Measurement errors can play a pivotal role in geophysical inversion. Most inverse models require users to prescribe or assume a statistical model of data errors before inversion. Wrongly prescribed errors can lead to over-or under-fitting of data, however, the derivation of models of data errors is often neglected. With the heightening interest in uncertainty estimation within Highlights Stacking, reciprocal and repeatability errors are compared using statistical analysis Having common electrodes increase correlation between measurements A new error model based on grouping the electrodes used is developed The new model yields better inversion results and uncertainty estimates