Geostatistics Toronto 2021

Quantitative Geology and Geostatistics
1. Auflage 2023
ISBN: 978-3-031-19844-1
Verlag: Springer International Publishing

Buch, Englisch, 281 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 672 g

Reihe: Springer Proceedings in Earth and Environmental Sciences

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.

Zielgruppe

Research

Autoren/Hrsg.

Avalos Sotomayor, Sebastian Alejandro

Srivastava, R. Mohan

Ortiz, Julian M.

Fachgebiete

• Geowissenschaften Geologie Geotechnik
• Geowissenschaften Geologie Geophysik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

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.

Über Autor(innen)

Dr. Sebastian Alejandro Avalos Sotomayor is a Mining Engineer and Master in Mining from Universidad de Chile and Ph.D. from Queen’s University. Currently, he is a Postdoctoral Fellow at Queen’s University. With 9 years of experience, he focuses on the application of conventional and advanced predictive modeling techniques in geometallurgical modeling.

Dr. Julian Maximiliano Ortiz Cabrera is a Mining Engineer from Universidad de Chile and Ph.D. from University of Alberta. Currently, he is Head and Associate Professor at Queen’s University, where he teaches geostatistics and surface mining courses and conducts research related to ore body estimation and simulation and geometallurgical modeling. He has over 20 years of consulting experience in ore body modeling, predictive geometallurgy, and technology adoption.

R. Mohan Srivastava obtained his B.Sc. from Massachsetts Institute of Technology and M.Sc. Geostatistics from Stanford University. He has more than 40 years of experience as a geostatistician. He is author of the book “An Introduction to Applied Geostatistics” and of more than 50 technical articles on theory and practice. In mining, his consulting work spans project development from exploration through to short-term production planning.