SC1.29 ECS
A hands-on introduction to Multiple Point Statistics
Convener: Mathieu Gravey | Co-conveners: Moctar Dembélé, Fabio Oriani
Fri, 12 Apr, 08:30–10:15
Room -2.62

Recommended for: geoscientists, climate scientists, geostatisticians, engineers.

This course is an introduction to stochastic simulation using Multiple Point Statistics (MPS), a modelling approach based on the use of training images with the aim of generating realistic heterogeneity characterizing natural processes. This family of techniques has been shown to be particularly suited for preserving complex features, for example the connectivity and geometry of geological units [1], the seasonality and complex time dependence of climate time-series [2], or the small-scale variability of missing data from remote sensing images [3].

In the routine practice, MPS can be used to fill the gaps in spatial or temporal datasets, interpolate sparse data, or simulate random fields to study the uncertainty of a process outcome. We will present the theory behind MPS, demonstrate an open-source code, and give practical tutorials on how to use it.

The course will be organized in two parts: the first one is a short introduction on the theory at the base of stochastic simulation and interpolation. The second and main part is dedicated to practical cases related to time series modeling and remote sensing data.


[1] dell’Arciprete, D., Bersezio, R., Felletti, F. et al., Comparison of three geostatistical methods for hydrofacies simulation: a test on alluvial sediments, Hydrogeol Journal (2012) 20: 299.

[2] Oriani F, Mehrotra R, Mariethoz G, Straubhaar J, Sharma A, Renard P (2017). Simulating rainfall time-series: how to account for statistical variability at multiple scales, Stochastic Environmental Research and Risk Assessment, doi: 10.1007/s00477-017-1414-z.

[3] Gaohong Yin, Gregoire Mariethoz, Ying Sun & Matthew F. McCabe (2017) A comparison of gap-filling approaches for Landsat-7 satellite data, International Journal of Remote Sensing, 38:23, 6653-6679, DOI: 10.1080/01431161.2017.1363432