Research

One of our main research areas is related to Multiple-Point Statistics, and here is a list of references related to MPS.

Please check out the book that will tell you everything you always wanted to know about MPS, which is published with Wiley.

Below is a (incomplete and not up-to-date) list of some recent and less recent research topics. For a more complete list, please refer to the publications page where links to journal articles are available, except the very recent ones for which there may be an embargo period. You can also send me an email if you need a specific paper (please no researchgate or linkedin messages).

Ongoing projects

  • Downscaling of digital elevation models
  • Stochastic models for super-small-scale rainfall generation
  • Colorization of multispectral remote sensing images
  • Analogue-based fusion of different satellite products
  • Inverse modeling of subglacial networks
  • Fundamental development of geostatistical algorithms (without preconceptions: working on both training image-based and covariance-based models)
  • Environmental data acquisition with innovative low-cost sensors – we want to know where the data we model are coming from!
  • Application of the above to real-world questions, mostly in the context of environmental change in alpine regions

Published stuff

Rainfall simulation with multiple-point geostatistics including radar data, elevation and weather types.


Gap-filling of satellite images using analogues – and other techniques (with particular focus on Landsat-7, but not only)


Agent-based models for water resources management – and their use to incorporate social dynamics


Gap-filling of environmental time series


Multiple-point simulation using graph cuts


Fusion of radar and rain gauge data (and combining data more in general)


Space-time rainfall simulation


Optimal sampling design aided by training images


Multiple-point simulation with block constraints


Graph cuts for efficient geostatistical inversion


Lidar remote sensing in caves and the relationships between cave morphology and water percolation


MPS with local proportions and trends


MPS for time series modelingalso for rainfall modeling


Multiscale MPS modeling


Stochastic simulation of meandering structures with satellite-based analogs

And some cool animations here and here


Texture synthesis algorithms – including Image Quilting


Climate models downscalingsee also rainfall downscaling


Image Quilting


Validation of training images by patterns comparison

Download the Fortran code for patterns validation


Probabilistic mapping of buried soil features


Reconstruction of buried paleovalleys


River bathymetry modeling


Remote sensing spatio-temporal gaps reconstruction


Percolation processes in karst environments and chaotic behavior


Paleoclimate data quantitative analysis


Super-Resolution


Transform-invariant distances – with applications to subsurface models


Spatial inverse problems: Iterative Spatial Resampling


Direct Sampling multiple-point simulation


Parallelization


Image/volume completion


Probability aggregation

Cosimulation by Probability aggregation

We propose a new cosimulation algorithm for simulating a primary attribute using one or several secondary attributes known exhaustively on the domain. This problem is frequently encountered in surface and groundwater hydrology when a variable of interest is measured only at a discrete number of locations and when the secondary variable is mapped by indirect techniques such as geophysics or remote sensing. In the proposed approach, the correlation between the two variables is modeled by a joint probability distribution function. A technique to construct such relation using underlying variables and physical laws is proposed when field data are insufficient. The simulation algorithm proceeds sequentially. At each location of the domain, two conditional probability distribution functions (cpdf) are inferred. The cpdf of the main attribute is inferred in a classical way from the neighboring data and a model of spatial variability. The second cpdf is inferred directly from the joint probability distribution function of the two attributes and the value of the secondary attribute at the location to be simulated. The two distribution functions are combined by probability aggregation to obtain the local cpdf from which a value for the primary attribute is randomly drawn. Various examples using synthetic and remote sensing data demonstrates that the method is more accurate than the classical collocated cosimulation technique when a complex relation relates the two attributes.

Ressources

Powerpoint presentation: www.minds.ch/gm/ppt/Mariethoz_cosimul_proba_aggregation.ppt

References
Mariethoz Grégoire
, Renard Philippe, Roland Froidevaux, 2009. Integrating auxiliary parameters in geostatistical simulations using probability aggregation. Water resources Research 45(W08421), doi:10.1029/2008WR007408. www.minds.ch/gm/pdf/mariethoz2009b.pdf


Plurigaussian simulations