## 1 Introduction  
Tectonic movements are fundamental processes that shape the dynamic structure of the Earth's crust, involving the movement of tectonic plates [1]. In tectonically active regions, these plate movements can trigger catastrophic earthquakes [2]. As such, modeling tectonic motions is crucial for understanding the Earth's crust dynamics and predicting natural disasters resulting from these movements [3, 4, 5]. Accurately determining the rate of energy accumulation along faults and forecasting how this accumulated energy will be released over time is essential for assessing earthquake risk. The Global Navigation Satellite System (GNSS) technique that offers high location accuracy is widely used for modeling tectonic movements [3, 4, 6, 7, 8, 9, 10]. GNSS-based velocity measurements, geodetic observations, and artificial intelligence-assisted prediction methods have made great advances in modeling tectonic movements. Especially in regions such as Turkey, which is located at the intersection of multiple plate boundaries, such models both reveal new scientific knowledge and help the society to be prepared against natural disasters. The estimation of accurate geodetic velocity has, therefore, great importance in geosciences [11]. The modeling of tectonic movements with the GNSS technique consists of three stages. The first is to establish a GNSS network near the fault that is to be modeled with a geometry representing the fault. At this stage, factors such as the distribution of the stations to be included in the network, their location with respect to the fault and each other (proximity, distance, angle, etc. ), and the type of ground on which the station will be established (e.g., rocky) are determined. The second one is where stations in the GNSS network are measured. At this stage, at least three campaigns of GNSS measures are performed (usually one year apart) at stations in the network. In the third and final, the GNSS data are evaluated with suitable software (GAMIT/GLBOK, Bernese, Gipsy X, etc. ), and the location and velocity information of each station are obtained. By analyzing the velocity data that are obtained, the fault is modeled by using information that includes fault kinematics, slip rate, and strain rate, and an earthquake hazard analysis is performed with this information [2; 10]. All stages mentioned above are important for modeling tectonic movements. To realize a better model, the velocities of the stations in the GNSS network must be determined with high accuracy. When it is considered that the slip velocities of the faults are within an accuracy of a few mm/year, it can be seen that the velocity accuracies to be obtained should be lower than the fault slip velocities. If this is not the case, it will not be possible to determine the fault slip rates with the velocity data to be obtained. On the other hand, the velocity accuracies are directly related to the position accuracies as a result of the measurements in each campaign, regardless of the displacement. High position accuracy is required for high velocity accuracy. To achieve high position accuracy, various models are employed during the data evaluation phase [12]. High accuracy in positioning with the GNSS depends on factors, including measurement time, facility type, and the GNSS data evaluation strategy. In addition, the time elapsed between campaigns is also important in determining velocity accuracy [13]. Thus, it is important to model the position accuracies in each campaign and the elapsed time between campaigns in determining GNSS velocities in order to produce a higher level of accuracy in deformation and tectonic modeling studies with GNSS. It is possible to calculate the positions and velocities of continuous GNSS stations with high accuracy (provided sufficient data is available). This is because these stations do not include effects such as leveling error or antenna height measurement error, and they provide uninterrupted data. However, the installation of these stations is quite costly.