Every man-made structure creates certain risks — dams are no exception. Most failures in man-made structures that have occurred could have been avoided if the structures’ behaviour had been inspected, monitored, and analyzed continuously, and if proper corrective measures had been taken in a timely fashion. The DSI (The General Directorate of State Hydraulic Works), which is the institution responsible for dam safety, has long used surveying methods to measure the displacements of geodetic points as a part of dam monitoring policy. In this study, we focus on the dam's mechanical behaviour throughout a time period of more than 10 years. These study results have been derived from a separate, ongoing project that has monitored deformation on the Ataturk Dam and is now determining the water level of the reservoir. The project results show that although the dam body has become more stable and the water load behind the dam has increased, the rate of displacement of the dam has declined significantly. From these results, it can be seen that the reservoir water level can be increased evenly over time and that 542 m is the maximum water level of the dam's reservoir.

This study addresses the potential of machine learning (ML) algorithms in geophysical and geodetic research, particularly for enhancing GNSS time series analysis. We employed XGBoost and Long Short-Term Memory (LSTM) networks to analyze GNSS time series data from the tectonically active Anatolian region. The primary objective was to detect discontinuities associated with seismic events. Using over 13 years of daily data from 15 GNSS stations, our analysis was conducted in two main steps. First, we characterized the signals by identifying linear trends and seasonal variations, achieving $R^2$ values of 0.84 for the XGBoost v.2.1.0 model and 0.81 for the LSTM model. Next, we focused on the residual signals, which are primarily related to tectonic movements. We applied various threshold values and tested different hyperparameters to identify the best-fitting models. We designed a confusion matrix to evaluate and classify the performance of our models. Both XGBoost and LSTM demonstrated robust performance, with XGBoost showing higher true positive rates, indicating its superior ability to detect precise discontinuities. Conversely, LSTM exhibited a lower false positive rate, highlighting its precision in minimizing false alarms. Our findings indicate that the best fitting models for both methods are capable of detecting seismic events (Mw ≥ 4.0) with approximately 85% precision.

Triple junctions involving convergent plate boundaries extend beyond local implications, which is crucial for studying the geology of convergent plate boundary zones. However, kinematic models overlook Cyprus-Anatolia motion due to limited geodetic constraints. Our study area comprises Cyprus, southern Turkey, and the Levant coast, focusing on the Kahramanmaraş triple junction, where a destructive earthquake sequence occurred on February 6, 2023. We present precise positioning data merged with published velocities, constructing an up-to-date velocity field for the interseismic period. Employing two kinematic approaches, we analyze its tectonic implications. In Cyprus, we find the relative motion of Africa (Sinai Plate) and Anatolia is partitioned between convergence in the Cyprus subduction, with a rate of 3.5–6.2 mm/yr, progressively decreasing from west to east and left-lateral transpressive Kyrenia fault, situated along the northern coast of Cyprus, with rate 3.3–4.2 mm/yr. The relative strike-slip motion between Arabia and Anatolia is partitioned between the East Anatolian Fault (slip rates 5.2–6.2 mm/yr) and some secondary faults such as Çardak and Malatya faults (slip rates 2.0–1.7 mm/yr respectively) and causes distributed deformation for a 50–60 km wide region. The largest second invariant strain rate tensors from the continuum kinematic model also coincide with the same region, the East Anatolian shear zone. A shear partitioning system exists around the Kahramanmaraş triple junction, from Cyprus to southeast Turkey. The Levant Fault has a 3.5–4.7 mm/yr left-lateral slip rate, decreasing northward as part of it is transferred to offshore faults. Strain rates appear relatively small in the Taurus range and Adana/Cilicia basin, transitioning from extensional/transtensional to compressional from east to west.

GNSSpy is a free and open source library for handling multi-GNSS and different versions (2.x and 3.x) of RINEX files. It performs basic Precise Point Positioning (PPP) algorithms to calculate coordinates of the GNSS stations precisely. Therefore, the Multi-GNSS Experiment (MGEX) products are used within the package. It also calculates the ionospheric delay by using International GNSS Service (IGS) atmospheric models for single frequency receivers and minimize its effect by combining frequencies for the receivers have at least dual frequency. GNSSpy can be used for editing (slicing, decimating, merging) and quality checking for RINEX files. It can be successfully used for visualizing GNSS data such as skyplot, azimuth-elevation, time-elevation, ground track and visibility plot. Additionally, this library can be used for basic geodetic computations such as geodetic positions on reference ellipsoid and projection computations. GNSSpy can be downloaded from the GitHub repositories (https://github.com/GNSSpy-Project/gnsspy). The toolkit is still being improved by the authors.

This study evaluates the performance of multi-constellation GNSS Precise Point Positioning (PPP) for offshore kinematic observations in the challenging environment of Antarctica using the data collected on a moving ship during the 6th Turkish Antarctic Expedition in 2022. The analysis involved two open-source PPP software solutions, PRIDE PPP-AR, and Ginan, which were used to process GNSS data from both GPS-only and multi-GNSS (GPS, GLONASS, Galileo, BeiDou) constellations. The results indicate that PRIDE PPP-AR generally provided better horizontal and vertical positional accuracy than Ginan software, achieving improvements of up to approximately 33% in height component root mean square error (RMSE) for both constellation solutions. While the mean value of horizontal position differences for PRIDE PPP-AR in multi-GNSS solution was 4 cm, while 5 cm for Ginan. The anticipated advantages of multi-GNSS over GPS-only solutions were not consistently observed for Ginan software in 2D position and height components. However, PRIDE PPP-AR demonstrated improved internal consistency with multi-GNSS solution, achieving an RMSE value of 5 cm for horizontal positioning compared to 6 cm for Ginan. The general results of the study reveal that the PPP technique, which does not require any reference station’s GNSS data, can achieve almost the same accuracy as the differential positioning technique. With this superiority, the PPP technique is an ideal positioning technique, especially in remote marine environments and polar regions, where geodetic infrastructure is inadequate and environmental conditions are difficult