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The research group is affiliated to the University of Lausanne, the UNSW School of Civil and Environmental Engineering, and supported by Swiss National Science Foundation (SNSF), the National Centre for Groundwater Research and Training (NCGRT), and the National ICT Australia (NICTA)

 

·         Gregoire Mariethoz (Professor Assistant, UNIL)

·         Lionel Benoit (PhD Candidate, UNIL) – Rainfall measurement and stochastic space-time simulation/interpolation.

·         Mathieu Gravey (PhD Candidate, UNIL) – GPU-based topography point clouds completion methods, remote sensing images colorization.

·         Luiz Gustavo Rasera (PhD Candidate, UNIL) – geostatistical topography downscaling and data fusion.

·         Inigo Irarrazaval (PhD Candidate, UNIL) – Subglacial channels modeling with inverse methods.

·         Elisa Giaccone (PhD Candidate, UNIL, main supervisor: Christophe Lambiel, part of the Integralp project) – Interactions geomorphology/plants/permafrost in alpine environments.

·         James Thornton (PhD Candidate, University of Neuchatel, main supervisor: Philip Brunner, part of the Integralp project) – Hydrological and hydrogeological modeling in alpine environments.

·         Moctar Dembele (PhD Candidate, UNIL, main supervisor: Bettina Schaefli) – Hydrology and remote sensing for water accounting.

·         Qiyu Chen (PhD Candidate, UNIL / China University of Geosciences) - Geological stochastic modeling method for the Quaternary sedimentary system 3D geological stochastic modeling method for Quaternary sedimentary systems.

·         Juan Carlos Castilla (PhD Candidate, UNSW Australia) – 3D aquifer modeling, agents-based models, uncertainty in hydrogeological models, climate change.

 

Former group members

·         Xue Li (former PhD Candidate, UNIL / University of Science and Technology of China) – Development of geostatistical simulation algorithms.

·         Ehsan Baninajar (former PhD Candidate, UNIL / Sahand University of Technology, Iran) – Automatic parameterization of MPS methods.

·         Fabio Oriani (former PhD Candidate, University of Neuchatel) – Climatic time-series modeling with Multiple-point geostatistics.

·         Kashif Mahmud (former Former PhD student at UNSW Australia) – Multiple-point geostatistics, texture synthesis, upscaling, multivariate analysis, karst modeling.

·         Sanjeev Jha (former Senior Researcher at UNSW Australia, now with the Australian Bureau of Meteorology) – Downscaling of hydrological data, 3D geological modeling under structural heterogeneity and variable connectivity, river sedimentary processes and bathymetry.

 

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 partial 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.

 

 

Agent-based models for water resources management

 

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

 

MPS with local proportions and trends

 

 

MPS for time series modeling – also for rainfall modeling

 

Multiscale MPS modeling

 

Training image selection and validation

 

Stochastic simulation of meandering structures with satellite-based analogs

 

Texture synthesis algorithms – including Image Quilting

 

Climate models downscaling – see 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

 

Meanders generation

 

Remote sensing spatio-temporal gaps reconstruction

 

Percolation processes in karst environments and chaotic behavior

 

Paleoclimate data quantitative analysis

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Super-Resolution

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Transform-invariant distances – with applications to subsurface models

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Spatial inverse problems: Iterative Spatial Resampling

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Direct Sampling

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Parallelization

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Image/volume completion

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Probability aggregation

Plurigaussian simulations