Unsupervised data selection for focused time-lapse inversion in electrical resistivity tomography monitoring

Abstract Time-lapse electrical resistivity tomography (TLERT) is a popular technique to monitor subsurface processes. Inversions and interpretation often remain challenging because of noisy data and the superposition of several processes influencing the results. To improve TLERT imaging, we apply clustering of data time series prior to the inversion process. We invert only for a subset of data points displaying some interesting temporal variations. This approach allows to reduce data misfit within the data subset and successfully isolate the main processes from other taking place along the measuring profile. This opens new perspectives for the processing of TLERT data and the monitoring of subsurface processes.