One of our main research areas is related to Multiple-Point Statistics, and here is a list of references related to MPS.
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).
- 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
Rainfall simulation with multiple-point geostatistics including radar data, elevation and weather types.
Download the Fortran code for patterns validation
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.
Powerpoint presentation: Mariethoz_cosimul_proba_aggregation.ppt
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. mariethoz2009b.pdf