ESR N° 15: Integration of dynamical hydrogeophysical data in a multiple-point geostatistical framework

 

Research Fellow : JORGE LOPEZ ALVIS

Find him on ResearchGate !

Host Institution : Applied Geophysics, University of Liege, Belgium

Objectives

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.

Supervisors

  • Prof. F. Nguyen, Applied Geophysics, University of Liege
  • Prof. Thomas Hermans, Department of Geology, Ghent University

Glossary

  • 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


Previous page : see ESR14-Project