Targeted optimization of geoelectrical experiments

Thesis details

Targeted optimization of geoelectrical experiments

• 6 months
• B.Sc / M.Sc.
• 90% Programming
• 80% Field work
• 10% Lab work
• 60% Theory
• 70% Processing
• 60% Interpretation
• 50% Geology

Contact person

©

M.Sc.

Nino Menzel

PhD researcher

nino.menzel@gim.rwth-aachen.de

+49 241 / 80 98274

Although innovations in acquisition hardware, computational resources and inversion techniques enabled considerable improvements of geophysical subsurface imaging, the information content of an electrical resistivity tomography survey is still largely controlled by the survey design. In many field campaigns, standard electrode layouts such as Wenner, Dipole-Dipole and Schlumberger are used without explicitly accounting for geological uncertainty in the shallow subsurface. By optimizing ERT layouts based on uncertainty-informed geological models, the survey can focus resolution in the most relevant target zones and improve efficiency while remaining practical for field deployment.

Although several OED approaches were developed during the last years, most geoelectrical field surveys are still conducted using either a single or multiple standard electrode configurations. The Compare-R (CR) method (Uhlemann et al., 2018) provides a particularly interesting algorithm to improve resolution and acquisition time, but has only been applied in a limited number of field studies (e.g., Meng et al., 2022). This thesis aims to gain further practical experience in the context of OED for ERT by coupling uncertainty-informed geological models of the near surface with electrode layout optimization for the Syscal Terra device from IRIS instruments.

Your tasks:

• Develop uncertainty-informed geological models of the target area, including shallow subsurface zones of interest and their parameter uncertainty.
• Use these models to identify target regions and to derive ERT survey layouts optimized for information content, applying the CR method or comparable OED strategies.
• Perform synthetic forward modelling and, where possible, field measurements using both conventional (Wenner, Schlumberger, Dipole-Dipole) and uncertainty-guided optimized electrode arrays.
• Process and invert the datasets using pyGIMLi (Rücker et al., 2017) and compare the performance of standard versus uncertainty-informed optimized datasets.
• Interpret the ERT models using reference geological and hydrological data to assess how uncertainty-informed optimization affects subsurface characterization.

Supplementary Documents

Meng, J., Dong, Y., Xia, T., Ma, X., Gao, C., & Mao, D., 2022. Detailed LNAPL plume mapping using electrical resistivity tomography inside an industrial building. Acta Geophysica, 70(4), 1651-1663.

Rücker, C., Günther, T. & Wagner, F.M., 2017. pyGIMLi: An open-source library for modelling and inversion in geophysics. Computers and Geosciences, 109, 106-123.

Uhlemann, S., Wilkinson, P.B., Maurer, H., Wagner, F.M., Johnson, T.C., & Chambers, J.E., 2018. Optimized survey design for electrical resistivity tomography: combined optimization of measurement configuration and electrode placement. Geophysical Journal International, 214(1), 108-121.