The objective of this paper is to present the effort to establish the geoidal model for the state of São Paulo to be used for mapping purposes by the Instituto Geográfico e Cartográfico do Estado de São Paulo (IGC). This model is limited by 26ºS and 19ºS latitude and 54ºW and 44ºW longitude. The Stokes-Helmert geoid software (SHGeo) from the University of New Brunswick (UNB), Canada, was used. This is well established, tested scientific package for gravimetric geoid determination based on the Stokes-Helmert approach. The gravimetric geoid computation and result validation in the area was facilitated by the utilisation of the following data sets: terrestrial gravimetric data; XGM2019e geopotential model with degrees 100 and 360 to compute the long- and medium-wave components of the gravity anomaly; the UNB_TopoDensT_2v02 global laterally varying topographic density model developed by UNB; and SRTMv3 DEM data. The latter was used to compute topographic effects and downward continuation. The short-wavelength component was estimated using the Fast Fourier Transform (FFT) method, with the Kernel modification proposed by Vaníček and Kleusberg. The quasigeoid models were also calculated using the relationship between geoidal height and height anomaly. The validation of these two models was achieved through the utilisation of GNSS/levelling points (IBGE and IGC), with the geopotential model employed up to degree 100 and 360. In this analysis, the standard deviations of these models were 13 cm and 10 cm, respectively.
In order to eliminate the high-frequency signal errors in the Universal Time (UT1) series obtained by Very Long Baseline Interferometry (VLBI) and the International GNSS Monitoring and Assessment System (iGMAS), Vondrak filter is applied to smooth the observed UT1 series. The Vondrak filter algorithm achieves varying levels of precision improvement with different smoothing factors, this paper proposes a novel method to determine the smoothing factor based on characteristic values of smoothness and standard deviation. Experimental validation is conducted using observational data from VLBI to verify the proposed method. Experiments demonstrate that the determined smoothing factor significantly enhances UT1 precision, the measurement precision of the original VLBI UT1 series is 44.35 us, while after filtering it improved to 29.94 us—precision enhancement of approximately 33
This study jointly analyses the crustal deformation and the ionospheric behaviour associated with the 2004 Sumatra–Andaman earthquake (Mw 9.1–9.3) using a regional GNSS network of six continuously operating stations (SAMP, BAKO, NTUS, IISC, DGAR and PIMO). Kinematic Precise Point Positioning processed with CSRS-PPP is combined with PCA-based common-mode-error filtering, Z-score and CUSUM change-point testing, and geomagnetic screening using the Kp and Dst indices. The geodetic analysis resolves a dominant co-seismic horizontal displacement of 26 ± 2 cm in the west-southwest direction at SAMP, 306 km from the epicentre, decaying to millimetre level at the far-field sites (BAKO 0.4 cm, DGAR 0.8 cm, at or below the noise floor of the technique); these values agree with published regional tectonic models and validate the processing pipeline. The ionospheric analysis, based on the geometry-free L4 combination of all 102 station-days of dual-frequency observations between 15 and 31 December 2004, does not support the pre-seismic vertical-TEC enhancement that has been reported for 21 December 2004 in earlier work. On that date the daily VTEC anomaly is negative at five of the six stations and + 0.2 TECU at the sixth, the network-mean robust z-score is − 0.86, and no station exceeds the + 2.5σ detection threshold; the largest positive network-wide departures of the analysis window fall on 22 and 24 December and on the mainshock day itself, none of them reaching that threshold. The candidate precursor is therefore not confirmed. The result illustrates how strongly reported seismo-ionospheric precursors depend on the reference baseline, the bias handling and the elevation screening adopted, and it underlines the need for multi-station spatial-coherence testing and rigorous geomagnetic screening before any candidate anomaly is interpreted within the Lithosphere–Atmosphere–Ionosphere Coupling framework. Kinematic PPP resolves a 26 cm co-seismic offset at SAMP, matching published Sumatra–Andaman tectonic models. Reprocessing of all 102 station-days does not reproduce the reported 21 December 2004 pre-seismic VTEC enhancement; the network-mean anomaly on that date is negative. Multi-station coherence testing and geomagnetic screening are shown to be necessary conditions for evaluating candidate ionospheric precursors.
The ionosphere represents the Global Navigation Satellite System (GNSS) signals have the largest source of positioning and ranging errors from the ionosphere. NeQuick-G is a globally authorized ionospheric broadcast correction algorithm designed for users of the single-frequency Galileo satellite navigation system. In contrast, the Klobuchar ionospheric correction model includes eight ionospheric coefficients that correct GPS signals. However, the NeQuick-G model requires improvement to enhance positional accuracy for single-frequency GNSS users. This study explores the transformation between these two distinct ionospheric models, focusing on converting NeQuick-G coefficients into improved values using the XGBoost machine learning algorithm. The performance analysis of the proposed Improved NeQuick-G model has tested two significant geomagnetic storm events from solar cycle 25. The first event, occurring on May 10, 2024, mentioned as the Mother’s Day Event, is recognized as the most severe geomagnetic storm in the past twenty years. The second major storm was recorded on October 11, 2024. The coefficients of NeQuick-G and Klobuchar model data are analyzed from Doy of 104 to 287 in 2024. The study proposes an improved NeQuick-G model that incorporates dual-frequency Total Electron Content (TEC) data and XGBoost. In this paper, vertical TEC (VTEC) variations of two latitudinal GNSS stations are analyzed using the proposed model and the ionospheric broadcast models during the two geomagnetic storm events. This method trains the model coefficients, along with the residuals from both NeQuick-G and dual-frequency TEC. The improved machine learning model demonstrates mean absolute error (MAE) and root means square error (RMSE) values of 4.90 and 6.26 TECU at IISC during the May storm and 8.50 and 12.32 TECU at LCK4 during the October storm. The enhanced NeQuick model is expected to serve as a better ionospheric correction model, improving the accuracy of GNSS-based applications, particularly under space weather conditions.
Monitoring surface deformations is critical for disaster risk management, infrastructure safety, and the sustainable utilization of natural resources. This study analyzes deformations' temporal and spatial characteristics in an active mine site with a new methodology using Interferometric Synthetic Aperture Radar (InSAR) periodic deformation time series derived from Sentinel-1 satellite data. It is seen that linear modeling approaches of InSAR time series, which are widely used in the literature, are inadequate in representing multi-stage and complex deformation processes. Based on this deficiency, in the study, inflection points (sudden breaking moments in the deformation trend) where the curvature direction changes in the deformation time series are determined, the data is divided into two sub-periods, and separate curve fitting models are applied for each period. Separate curve-fitting models were subsequently applied to each segment. Additionally, spatial deformation trends were revealed by analyzing the time series of deformation velocity and areal density ratios generated by classifying deformation rates. The developed approach aims to contribute to understanding the evolutionary processes of deformations and enhancing early warning and site safety systems by representing deformation dynamics with higher accuracy than conventional models.
