## 3 Conclusions  
Lower positional accuracy directly affects the accuracy of station velocity predictions, which in turn hampers the accurate calculation of fault slip velocities. This limitation is particularly critical for faults with low slip rates (e.g., 1-2 mm/year), as the calculated slip velocities may fall within or below the sigma value, obscuring or entirely masking the fault's actual movement.  
The key findings derived from the analysis in the presented study are summarized below:  
* East (E) Component: For 1-year interval GNSS data, achieving \(\pm\)1.5 mm position accuracy per epoch resulted in a velocity accuracy greater than \(\pm\)1 mm/year, with the best observed accuracy being \(\pm\)1.3 mm/year. However, for 2- and 3-year interval datasets, submillimeter velocity accuracies could be achieved. Specifically, the best velocity accuracies were \(\pm\)0.6 mm/year for 3-year intervals and \(\pm\)0.7 mm/year for 2-year intervals.
* North (N) Component: Similarly, for 1-year interval GNSS data with \(\pm\)1.5 mm position accuracy per epoch, the maximum attainable velocity accuracy was \(\pm\)1.4 mm/year. For 2- and 3-year interval data, the best achievable velocity accuracies improved to \(\pm\)0.6 mm/year for 3-year intervals and \(\pm\)0.8 mm/year for 2-year intervals.
* Overall Observations: For GNSS campaigns conducted at 2- or 3-year intervals, velocity accuracies within \(\pm\)1.5 mm/year are achievable for both components, provided the position accuracies remain below \(\pm\)5 mm per epoch.
* The position accuracies of campaigns 1 and 3 had a more pronounced impact on velocity accuracy. However, as the positional accuracy of campaign 1 deteriorated, the influence of campaign 2's positional accuracy became increasingly significant.  
Figure 9: Prediction of velocity accuracies as a function of position accuracies and temporal intervals.  
The findings indicate that conducting three campaigns (one epoch per year) with 1-year interval GNSS data is sufficient to achieve a velocity accuracy of less than \(\pm 2\) mm/year, which is considered the reference threshold for fault slip rate determination studies. However, under the tested conditions, it was not feasible to obtain a velocity accuracy lower than \(\pm 1\) mm/year with only three campaigns of 1-year interval data. This highlights the importance of developing a tailored observation plan based on fault activity, particularly in newly established seismogeodetic networks. Specifically, three campaigns with a 1-year interval are adequate for Holocene faults with relatively higher slip velocities. In contrast, for Quaternary faults, which generally exhibit lower slip rates, GNSS observations at 2- to 3-year intervals or the inclusion of four or more GNSS observations are necessary to achieve the required accuracy.  
The developed ML model enables the analysis of the relationship between position accuracies and velocity accuracies in a unique dataset of 1500 lines produced specifically for the study. With these results obtained for the first time, when establishing a new seismogeodetic network or planning GNSS observations around faults that have experienced destructive earthquakes and completed their post-seismic period, the velocity accuracy can be predicted, and GNSS observation intervals can be optimized based on the required position accuracies, as outlined by [13] or [42]. This allows for more efficient planning of field studies for tectonic-focused GNSS observations, such as extending the measurement period, adding a fourth campaign, or increasing the time between campaigns. Consequently, this approach can help achieve velocity accuracies that enhance the precision of earthquake hazard analysis, ultimately leading to more reliable risk assessments.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The author declares no conflicts of interest.