Code
01CD1723
Duration
01 June 2023 → 31 May 2024
Funding
Regional and community funding: Special Research Fund
Promotor
Fellow
Research disciplines
-
Natural sciences
- Hydrogeology
- Geophysics not elsewhere classified
-
Engineering and technology
- Water resources management
Keywords
uncertainty
Electromagnetic geophysical data
Salinity
Project description
Imaging the subsurface is of prime importance for many geological applications. Among geophysical
techniques, electromagnetic methods (EM) have become popular for rapidly covering large areas.
However, the solution of the inversion of EM data is not unique. In this research we are quantifying the
uncertainty of salinity estimation from EM data using a new framework called Bayesian evidential
learning.