Research Fellow : JORGE LOPEZ ALVIS
Find him on ResearchGate !
Host Institution : Applied Geophysics, University of Liege, Belgium
Integrate time-lapse geophysical data sets, at an hourly or daily temporal resolution, into a geostatistical inverse framework to gain insight on transport processes and hydrogeological parameters distribution in the subsurface.
Tasks and methodology
Bayesian evidential learning (BEL) of data-prediction relationships will be applied using geophysical data and subsurface transport models. Within the BEL framework, probabilistic falsification will be used to check for consistency of plausible geological scenarios. A combination of 3D image analysis on geophysical data (ERT, SIP and GPR), temporal information, multiple-point geostatistical simulations and dimension reduction techniques will be integrated in the framework.
- Prof. F. Nguyen, Applied Geophysics, University of Liege
- Prof. Thomas Hermans, Department of Geology, Ghent University
- BEL – bayesian approach where subsurface models are used to learn a statistical model that relates data and prediction variables.
- ERT – electrical resistivity tomography.
- SIP – spectral induced polarization.
- GPR – ground penetrating radar.
Database for the future datasets : H+ database