نتایج جستجو برای: geostatistical simulation

تعداد نتایج: 561254  

Journal: :CoRR 2018
Lukas Mosser Olivier Dubrule Martin J. Blunt

Geostatistical modeling of petrophysical properties is a key step in modern integrated oil and gas reservoir studies. Recently, generative adversarial networks (GAN) have been shown to be a successful method for generating unconditional simulations of poreand reservoir-scale models. This contribution leverages the differentiable nature of neural networks to extend GANs to the conditional simula...

Journal: :Journal of Geographical Systems 2015
Daisuke Murakami Morito Tsutsumi

The modifiable areal unit problems (MAUP) is a problem by which aggregated units of data influence the results of spatial data analysis. Standard GWR, which ignores aggregation mechanisms, cannot be considered to serve as an efficient countermeasure of MAUP. Accordingly, this study proposes a type of GWR with aggregation mechanisms, termed area-to-point (ATP) GWR herein. ATP GWR, which is close...

2008
Matthew Williams Dan Cornford Lucy Bastin Ben Ingram

Traditionally, geostatistical algorithms are contained within specialist GIS and spatial statistics software. Such packages are often expensive, with relatively complex user interfaces and steep learning curves, and cannot be easily integrated into more complex process chains. In contrast, Service Oriented Architectures (SOAs) promote interoperability and loose coupling within distributed syste...

Journal: :International Journal of Health Geographics 2008
Pierre Goovaerts Samson Gebreab

BACKGROUND Geostatistical techniques are now available to account for spatially varying population sizes and spatial patterns in the mapping of disease rates. At first glance, Poisson kriging represents an attractive alternative to increasingly popular Bayesian spatial models in that: 1) it is easier to implement and less CPU intensive, and 2) it accounts for the size and shape of geographical ...

2007
G. V. Last C. J. Murray D. A. Bush E. C. Sullivan M. L. Rockhold B. N. Bjornstad

The vadose zone beneath the USDOE’s Hanford Site in southeastern Washington State (Fig. 1) received approximately 1 trillion liters (450 billion gallons) of liquid waste, some contaminated with radioactive and hazardous contaminants. Today, Hanford is engaged in the world’s largest environmentalcleanup project, and a variety of conceptual and mathematical vadose zone models have been developed ...

Journal: :Scandinavian Journal of Statistics 2006

2011
Yu-Pin Lin Hone-Jay Chu Chen-Fa Wu Tsun-Kuo Chang Chiu-Yang Chen

Concentrations of four heavy metals (Cr, Cu, Ni, and Zn) were measured at 1,082 sampling sites in Changhua county of central Taiwan. A hazard zone is defined in the study as a place where the content of each heavy metal exceeds the corresponding control standard. This study examines the use of spatial analysis for identifying multiple soil pollution hotspots in the study area. In a preliminary ...

Journal: :ISPRS Int. J. Geo-Information 2015
Marcelo Pedroso Curtarelli Joaquim Leão Igor Ogashawara João Antônio Lorenzzetti José L. Stech

The generation of reliable information for improving the understanding of hydroelectric reservoir dynamics is fundamental for guiding decision-makers to implement best management practices. In this way, we assessed the performance of different interpolation algorithms to map the bathymetry of the Tucuruí hydroelectric reservoir, located in the Brazilian Amazon, as an aid to manage and operate A...

2007
Brian J. Smith Jun Yan Mary Kathryn Cowles

Spatial data, either areal or geostatistical (point-referenced), are becoming increasingly utilized in the study of many scientific fields due to the accessibility of data monitoring systems and associated datasets. When both types of data are available for the same underlying spatial process, computationally efficient and statistically sound methods are needed for their joint analysis. Markov ...

ژورنال: اندیشه آماری 2017
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‎Analysis of large geostatistical data sets‎, ‎usually‎, ‎entail the expensive matrix computations‎. ‎This problem creates challenges in implementing statistical inferences of traditional Bayesian models‎. ‎In addition,researchers often face with multiple spatial data sets with complex spatial dependence structures that their analysis is difficult‎. ‎This is a problem for MCMC sampling algorith...

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