## 4 Discussion  
The results of this study reveal distinct correlations between surface displacement measurements and underlying shear deformations in tailings storage facilities (TSFs), supporting the effectiveness of geodetic monitoring in assessing dam stability. Our findings align with previous studies emphasizing the value of surface displacement as an indicator of subsurface stress dynamics in dam structures, specifically in the context of TSFs where stability is critical for environmental and operational safety. The use of geographically weighted regression (GWR) in this study enhances the spatial resolution of displacement analysis, allowing us to pinpoint areas of potential concern that might otherwise go undetected in standard, location-agnostic models. This location-specific approach not only corroborates existing literature on localized stress effects in TSF structures but also introduces a practical framework for continuous, targeted monitoring that may be more resource-efficient for dam operators. The broader implications of our findings suggest that by integrating high-resolution displacement monitoring, dam operators can establish proactive maintenance strategies, potentially averting costly or catastrophic failures. While the current study relied on surface measurements, future research should aim to incorporate complementary data such as pore water pressure and geotechnical layer properties to provide a fuller profile of TSF stability. Expanding on these methods to include remote sensing data, such as satellite-based interferometry, could enhance monitoring capabilities further and broaden applicability across various dam types and environments. In addition, exploring the integration of machine learning models with GWR could improve prediction accuracy, presenting a promising direction for enhancing TSF safety protocols. The main obstacle for using the described approach is data availability. Geodedical data are usually collected manually on an annual basis. In cases of dam failures, the dam movement may evolve on a  
Figure 8: Trend prediction for total displacement and derivatives: velocity and acceleration for two selected (**a,b**) benchmarks. much higher pace, thus continuous displacement data may be necessary. One of the ways to obtain such data is using of GNSS position sensors. A precise and real-time positioning solution is indispensable for various applications, including geotechnical monitoring of critical areas such as tailings storage facilities (TSFs). Global Navigation Satellite Systems (GNSSs) play a pivotal role in ensuring centimeter/millimeter-level accuracy for these applications. GNSSs encompass all global positioning technologies relying on satellite constellations, including GPS (USA), GLONASS (Russia), Galileo (Europe), BeiDou (China), IRNSS/NavIC (India), and QZSS (Japan). Each constellation comprises a network of approximately 25 satellites. These satellites operate within the frequency range of 1 GHz to 1.6 GHz, known as the L band. This frequency band is chosen because it exhibits relatively low interference characteristics from the Ionosphere and Troposphere, making it nearly transparent to their effects. Nevertheless, the Ionosphere and Troposphere remain significant sources of potential errors in GNSS accuracy. Signals are transmitted at a power of 44 W using antennas with a gain of 12 dBi. Each GNSS signal comprises three essential components: (1) a carrier signal, (2) an identifier, which serves for both satellite identification and precise timing measurement, and (3) the navigation message. This navigation message contains various critical information, including ephemerides, detailing the satellite's current position, and almanacs, which provide data about the positions of other satellites in the constellation. In the mining industry, most GNSS systems make use of concepts such as Differential GNSS (DGNSS) or Real-Time Kinematics (RTK).