Del 20 i serien Quantitative Geology and Geostatistics

International Geostatistics Congress 2024

Inbunden, Engelska, 2025

AvLeonardo Azevedo,João Narciso,Amílcar Soares,Maria João Pereira,José Antonio Vargas-Guzmán

4 539 kr

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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.

Produktinformation

Utgivningsdatum2025-10-26
Mått155 x 235 x 35 mm
Vikt1 124 g
FormatInbunden
SpråkEngelska
SerieQuantitative Geology and Geostatistics
Antal sidor558
FörlagSpringer International Publishing AG
ISBN9783031928697

Tillhör följande kategorier

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.