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.
More details about ESR15-project:
- Tasks and methodology
- Dissemination and communication
- Database for the future datasets
- 6 months at CSIC in Barcelona: 1/07/2019 to 1/01/2020
- 2 months Aquale (31/03/2020-31/05/2020) and/or 2 months at Ghent University (31/03/2020-31/05/2020) ( The Coordinator officially notified the EU to add Ghent University as partner on the Project Officer recommendation.)
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
Dissemination & communication:
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- June 2018: Oral presentation at the 2018 Computational Methods in Water Resources (CMWR) conference, titled “Updating prior geologic uncertainty with GPR traveltime tomographic data”.
- July 2018: Poster presented during the 4th Cargèse Summer School, titled “Updating uncertainty in hierarchical subsurface model using geophysical data: synthetic case for crossborehole-hole GPR”.
- November 2018: Geology Research seminar in University of Ghent, titled “Using geophysical data to update uncertainty in structural parameters of subsurface models”.
Database for the future datasets : H+ database
- 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.