International Geostatistics Congress 2024
Series: Quantitative Geology and Geostatistics; 20;
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146 444 Ft (139 470 Ft + 5% VAT)
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146 444 Ft
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Product details:
- Publisher Springer Nature Switzerland
- Date of Publication 26 October 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783031928697
- Binding Hardback
- No. of pages558 pages
- Size 235x155 mm
- Language English
- Illustrations XVII, 558 p. 268 illus., 258 illus. in color. Illustrations, black & white 700
Categories
Long description:
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This book explains applications of geostatistics in mining and petroleum engineering and presents advances in theoretical geostatistics. The 12th International Geostatistical Congress follows a long tradition where theoreticians, engineers and practitioners working on geostatistics gather for four days. In 2024, the 12th International Geostatistical Congress was held in the middle of the North Atlantic in Azores.
This edition covers the fields of mineral and energy resources engineering, environment, hydrology, ecology, soil sciences, remote sensing, agriculture, fishery, health, spatial data science and the challenges related to sustainable development and carbon neutrality.
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Theory.- Optimising Kriging Parameters with Evolutionary Algorithms: Genetic Kriging Neighbourhood Analysis.- Iterative Gaussianisation with Householder Transformation.- Model Validation with an Ensemble of Exhaustive 2D Multivariate, Continuous, and Categorical Datasets.- High-Order Stochastic Simulation via Semidefinite Programming.- On the Influence of the Correlation Path in Incremental Multi-well Stratigraphic Correlation.- New Families of Covariance Functions.- Predefining Connectivity in Geostatistical Models Using the Plurigaussian Method.- A Bayesian Quantile Clustering Approach of Spatio-temporal Climate Time-series.- Environmental Engineering and Ecology.- “Real-time” Geostatistics for an Uncertainty Driven Environmental Soils Characterization–First Field Algorithms Reviewed and Discussed.- Geospatiotemporal Data Integration and Modeling for Environmental Prediction in Large Agricultural Systems.- Geostatistics for Spatial Extremes: Recent Approaches.
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