increasingly large number of disciplines in environmental sciences. On the one hand, it enables the analysis and handling, in a rigorous probabilistic framework of the issues of spatial and temporal interpolation of continuous or categorical environmental variables. On the other hand, the methodology is also used to design and optimize sampling campaigns. "Geostatistics for Environmental Applications" contains forty selected contributions covering the latest progress in a broad spectrum of fields including air quality, climatology, ecology, groundwater hydrology, surface hydrology, oceanography, soil contamination, epidemiology and health, natural hazards, and remote sensing.
Geostatistics Banff 2004
The five major sections are: theory, mining, petroleum, environmental and other applications. The first section showcases new and innovative ideas in the theoretical development of geostatistics as a whole; these ideas will have large impact on (1) the directions of future geostatistical research, and (2) the conventional approaches to heterogeneity modelling in a wide range of natural resource industries. The next four sections are focused on applications and innovations relating to the use of geostatistics in specific industries. Historically, mining, petroleum and environmental industries have embraced the use of geostatistics for uncertainty characterization, so these three industries are identified as major application areas. The last section is open for innovative geostatistical application to address the issues and impact of uncertainty in other industries.
geoENV VI - Geostatistics for Environmental Applications : Proceedings of the Sixth European Conference on Geostatistics for Environmental Applications
This volume contains how geostatistics is applied within the environmental sciences. A few selected theoretical contributions are also included.
Design and Analysis of Simulation Experiments
This is an advanced expository book on statistical methods for the Design and Analysis of Simulation Experiments (DASE). Though the book focuses on DASE for discrete-event simulation (such as queuing and inventory simulations), it also discusses DASE for deterministic simulation (such as engineering and physics simulations). The text presents both classic and modern statistical designs. Classic designs (e.g., fractional factorials) assume only a few factors with a few values per factor. The resulting input/output data of the simulation experiment are analyzed through low-order polynomials, which are linear regression (meta)models. Modern designs allow many more factors, possible with many values per factor. These designs include group screening (e.g., Sequential Bifurcation, SB) and space filling designs (e.g., Latin Hypercube Sampling, LHS). The data resulting from these modern designs may be analyzed through low-order polynomials for group screening and various metamodel types (e.g., Kriging) for LHS.
