The results from noise estimation via GPR and the statistical method are compared in Figure . The two methods show a good agreement in the intermediate frequency range between about 0.1 Hz and 100 kHz. At higher frequencies, GPR shows a larger noise level than the statistical analysis. The reason is the presence of a systematic feature, which resembles Gaussian noise at the chosen sampling density but is present in all the data sets. If noise is estimated from a single measurement only, such systematic features cannot be distinguished. If the GPR noise level is determined from repetitions of the experiment, then the similarity of the noise becomes apparent as well, leading to a reduced noise estimate (Figure ). In the low frequency region, the statistical method produces a higher noise estimate. One reason for this observation is the tendency for overfitting by GPR when insufficient data is available, as may be the case at the boundaries of a data set . Furthermore, the statistical method may overestimate the random noise level due to the abovementioned non-random drift contribution that is not reproduced for different repetitions of the experiment. In this case, GPR may even provide a more faithful set of weights for DRT inversion. Mean GPR determined noise level of all the data sets, which were individually averaged over two frequency decades. The shaded regions represent the standard deviation. The gray curves show the GPR noise estimate after moving average for individual data sets. b) Ratio of noise standard deviation between statistically determined and GPR noise levels for the real (blue) and the imaginary part of the impedance (black). averaged over two frequency decades. c) Comparison of statistically determined noise standard deviation (blue: real; black: imaginary) and standard deviation of residuals between GPR estimate and experimental data (red: real; magenta: imaginary), without moving average.