Geostatistics Toronto 2021 : : Quantitative Geology and Geostatistics.

• Superior document: Springer Proceedings in Earth and Environmental Sciences Series
• Author: Avalos Sotomayor, Sebastian Alejandro.
• TeilnehmendeR: Ortiz, Julian M.; Srivastava, R. Mohan.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2023.; ©2023.
• Year of Publication: 2023
• Edition: 1st ed.
• Language: English
• Series: Springer Proceedings in Earth and Environmental Sciences Series
• Physical Description: 1 online resource (261 pages)

Table of Contents

• Intro
• Preface
• Acknowledgements
• Remembering Dr. Harry M. Parker (1946-2019)
• Contents
• Theory
• A Geostatistical Heterogeneity Metric for Spatial Feature Engineering
• 1 Introduction
• 2 Methodology
• 3 Results and Discussion
• 4 Case Study
• 5 Conclusion
• References
• Iterative Gaussianisation for Multivariate Transformation
• 1 Introduction
• 2 Iterative Multivariate Gaussianisation
• 3 Nickel Laterite Case Study
• 3.1 Overview
• 3.2 Workflow
• 3.3 Multivariate Transformation and Simulation
• 3.4 Benchmarking
• 3.5 Artifacts
• 4 Conclusions
• References
• Comparing and Detecting Stationarity and Dataset Shift
• 1 Introduction
• 2 Materials and Methods
• 3 Results and Discussion
• 4 Conclusions
• References
• Simulation of Stationary Gaussian Random Fields with a Gneiting Spatio-Temporal Covariance
• 1 Introduction
• 2 Theoretical Results
• 3 A Discrete-in-Time and Continuous-in-Space Substitution Algorithm
• 4 A Fully Continuous Spectral Algorithm
• 5 Concluding Remarks
• References
• Spectral Simulation of Gaussian Vector Random Fields on the Sphere
• 1 Introduction
• 2 Mathematical Background
• 3 Simulation Algorithms
• 3.1 Random Mixture of Spherical Harmonics (RMSH)
• 3.2 Random Mixture of Legendre Waves (RMLW)
• 3.3 Discussion
• 4 Examples
• 5 Conclusions
• References
• Petroleum
• Geometric and Geostatistical Modeling of Point Bars
• 1 Introduction
• 2 An Overview of Point Bar Geometry
• 3 Modeling Approach
• 4 Channel and Point Bar Facies Identification
• 5 Channel Path Recreation
• 6 Channel Path Migration
• 7 Modeling the IHS Geometry
• 8 Grid Generation
• 9 Preservation of Point Bar Architecture and Its Internal Heterogeneity
• 10 Concluding Remarks
• References
• Application of Reinforcement Learning for Well Location Optimization
• 1 Introduction
• 2 Theory.
• 3 Well Location Problem
• 4 Case Studies
• 5 Discussion
• 6 Conclusion
• Appendix
• Neural Network Architecture for Different Case Studies
• Visualization of Convergence
• References
• Compression-Based Modelling Honouring Facies Connectivity in Diverse Geological Systems
• 1 Introduction
• 2 Connectivity in Facies Models and Natural Systems
• 3 Compression-Based Facies Modelling
• 4 Conclusions
• References
• Spatial Uncertainty in Pore Pressure Models at the Brazilian Continental Margin
• 1 Introduction
• 2 Theoretical Foundations and Definitions
• 3 Data Presentation and Interpretation
• 4 Conclusions
• 5 Benefits Promoted by This Work
• References
• The Suitability of Different Training Images for Producing Low Connectivity, High Net:Gross Pixel-Based MPS Models
• 1 Introduction
• 2 Pixel-Based MPS Modelling with Common Training Images
• 3 Pixel-Based Modelling with Low Connectivity
• 4 Summary
• References
• Probabilistic Integration of Geomechanical and Geostatistical Inferences for Mapping Natural Fracture Networks
• 1 Introduction
• 2 MPS Algorithm in Classification Framework
• 3 Combination of Probabilities
• References
• Mining
• Artifacts in Localised Multivariate Uniform Conditioning: A Case Study
• 1 Introduction
• 2 Multivariate Uniform Conditioning and LMUC
• 3 Case Study Presentation and Results
• 3.1 Global and Local Scatterplots
• 3.2 Correlation Between Localised Attributes
• 4 Conclusions
• References
• Methodology for Defining the Optimal Drilling Grid in a Laterite Nickel Deposit Based on a Conditional Simulation
• 1 Introduction
• 2 Sequential Gaussian Simulation
• 3 Sequential Indicator Simulation
• 4 Optimisation of a Drilling Grid
• 5 Case Study
• 5.1 Methodology
• 5.2 Geostatistical Simulation with Original Database.
• 5.3 Geostatistical Simulation with a Virtual Drilling Grid Database
• 5.4 Geostatistical Simulation of 100 Realisations of Thickness, Nickel and Ore Type
• 6 Results and Discussion
• 7 Conclusions
• References
• LSTM-Based Deep Learning Method for Automated Detection of Geophysical Signatures in Mining
• 1 Introduction
• 2 Data Used
• 3 Methodology
• 3.1 Long Short-Term Memory (LSTM)
• 3.2 Training and Validation
• 4 Results and Discussion
• 5 Conclusion
• References
• Earth Science
• Spatio-Temporal Optimization of Groundwater Monitoring Network at Pickering Nuclear Generating Station
• Domains
• Applying Clustering Techniques and Geostatistics to the Definition of Domains for Modelling
• 1 Introduction
• 1.1 Machine Learning in Mining
• 1.2 Stationarity in the Context of Mineral Resource Modeling
• 1.3 Types of Clustering Algorithms and Background
• 1.4 Discussions on the Validation Process
• 1.5 Supervised Learning Applied to the Classification of New Samples
• 2 Methods and Workflow
• 2.1 Clustering Algorithms
• 2.2 Validation Methods
• 2.3 Automatic Classification of New Samples
• 2.4 Workflow
• 3 Case Study
• 3.1 Exploratory Data Analysis
• 3.2 Applying Cluster Analysis and Verifying the Results
• 3.3 Discussions on the Results of the Cluster Analysis
• 3.4 Supervised Learning Applied to the Automatic Classification of New Samples
• 4 Conclusions
• References
• Addressing Application Challenges with Large-Scale Geological Boundary Modelling
• 1 Introduction
• 2 Geology
• 3 Gaussian Processes
• 4 A Priori Data
• 5 Model Building
• 5.1 Spatial Rotations
• 5.2 Region Overlap
• 5.3 Mesh Resolution
• 5.4 Model Evaluation
• 6 Unassayed Production Holes
• 6.1 Results
• 7 Discussion and Conclusions
• References
• Appendix A Appendix: Short Abstracts
• Theory
• Petroleum
• Mining
• Earth Science.
• Domains
• Author Index.