## 3 Results
### Shear-Wave Splitting Parameters
Small fluctuations of \(\pm 1\) km or even \(\pm 2\) km from that average depth don't seem to affect the measured \(t_{\mathrm{n}}\) significantly, as the resulting changes are much smaller than the observed measurement scatter. We focus on patterns of significant linear increase or decrease of \(t_{\mathrm{n}}\) values observed at more than one station during specific periods, as this may suggest a change that could be related to more widespread stress variations. To assess this task, we performed a series of linear regressions on sliding windows along the \(t_{\mathrm{n}}\) data (with or without the application of the above-mentioned filters) versus origin time. The regressions were applied using a range of different window-lengths, measured either in number of successive data points or days, to allow for various time scales and data densities to be taken into account. For each window, least-squares inversion is performed to calculate the best-fit line, along with the respective correlation coefficient, \(R^{2}\). On each case, the Student's t-test is performed to examine whether the null hypothesis of zero slope can be rejected with a 95% confidence interval, and cases with \(p>0.05\) are discarded. We then examined the results and identified patterns of increase or decrease, which either persist or emerge, as stricter filters are applied. Furthermore, we examined periods during which a group of stations presented the same behavior regarding the \(t_{\mathrm{n}}\) change, whereas another group exhibited the opposite change behavior. The 3-point moving average is also calculated, to account for non-linear temporal variations or weak correlations. Figure 9 shows the \(t_{\mathrm{n}}\) temporal variations for selected stations. In all cases, Band-1 was selected, while filtering out measurements with \(\varphi\) outside the \(\overline{\varphi}\pm 30^{\circ}\) range (with \(\overline{\varphi}\) as determined from Cat1) usually manages to further reduce scattering. Certain common characteristics are identified between different stations. In January-April 2013, stations PSAR, KALE and ZIRI exhibit a moderate decrease of \(t_{\mathrm{n}}\) values. Weak decrease is also observed at stations TRIZ and PYRG, whereas station SERG has increasing \(t_{\mathrm{n}}\) values during February-May 2013. That is the period before the occurrence of the 2013 Helike swarm. Station TRIZ presents a mild increase of \(t_{\mathrm{n}}\) in the period of May-July 2013, while time-delays at PYRG and ALIK (with events from groups #6 and #8 only; Figure 9) also follow a weakly increasing trend that lasts until December 2014. On the other hand, in October 2013-January 2014, several stations in the north present a significant decrease of \(t_{\mathrm{n}}\) values, including PSAR, KALE, TRIZ and most of the stations of the Magoula array, e.g., MG04 (Figure 9). One of the trend lines of station PYRG also indicates a strong decrease of \(t_{\mathrm{n}}\) between November 2013 and March 2014. This period could be of particular significance, as it coincides with the time when the proposed seismicity triggering front of Figure 6 passes near the middle of profile A-B (\(Y\approx 25\) km). On the contrary, some stations in the south, such as AIOA and ALIK (the latter for events of groups #6 and #8), exhibit an increasing pattern in \(t_{\mathrm{n}}\) values during that period. February 2014 marks the beginning of new trends for many stations, with increasing \(t_{\mathrm{n}}\) values for PYRG and ZIRI and decreasing ones for AIOA and TRIZ. Lastly, a decreasing \(t_{\mathrm{n}}\) trend is exhibited at several stations, including ZIRI, KALE, AGRP and SERG, towards the last quarter of the study period; a time-span that begins with the \(M_{\mathrm{w}}=4.2\) event of 8 June 2014 and is marked by the seismic swarm in the Nafpaktos-Psathopyrgos area.