Open AccessBook
Applied Geostatistics with SGeMS: A User's Guide
Nicolas Remy,Alexandre Boucher,Jianbing Wu +2 more
- 23 Mar 2009
TL;DR: In this article, the authors present a general overview of Geostatistics: a recall of concepts, data sets, SGeMS EDA tools, common parameter input interfaces, estimation algorithms and stochastic simulation algorithms.
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Abstract: 1. Introduction 2. General overview 3. Geostatistics: a recall of concepts 4. Data sets & SGeMS EDA tools 5. Variogram computation and modeling 6. Common parameter input interfaces 7. Estimation algorithms 8. Stochastic simulation algorithms 9. Utilities 10. Scripting, commands and plug-ins List of programs List of symbols Bibliography.
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Citations
Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable
TL;DR: SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty in prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.
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Research on the reconstruction method of porous media using multiple-point geostatistics
TL;DR: In this article, a reconstruction method of porous media using the structural characteristics captured by the data templates of multiple-point geostatistics is proposed, where the probability of each structural characteristic of a pore space is acquired first, and then these characteristics are reproduced according to the probabilities to present the real structural characteristics in the reconstructed images.
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Spatial variability of selected physicochemical parameters within peat deposits in small valley mire: a geostatistical approach
TL;DR: In this article, a geostatistical method for 2D and 3D modelling spatial variability of selected physicochemical properties of biogenic sediments was applied to a small valley mire in order to identify the processes that lead to the formation of various types of peat.
A methodology for quantifying the value of spatial information for dynamic Earth problems
TL;DR: This transferable methodology provides a framework to estimate the value of spatial data given a particular decision scenario and demonstrates that a higher VOI occurs when the geophysical attribute (the data) better discriminates between geological indicators.
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References
A Computational Approach to Edge Detection
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
29.9K
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Inverse Problem Theory and Methods for Model Parameter Estimation
Albert Tarantola
- 20 Dec 2004
TL;DR: This chapter discusses Monte Carol methods, the least-absolute values criterion and the minimax criterion, and their applications to functional inverse problems.
Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics
TL;DR: The approach proposed in this paper consists of borrowing the required multiple-point statistics from training images depicting the expected patterns of geological heterogeneities from the geostatistical numerical model where they are anchored to the actual data in a sequential simulation mode.
1.6K
The intrinsic random functions and their applications
TL;DR: The intrinsic random functions (IRF) are a particular case of the Guelfand generalized processes with stationary increments and constitute a much wider class than the stationary RF, and are used in practical applications for representing nonstationary phenomena as discussed by the authors.