Journal ArticleDOI
Direct Sequential Simulation and Cosimulation
TLDR
In this paper, a new approach for the direct sequential simulation is proposed, which is to use the local sk estimates of the mean and variance, not to define the local cdf but to sample from the global cdf.Abstract:
Sequential simulation of a continuous variable usually requires its transformation into a binary or a Gaussian variable, giving rise to the classical algorithms of sequential indicator simulation or sequential Gaussian simulation. Journel (1994) showed that the sequential simulation of a continuous variable, without any prior transformation, succeeded in reproducing the covariance model, provided that the simulated values are drawn from local distributions centered at the simple kriging estimates with a variance corresponding to the simple kriging estimation variance. Unfortunately, it does not reproduce the histogram of the original variable, which is one of the basic requirements of any simulation method. This has been the most serious limitation to the practical application of the direct simulation approach. In this paper, a new approach for the direct sequential simulation is proposed. The idea is to use the local sk estimates of the mean and variance, not to define the local cdf but to sample from the global cdf. Simulated values of original variable are drawn from intervals of the global cdf, which are calculated with the local estimates of the mean and variance. One of the main advantages of the direct sequential simulation method is that it allows joint simulation of N
v variables without any transformation. A set of examples of direct simulation and cosimulation are presented.read more
Citations
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Book
Applied Geostatistics with SGeMS: A User's Guide
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.
Journal ArticleDOI
Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review
Ana Horta,Brendan P. Malone,Uta Stockmann,Budiman Minasny,Thomas F. A. Bishop,Alex B. McBratney,Robert Pallasser,L. E. Pozza +7 more
TL;DR: In this article, a cost-effective method for measurement, sampling design, and assessment of contaminated soil sites is presented, aided by on-the-go proximal soil sensing; and expedited by subsequent adaptive spatially optimal sampling and prediction procedures enabled by field spectroscopic methods and advanced geostatistics.
Journal ArticleDOI
Homogenization of Climate Data: Review and New Perspectives Using Geostatistics
TL;DR: In this paper, a geostatistical simulation approach is applied to precipitation data from 66 monitoring stations located in the southern region of Portugal (1980-2001) and compared with those from three well established statistical tests: the Standard normal homogeneity test (SNHT) for a single break, the Buishand range test, and the Pettit test.
Journal ArticleDOI
Trends in extreme precipitation indices derived from a daily rainfall database for the South of Portugal
TL;DR: In this paper, a qualitative classification of 106 daily rainfall series from stations located in the South of Portugal and evaluates temporal patterns in extreme precipitation by calculating a number of indicators at stations with homogeneous data within the 1955/1999 period.
Journal ArticleDOI
Linear inverse Gaussian theory and geostatistics
TL;DR: Inverse problems in geophysics require the introduction of complex a priori information and are solved using computationally expensive Monte Carlo techniques (where large portions of the model space are explored) as discussed by the authors.
References
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Book
Stochastic Simulation
TL;DR: Brian D. Ripley's Stochastic Simulation is a short, yet ambitious, survey of modern simulation techniques, and three themes run throughout the book.
Book ChapterDOI
Joint Sequential Simulation of MultiGaussian Fields
TL;DR: The sequential simulation algorithm can be used for the generation of conditional realizations from either a multiGaussianrandom function or any non-Gaussian random function as long as its conditional distributions can be derived.
BookDOI
Geostatistics for natural resources characterization
TL;DR: In this article, Kriging is used for estimating the recoverable reserve of a mine in the presence of faults in the reserve map of a Porphyry copper mine in Canada.
Journal ArticleDOI
Joint simulation of multiple variables with a Markov-type coregionalization model
TL;DR: This new algorithm builds on a Markov-type hypothesis whereby collocated information screens further away data of the same type, allowing cosimulation without the burden of a full cokriging, yet at less cpu cost.
Book ChapterDOI
Modeling Uncertainty: Some Conceptual Thoughts
TL;DR: The place of random experiments and stochastic models in experimental sciences is discussed first and a safeguard for building models of uncertainty is to charge them maximally with data deemed relevant to the “unknown” at hand.
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