Research Explorer

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Project

Improving salinity estimation from electromagnetic data using Bayesian Evidential Learning

Outputs and Outcomes

Code

01CD1723

Duration

01 June 2023 → 31 May 2024

Funding

Regional and community funding: Special Research Fund

Promotor

Thomas Hermans

Fellow

Arsalan Ahmed

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.

English

Dutch

English

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