In: Environmental Modelling & Software, 2015, vol. 72, p. 184-197
In the last years, the use of training images to represent spatial variability has emerged as a viable concept. Among the possible algorithms dealing with training images, those using distances between patterns have been successful for applications to subsurface modeling and earth surface observation. However, one limitation of these algorithms is that they do not provide a precise control on the...
|
Thèse de doctorat : Université de Neuchâtel, 2009 ; Th. 2105.
Accurately modeling connectivity of geological structures is critical for flow and transport problems. Using multiple-points simulations is one of the most advanced tools to produce realistic reservoir structures. It proceeds by considering data events (spatial arrangements of values) derived from a training image (TI). The usual method consists in storing all the data events of the TI in a...
|
In: Stochastic Environmental Research and Risk Assessment, 2018, vol. 32, p. 321-340
Daily rainfall is a complex signal exhibiting alternation of dry and wet states, seasonal fluctuations and an irregular behavior at multiple scales that cannot be preserved by stationary stochastic simulation models. In this paper, we try to investigate some of the strategies devoted to preserve these features by comparing two recent algorithms for stochastic rainfall simulation: the first one is...
|
