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Geostatistics Toronto 2021 : Quantitative Geology and Geostatistics / edited by Sebastian Alejandro Avalos Sotomayor, Julian M. Ortiz, R. Mohan Srivastava.

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Format:
Book
Author/Creator:
Avalos Sotomayor, Sebastian Alejandro.
Contributor:
Ortiz, Julian M.
Srivastava, R. Mohan.
Series:
Springer Proceedings in Earth and Environmental Sciences, 2524-3438
Language:
English
Subjects (All):
Geotechnical engineering.
Statistics.
Geophysics.
Geotechnical Engineering and Applied Earth Sciences.
Applied Statistics.
Local Subjects:
Geotechnical Engineering and Applied Earth Sciences.
Applied Statistics.
Geophysics.
Physical Description:
1 online resource (282 pages)
Edition:
1st ed. 2023.
Place of Publication:
Cham : Springer International Publishing : Imprint: Springer, 2023.
Summary:
This open access book provides state-of-the-art theory and application in geostatistics. Geostatistics Toronto 2021 includes 28 short abstracts, 18 extended abstracts, and 7 full articles in the fields of geostatistical theory, multi-point statistics, earth sciences, mining, optimal drilling, domains, seismic, classification uncertainty risk, and artificial intelligence and machine learning. All contributions were presented at the 11th International Geostatistics Congress held in virtually at Toronto, Canada, from July 12-16, 2021. This book is valuable to researchers, scientists, and practitioners in geology, mining, petroleum, geometallurgy, mathematics, and statistics.
Contents:
A Geostatistical Heterogeneity Metric For Spatial Feature Engineering
Iterative Gaussianisation For Multivariate Transformation
Comparing And Detecting Stationarity And Dataset Shift
Simulation Of Stationary Gaussian Random Fields With A Gneiting Spatio-Temporal Covariance
Spectral Simulation Of Gaussian Vector Random Fields On The Sphere
Geometric And Geostatistical Modeling Of Point Bars
Application Of Reinforcement Learning For Well Location Optimization
Compression-Based Modelling Honouring Facies Connectivity In Diverse Geological Systems
Spatial Uncertainty In Pore Pressure Models At The Brazilian Continental Margin
The Suitability Of Different Training Images For Producing Low Connectivity, High Net:Gross Pixel-Based Mps Models
Probabilistic Integration Of Geomechanical And Geostatistical Inferences For Mapping Natural Fracture Networks.
ISBN:
3-031-19845-X
OCLC:
1371295030

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