## 4 Results  
### Multiple Station Processing Results  
The MT datasets were processed using a recent robust multivariate processing routine based on the eigenvalue decomposition method within the Goethe University software package, FFMT [34]. Written in MATLAB, this multistation processing routine adopts a multivariate statistical estimation for magnetotelluric data introduced by [35]. The system utilizes both electric and magnetic channels from two or more stations to identify and filter out disruptive, uncoordinated noise within the data, allowing for the isolation of signals originating exclusively from the MT source fields. The theory of these standard robust processing routine and eigenvalue decomposition method is described in more detail in [PERSON] et al. [34], [PERSON] [36], [PERSON] [37], and [PERSON] [38]. The basics steps of this processing routine entail filtering of the measured time series with a target frequency depending on a high pass and low pass Butterworth filter and then dividing them into time windows. Transfer function estimates are calculated independently for each time window and each target frequency. When obtaining good data quality, the first and second eigenvalues show the two polarizations of the MT source field. They should be much stronger than the third eigenvalue. However, the third eigenvalue exhibits time-dependent fluctuations as well. The third eigenvalue of a perfect plane wave, as predicted by the magnetotelluric technique, would be zero at all time intervals and in all periods. There are several time periods when the third eigenvalue is identical to the first eigenvalue, suggesting the presence of sources other than the plane wave. Figure 3 provides a visual representation of the first and third eigenvalues across all time intervals and periods. Figure 3: Frequency and time-dependent first and third eigenvalues for all time windows for time series from Site501_ADU_2 Ch, measured with a sampling rate of 2048 Hz. The time interval is shown along the \(x\)-axis and target frequencies along the \(y\)-axis. The recording took place from 6:00 p.m. to 6:00 a.m. CAT and lasted 12 h. The color scale can be used as an indicator of the signal-to-noise ratio. The MT signal has two polarizations, which means only two large eigenvalues should be present. Large 3 rd eigenvalue = additional EM sources or noise. The processing method employed in this research is effective in assessing the extent of incoherent noise but is not well suited for distinguishing coherent noise that affects all stations from the actual data [34; 38]. However, when examining the number of dominating eigenvalues, there may be indications of impacts from coherent noise sources. Figure 4 depicts a weighted median derived from the eigenvalue of the preferred time window and selected eigenvalues. The first, second, and third eigenvalues are significantly separated at longer periods. The median result was set to include 80% of the time intervals. The outcomes were significantly improved by selecting this higher percentage. A reliable time series processing is essential, especially for the data that have been hampered by anthropogenic noise. The MT data were heavily affected by cultural noise due to the proximity of Kasane town. The multivariate processing routine explained above has shown a significant improvement in the quality of processed data from the study area. Figure 4: The eigenvalues, calculated as a weighted median, pertain to the top 80% of chosen time intervals. First eigenvalue = red, 2 nd eigenvalue = black, all others = blue. Utilizing the impedance tensor, we computed the apparent resistivity \(\rho_{a,xy}\) and impedance phase \(\phi\) for site701 (ADU-2 Ch), site702 (ADU-4 Ch), and site702 (MTU-5A) through a multi-station processing approach, as these three sites were concurrently sampled.