It's important that all ERT surveys in the CPERS database are processed in a standardized way.
Below, you will find the details for a generalized data processing workflow that was automatically applied to all incoming datasets to produce reasonable and consistent results.
This data processing happens behind the scenes to prepare the data that you will see on the map of surveys.
The data processing algorithm has two main steps: data filtering and inversion. The data filtering step includes a technical filter, magnitude filter, and moving median filter (Rosset et al., 2013) and a step to identify bad electrodes and remove their data points. The inversion uses an optimized regularization parameter, L1 model norm, L2 data norm, and a noise model determined by reciprocal data (if available) or an assumed noise model of 4% relative noise. The inversion uses pyGIMLi (Rücker et al., 2017), an open-source Python library.
A preview of the code is shown below. If you want to download, use, or modify the script you can visit the GitHub repository.