Optimized Experimental Design (OED)

Thesis details

Optimized Experimental Design (OED)

• 6 months
• 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 98273

Although innovations in acquisition hardware, computational resources and inversion techniques enabled considerable improvements of geophysical subsurface imaging, the underlying design of the experiment ultimately controls the information content of the dataset. Yet in most field campaigns, little attention is put into optimizing the geophysical survey. However, by only utilizing those layouts that provide the highest benefit, experiment optimization promises to maximize the information content of a geophysical dataset while still being cost-effective and quick.

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, like Wenner, Dipole-Dipole and Schlumberger. The Compare-R (CR) method (Uhlemann et al., 2018) provides a particularly interesting algorithm to improve resolution and acquisition time, but was only applied in some field studies in the past (e.g., Meng et al., 2022). Aim of this thesis is to gain further practical experience in the context of OED for geoelectrical measurements tailored to a brand new geoelectrical measurement device (Syscal Terra from IRIS instruments).

Your tasks:

• Set up synthetic forward models of the target area using the CR method to derive optimized survey designs for the application in the field.
• Develop and incorporate practical and device-specific design considerations (e.g., exploitation of available measurement channels of the Syscal Terra) in the algorithm.
• Perform ERT measurements during a field campaign using both conventional (Wenner, Schlumberger, Dipol-Dipol) as well as optimized electrode arrays.
• Process and invert the datasets using pyGIMLi (Rücker et al., 2017) and compare the results achieved with conventional as well as optimized datasets.
• Interpret the ERT models using reference data that provide both geological as well as hydrological information.

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.