A package for geostatistical integration of coarse and fine scale data. Building numerical models in earth science requires integration of many different data. Data typically come from different sources with different volume supports: fine-scale point support and coarse-scale block support. All are valuable information and should be incorporated into the final models. In addition, prior information, such as spatial correlation and property statistics (mean, variance, etc.), should also be considered. Several approaches are proposed to build high resolution models conditioned to both point and linear average block support data and accounting for prior structural information. A 3D case study for downscaling coarse scale data conditioned to point support data is presented. The results demonstrate the applicability and limitations of the proposed algorithms and software package.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Rasera, Luiz Gustavo; Gravey, Mathieu; Lane, Stuart N.; Mariethoz, Gregoire: Downscaling images with trends using multiple-point statistics simulation: an application to digital elevation models (2020)
- Soares, Amílcar; Nunes, Rúben; Azevedo, Leonardo: Integration of uncertain data in geostatistical modelling (2017)
- Mejer Hansen, Thomas; Skou Cordua, Knud; Mosegaard, Klaus: A general probabilistic approach for inference of Gaussian model parameters from noisy data of point and volume support (2015)
- Oliveira, Ana Rita; Branquinho, Cristina; Pereira, Maria; Soares, Amílcar: Stochastic simulation model for the spatial characterization of lung cancer mortality risk and study of environmental factors (2013)
- Mariethoz, Grégoire; Renard, Philippe; Straubhaar, Julien: Extrapolating the fractal characteristics of an image using scale-invariant multiple-point statistics (2011)
- Goovaerts, Pierre: Combining areal and point data in geostatistical interpolation: Applications to soil science and medical geography (2010)