References
Some references about DEESSE
J. Straubhaar, P. Renard (2021) Conditioning Multiple-Point Statistics Simulation to Inequality Data. Earth and Space Science, doi:10.1029/2020EA001515
J. Straubhaar, P. Renard, T. Chugunova (2020) Multiple-point statistics using multi-resolution images. Stochastic Environmental Research and Risk Assessment 20, 251-273, doi:10.1007/s00477-020-01770-8
J. Straubhaar, P. Renard, G. Mariethoz (2016) Conditioning multiple-point statistics simulations to block data. Spatial Statistics 16, 53-71, doi:10.1016/j.spasta.2016.02.005
G. Mariethoz, J. Straubhaar, P. Renard, T. Chugunova, P. Biver (2015) Constraining distance-based multipoint simulations to proportions and trends. Environmental Modelling & Software 72, 184-197, doi:10.1016/j.envsoft.2015.07.007
G. Mariethoz, P. Renard, J. Straubhaar (2010) The Direct Sampling method to perform multiple-point geostatistical simulation. Water Resources Research 46, W11536, doi:10.1029/2008WR007621
Reference about DEESSEX
A. Comunian, P. Renard, J. Straubhaar (2012) 3D multiple-point statistics simulation using 2D training images. Computers & Geosciences 40, 49-65, doi:10.1016/j.cageo.2011.07.009
Some references about GRF
J. W. Cooley, J. W. Tukey (1965) An algorithm for machine calculation of complex Fourier series. Mathematics of Computation 19(90):297-301, doi:10.2307/2003354
C. R. Dietrich, G. N. Newsam (1993) A fast and exact method for multidimensional gaussian stochastic simulations. Water Resources Research 29(8):2861-2869, doi:10.1029/93WR01070
A. T. A. Wood, G. Chan (1994) Simulation of Stationary Gaussian Processes in
. Journal of Computational and Graphical Statistics 3(4):409-432, doi:10.2307/1390903
Other references
C. Lantuéjoul (2002) Geostatistical Simulation, Models and Algorithms. Springer Verlag, Berlin, 256 p.
P. Renard, D. Allard (2013), Connectivity metrics for subsurface flow and transport. Advances in Water Resources 51:168-196, doi:10.1016/j.advwatres.2011.12.001
J. Straubhaar, P. Renard (2024), Exploring substitution random functions composed of stationary multi-Gaussian processes. Stochastic Environmental Research and Risk Assessment, doi:10.1007/s00477-024-02662-x