Global Navigation Satellite System Data Research Articles

Complex fault system associated with the Molucca Sea Divergent double subduction zone revealed by the 2019 Mw 6.9 and Mw 7.1 Earthquakes

GNSS (Global Navigation Satellite System) data in northern Sulawesi and western Halmahera reveals a pattern of coseismic displacement that was caused by the 7 July 2019 (Mw 6.9) and 14 November 2019 (Mw 7.1) Molucca Sea earthquakes. The coseismic slip of these earthquakes are obtained via inversion on rectangular fault planes of surface GNSS coseismic deformation offsets. The 7 July 2019 earthquake ruptured on an east-dipping fault with a maximum slip of ∼35 cm located at ∼4 km depth and ∼ 100 km north-west of the epicenter. The 14 November 2019 earthquake also ruptured on an east-dipping fault, which has a maximum slip of ∼64 cm located at ∼22 km depth and ∼ 20 km south-west of the epicenter. The coseismic slip distribution of the 14 November earthquake is spatially aligned to that of an earthquake of similar magnitude that took place on 15 November 2014 in the same region. This observation points to the possibility of synchronization, thus raising the prospect of a future earthquake exceeding magnitude 7. If the stress was completely released during the 2014 event, it would have been necessary to reload that portion of the fault at a rate significantly larger than the observed convergence rate of ∼5.8 cm/yr. This suggests that partial ruptures are likely controlling the recurrence time of large earthquakes in the region.

The Real-Time Detection of Vertical Displacements by Low-Cost GNSS Receivers Using Precise Point Positioning.

Vertical displacements are traditionally measured with precise levelling, which is inherently time consuming. Rapid or even real-time height determination can be achieved by the Global Navigation Satellite System (GNSS). Nevertheless, the accuracy of real-time GNSS positioning is limited, and the deployment of a network of continuously operating GNSS receivers is not cost effective unless low-cost GNSS receivers are considered. In this study, we examined the use of geodetic-grade and low-cost GNSS receivers for static and real-time GNSS levelling, respectively. The results of static GNSS levelling were processed in four different software programs or services. The largest differences for ellipsoidal/normal heights reached 0.054 m/0.055 m, 0.046 m/0.047 m, and 0.058 m/0.058 m for points WRO1, BM_ROOF, and BM_CP, respectively. In addition, the values depended on the software used and the location of the point. However, the multistage experiment was designed to analyze various strategies for GNSS data processing and to define a method for detecting vertical displacement in a time series of receiver coordinates. The developed method combined time differentiation of coordinates estimated for a single GNSS receiver using the Precise Point Positioning (PPP) technique and Butterworth filtering. It demonstrated the capability of real-time detection of six out of eight displacements in the range between 20 and 55 mm at the three-sigma level. The study showed the potential of low-cost GNSS receivers for real-time displacement detection, thereby suggesting their applicability to structural health monitoring, positioning, or early warning systems.

Unlocking the potential of CYGNSS for pan-tropical inland water mapping through multi-source data and transformer

Cyclone Global Navigation Satellite System (CyGNSS) data are widely recognized for their sensitivity to inland water bodies. However, the detection of water bodies using single CyGNSS data is subject to uncertainties, presenting challenges for large-scale and accurate water system detection. In this study, we employ CyGNSS data for regression estimation to map inland water bodies. In comparison to previous studies, we incorporate additional constraints, including topographic factors, vegetation information, soil moisture, and latitude and longitude data. Leveraging the U-shaped structure, Swin Transformer, and ContextModule, we effectively extract water body distribution information, referred to as CFRT. Through rigorous performance comparison with prevalent deep learning models, our method demonstrates remarkable accuracy. The generated water percent exhibits notable consistency with the reference data, achieving a root mean square error (RMSE) of 7.15% and a mean intersection over union of 0.778 within the reachable area of the CyGNSS data. Our approach emphasizes the significance of utilizing multi-source data to substantially enhance the accuracy of CyGNSS water system detection.

Improvement of the Estimation of the Vertical Crustal Motion Rate at GNSS Campaign Stations Based on the Information of GNSS Reference Stations

With the enrichment of GNSS data and the improvement in data processing accuracy, GNSS technology has been widely applied in fields such as crustal deformation. The Crustal Movement Observation Network of China (CMONOC) has provided decades of Global Navigation Satellite System (GNSS) data and related data products for crustal deformation research on the Chinese mainland. The coordinate time series of continuously observed reference stations contain abundant information on crustal movements. In contrast, the coordinate time series of periodically observed campaign stations have limited data, making it difficult to separate or remove instantaneous non-tectonic movements from the time series, as performed with reference stations, to obtain a stable and reliable crustal movement velocity field. To address this issue, this paper proposes a method to improve the estimation of crustal movement velocity at campaign stations using the information of neighboring reference stations. This method constructs a Delaunay triangulation of reference stations and fits the periodic movement of each campaign station using an inverse distance weighted interpolation algorithm based on the reference station information. The crustal movement velocity of the campaign stations is then estimated after removing the periodic movement. This method was verified by its application to the estimation of the vertical motion rate at some reference and campaign stations in Yunnan Province. The results show that the accuracy of vertical motion rate estimation for virtual and real campaign stations improved by an average of 24.4% and 9.6%, respectively, demonstrating the effectiveness of the improved method, which can be applied to estimate crustal movement velocity at campaign stations in other areas.

Perspectives for Improvements on Real-Time GNSS Positioning with the Use of New Observables

The research aims to analyze Global Navigation Satellite System (GNSS) positioning solutions using the Precise Point Positioning (PPP) method in kinematic mode, in which the position is obtained epoch by epoch through data from only one GNSS receiver, precise ephemerides, and satellite clock corrections, both with high accuracy. In this context, the adopted methodology employs the open-source software RTKLIB, which, through its data post-processing capability, enables the evaluation of kinematic PPP performance by incorporating new observables such as L5 and E5a bands, crucial for enhancing positional accuracy and mitigating multipath effects. Additionally, for survey simulations, data from the UFPR and POVE stations of the Brazilian Network for Continuous Monitoring of GNSS (RBMC) were utilized. The selected stations are located in the Northern and Southern regions of Brazil. The GNSS data were processed with the same tracking duration for a 45-minute cold-start, aimed at observing solution convergence. Subsequently, the estimated outputs in RTKLIB were obtained based on station origins using coordinates provided by SIRGAS-CON. Moreover, by utilizing precise ephemerides data, it was possible to conclude that the addition of new observables for triple-frequency positioning led to an improvement in the accuracy of the obtained coordinates, particularly in the Vertical component. In this regard, the increase in accuracy in experiments using only triple-frequency data with a 15° elevation mask was approximately 25% at the UFPR station and about ~37% at the POVE station. Furthermore, it was observed that reducing the elevation mask from 15° to 10° had a positive impact on dual-frequency positioning at the UFPR station, resulting in gains of over 3% in the East component and around ~21% in the North component compared to values obtained with the 15° mask. Similarly, during triple-frequency positioning at the POVE station, gains exceedingly approximately 16% in the North component and around ~22% in the Vertical component were observed.

SLG-SLAM: An integrated SLAM framework to improve accuracy using semantic information, laser and GNSS data

Visual Simultaneous Localization and Mapping (V-SLAM) is pivotal for precise positioning and mapping. However, visual data from crowd-sourced datasets often contains deficiencies that may lead to positioning errors. Despite existing optimization techniques, current algorithms do not adequately adapt to varied data in vehicle driving scenarios. To address this gap, this study introduces a novel SLAM framework (SLG-SLAM). This framework refines trajectories by integrating semantic information, laser point cloud, and global navigation satellite system (GNSS) data into V-SLAM. Initial trajectory estimates are made after filtering out dynamic targets and are subsequently refined with matched laser point clouds, then corrected for scale and direction using GNSS. The efficacy of this approach is assessed using four public datasets and one self-collected dataset, showing significant enhancements across all datasets. The proposed method reduces the mean absolute trajectory error by 43.50% on the KITTI dataset and 14.91% on the MVE dataset compared to the baseline. Unlike the baseline, which fails on three other datasets, the proposed method successfully performs localization and mapping. Additionally, compared to three other single-source methods (DynaSLAM, MCL, MVSLAM), the proposed method consistently outperforms, demonstrating its superior adaptability and effectiveness.

Investigating Different Interpolation Methods for High-Accuracy VTEC Analysis in Ionospheric Research

The dynamic structure of the ionosphere and its changes play an important role in comprehending the natural cycle by linking earth sciences and space sciences. Ionosphere research includes a variety of fields like meteorology, radio wave reflection from the atmosphere, atmospheric anomaly detection, the impact on GNSS (Global Navigation Satellite Systems) signals, the exploration of earthquake precursors, and the formation of the northern lights. To gain further insight into this layer and to monitor variations in the total electron content (TEC), ionospheric maps are created using a variety of data sources, including satellite sensors, GNSS data, and ionosonde data. In these maps, data deficiencies are addressed by using interpolation methods. The objective of this study was to obtain high-accuracy VTEC (Vertical Total Electron Content) information to analyze TEC anomalies as precursors to earthquakes. We propose an innovative approach: employing alternative mathematical surfaces for VTEC calculations, leading to enhanced change analytical interpretation for anomaly detections. Within the scope of the application, the second-degree polynomial method, kriging (point and block model), the radial basis multiquadric, and the thin plate spline (TPS) methods were implemented as interpolation methods. During a 49-day period, the TEC values were computed at three different IGS stations, generating 1176 hourly grids for each interpolation model. As reference data, the ionospheric maps produced by the CODE (Center for Orbit Determination in Europe) Analysis Center were used. This study’s findings showed that, based on statistical values, the TPS model offered more accurate results than other methods. Additionally, it has been observed that the peak values in TEC calculations based on polynomial surfaces are eliminated in TPSs.

Gdcov2sinex: a Python conversion tool from GipsyX’s gdcov file to SINEX file

The Solution INdependent EXchange (SINEX) file format, an international standard for the exchange of information, is essential in the Global Navigation Satellite System (GNSS) processing strategies that integrate different software applications. GNSS data processing using Jet Propulsion Laboratory’s (JPL) GipsyX software, which employs the Precise Point Positioning (PPP) technique, generates positions and covariance matrices in gdcov files, a GipsyX file format specific for storing this information. GipsyX software provides a tool, named mkDailySinex.py, to convert from gdcov file to SINEX file, but it is designed specifically for creating daily JPL SINEX files and does not function properly for non-JPL GipsyX users. I have developed a Python 3 program, named gdcov2sinex.py, which solves this problem, enabling any user to perform the SINEX conversion and, therefore, apply the processing strategies that integrate GipsyX with other software, such as GAMIT/GLOBK. The new python tool presented herein takes advantage of the capabilities of mkDailySinex.py and provides all its default options, but also expands the number of selectable options, which can be useful to the user. The source code, user manual, and a sample dataset of gdcov2sinex.py are provided as electronic supplementary material of this paper.

Feasibility of Coseismic Landslide Prediction Based on GNSS Observations: A Case Study of the 2022 Ms 6.8 Luding, China, Earthquake

Abstract On 5 September 2022, an M 6.8 earthquake struck Luding County in Sichuan Province, China, triggering extensive landslides and causing severe damages. In this study, taking this event as an exemplary case study, we test the feasibility of fast earthquake-induced landslide prediction utilizing Global Navigation Satellite System (GNSS) observations. Particularly, we construct finite-slip models based on static offsets and 1 Hz displacement waveforms. Employing these slip models, physics-based simulation (PBS) is applied separately to obtain peak ground velocity (PGV). The PGVs are then integrated into landslide spatial distribution probability prediction based on the Deep Forest algorithm. Our results show that the predicted landslides probability distribution of fast inversion models using static and high-rate GNSS data align well with the landslide catalog. Furthermore, high-rate GNSS data can improve the model performance by providing the evolution information of rupture. In addition, we also derive PGV from the empirically regressed ground-motion prediction equations (GMPEs) and incorporate it into landslide prediction. The GMPEs exhibits an advantage in terms of prediction recall for landslides and a relatively reduced accuracy compared with the PBS. Despite the inherent uncertainties in this study, based on the case study of the 2022 M 6.8 Luding earthquake, we utilize GNSS data and present a set of methods for real-time landslide prediction. The achieved model performance is relatively satisfactory, considering the challenges and uncertainties involved.

Spatiotemporal Evolution of Slow Slip Events at the Offshore Hikurangi Subduction Zone in 2019 Using GNSS, InSAR, and Seafloor Geodetic Data

AbstractDetecting crustal deformation during transient deformation events at offshore subduction zones remains challenging. The spatiotemporal evolution of slow slip events (SSEs) on the offshore Hikurangi subduction zone, New Zealand, during February–July 2019, is revealed through a time‐dependent inversion of onshore and offshore geodetic data that also accounts for spatially varying elastic crustal properties. Our model is constrained by seafloor pressure time series (as a proxy for vertical seafloor deformation), onshore continuous Global Navigation Satellite System (GNSS) data, and Interferometric Synthetic Aperture Radar displacements. Large GNSS displacements onshore and uplift of the seafloor (10–33 mm) require peak slip during the event of 150 to >200 mm at 6–12 km depth offshore Hawkes Bay and Gisborne, comparable to maximum slip observed during previous seafloor pressure deployments at north Hikurangi. The onshore and offshore data reveal a complex evolution of the SSE, over a period of months. Seafloor pressure data indicates the slow slip may have persisted longer near the trench than suggested by onshore GNSS stations in both the Gisborne and Hawkes Bay regions. Seafloor pressure data also reveal up‐dip migration of SSE slip beneath Hawke Bay occurred over a period of a few weeks. The SSE source region appears to coincide with locations of the March 1947 Mw 7.0–7.1 tsunami earthquake offshore Gisborne and estimated great earthquake rupture sources from paleoseismic investigations offshore Hawkes Bay, suggesting that the shallow megathrust at north and central Hikurangi is capable of both seismic and aseismic rupture.

GIS and GNSS Integration Techniques for Earth Observations

Abstract: The term "GNSS," or global navigation satellite system, refers to any satellite navigation system with global coverage. To determine your current location, GNSS devices and receivers employ geolocation information from satellite navigation systems. Trilateration is the basis for how navigation satellites operate. An object's position on the spheroid is defined by its latitude, longitude, and altitude above mean sea level. Today, the utilisation of Global Navigation Satellite Systems (GNSS) technology is required for a wide range of uses, including engineering, cities, and defence. Geographic Information Systems (GIS) can be integrated with GPS to create powerful tools for mapping and spatial analysis. This integration allows for precise display of geographic characteristics on maps, land use planning, response to emergencies, and management of natural resources. Precision agriculture is one area where GNSS and GIS are integrated, enabling farmers to gather precise position data while operating machinery. GNSS and GIS integration can be done through position-focused and technology-focused integration. Position-focused integration uses GNSS technology to locate items or features on the earth's topography, while technology-focused integration enhances the utility and accuracy of GNSS data using GIS applications and technology. Both forms of integration provide potent solutions for various industries.

Interseismic deformation in the northwestern Sichuan-Yunnan block constrained by Sentinel-1 InSAR and GNSS

The northwestern Sichuan-Yunnan block (NW SYB), located in the southeastern Tibetan Plateau, is characterized by complex fault systems. Its detailed crustal deformation is crucial to comprehending the kinematics of the Tibetan expansion. In this study, we integrate the Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) data to obtain the high-resolution present-day deformation of the NW SYB, which provides insights into the strain partitioning, fault kinematics, and block motion characteristics in this region. Our results show that the elastic strain is not only built up around faults but also widely distributed in the off-fault areas. The Ganzi segment of the Ganzi-Yushu fault and the Luhuo segment of the Xianshuihe fault in the study area accommodate 5.31 mm/yr and 8.41 mm/yr left-lateral strike-slip motion, respectively. The small-scale faults show certain deformation, among which the left-lateral and right-lateral slip rates of the Litang and Zhongdian faults are 2.53 mm/yr and 1.97 mm/yr, respectively. The spatial patterns of the strain partitioning and block motion show that the active strike-slip faults accommodate displacements from Cenozoic block extrusion and rotation, which partially coordinate the kinematic discrepancy of the Tibetan expansion. Our geodetic measurements and existing structural observations indicate that the southeastward expansion of the Tibetan Plateau is absorbed mainly by E-W shortening and N-S extension in the NW SYB, may accommodated by continental strike-slip faults in the upper crust and distributed shear in the lower crust.

Real-time cloud computing of GNSS measurements from smartphones and mobile devices for enhanced positioning and navigation

In recent years, Global Navigation Satellite Systems (GNSSs) have become integral to our daily lives because of their precise positioning and navigation capabilities. Widespread use of smartphones equipped with GNSS receivers results in the generation of a huge amount of positioning data. Therefore, real-time cloud computing has emerged as a promising approach to effectively leverage this wealth of location information. In this study, we developed an Android app that captures raw GNSS data from smartphones, leverages cloud computing resources, calculates the position of the device, and returns the computed solution to the user. Integration of cloud-based processing not only conserves the device resources but also enables real-time position calculation, paving the way for enhanced location-based applications and services.

Gnssrefl: an open source software package in python for GNSS interferometric reflectometry applications

An open source software package has been developed for Global Navigation Satellite Systems (GNSS) interferometric reflectometry. The gnssrefl package is written in python; it can be installed from the source code, the python packaging index website, or via a docker. It includes modules that download GNSS data and orbit data from global archives. A periodogram is used to retrieve the height of the GNSS antenna over the reflecting surface using signal to noise ratio data. Signals from the Global Positioning System, Glonass, Galileo, and Beidou constellations are supported. Modules are provided to estimate volumetric water content of soil, snow depth/accumulation, and water level. Utilities for mapping and assessing reflection zones and determining the maximum resolvable height are available.

Atmospheric density estimation in very low Earth orbit based on nanosatellite measurement data using machine learning

The atmospheric density in the very low Earth orbit (VLEO), the lower region of the thermosphere is a critical factor for satellite missions on prediction of orbit and lifetime. However, accurately determining atmospheric density remains challenging owing to difficulties in acquiring data within the VLEO. The re-Entry satellite with Gossamer aeroshell and GPS/Iridium (EGG) nanosatellite mission, conducted in 2017, obtained sparse coordinate data through the iridium network and Global Navigation Satellite System (GNSS). Using these data, we developed a methodology based on machine learning and special perturbations to estimate atmospheric density. The originally obtained atmospheric density predicted by NRLMSISE-00 was corrected during trajectory reproduction from GNSS data by special perturbation. The prediction performance of the method was validated using the generated trajectory data. Subsequently, we estimated the density at altitudes of approximately 200 km, particularly in the lower region of the VLEO. Density values that accurately reproduced trajectories of the EGG were 0.5 to 0.64 times greater than that predicted by the NRLMSISE-00 model. This methodology is effective for measuring atmospheric density in the lower VLEO region using GNSS data from low-cost and high-frequency nanosatellite missions. By increasing the availability of accurate atmospheric density data, this methodology enhances our understanding of the atmosphere in the VLEO.

Multi-Wave Structures of Traveling Ionospheric Disturbances Associated with the 2022 Tonga Volcanic Eruptions in the New Zealand and Australia Regions

Using dense global navigation satellite system data and brightness temperature data across the New Zealand and Australia regions, we tracked the propagation of traveling ionospheric disturbances (TIDs) associated with the 15 January 2022 Tonga volcanic eruptions. We identified two shock wave-related TIDs and two Lamb wave-related TIDs following the eruptions. The two shock wave-related TIDs, propagating with velocities of 724–750 and 445–471 m/s, respectively, were observed around New Zealand and Australia within a distance of 3500–6500 km from the eruptive center. These shock wave-related TIDs suffered severe attenuation during the propagation and disappeared more than 6500 km from the eruptive center. Based on the TEC data from the nearest ground-based receivers, we estimated the onset times of two main volcanic explosions at 04:20:54 UT ± 116 s and 04:24:37 UT ± 141 s, respectively. The two shock wave-related TIDs were most likely generated by these two main volcanic eruptions. The two Lamb wave-related TIDs propagated with velocities of 300–370 and 250 m/s in the near-field region. The Lamb wave-related TIDs experienced minimal attenuation during their long-distance propagation, with only a 0.17% decrease observed in the relative amplitudes of the Lamb wave-related TIDs from the near-field to far-field regions.

Adaptive Multi-Sensor Fusion Localization Method Based on Filtering

High-precision positioning is a fundamental requirement for autonomous vehicles. However, the accuracy of single-sensor positioning technology can be compromised in complex scenarios due to inherent limitations. To address this issue, we propose an adaptive multi-sensor fusion localization method based on the error-state Kalman filter. By incorporating a tightly coupled laser inertial odometer that utilizes the Normal Distribution Transform (NDT), we constructed a multi-level fuzzy evaluation model for posture transformation states. This model assesses the reliability of Global Navigation Satellite System (GNSS) data and the laser inertial odometer when GNSS signals are disrupted, prioritizing data with higher reliability for posture updates. Real vehicle tests demonstrate that our proposed positioning method satisfactorily meets the positioning accuracy and robustness requirements for autonomous driving vehicles in complex environments.

A hybrid information fusion method for SINS/GNSS integrated navigation system utilizing GRU-aided AKF during GNSS outages

Abstract To enhance the performance of integrated inertial navigation system (INS) and global navigation satellite system (GNSS) during GNSS outages, this paper proposed a fusion positioning method based on predictive observation information and adaptive filter parameter. Combined with an adaptive Kalman filter (AKF) and a Gated Recurrent Unit neural network (NN) that directly relates the inertial measurement unit (IMU) output sequence to the error estimation, the hybrid information fusion system can provide effective corrections to compensate for horizontal position errors under the constraints of complex and dynamic vehicle movement data during GNSS outages. Meanwhile, the designed adaptive parameter of the integrated navigation filter can adjust the credibility of the state prediction section when the GNSS is reconnected, ensuring the system can switch rapidly between the INS/GNSS and INS/NN integrated modes. The performance of the proposed information fusion method has been experimentally validated using IMU and GNSS data collected in a vehicle navigation test conducted on a stretch of expressway. The comparison results indicate that the proposed algorithm has error suppression capabilities under various experimental constraints and demonstrates a degree of extendibility and reusability.

Impact of the 2008 MW\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_W$$\\end{document} 7.9 Great Wenchuan earthquake on South China microplate motion

Tectonic plate motions drive the earthquake cycle, as they result in the slow accrual and sudden release of energy along plate boundaries. Steadiness of plate motions over the earthquake cycle is a central tenet of the plate tectonics theory and has long been a main pillar in models of earthquake genesis, or of plate-margins seismic potential inferred from slip-deficit estimates. The advent of geodesy in the geosciences and the availability of multi-year-long series of position measurements permit tracking the motions of tectonic plates from before to after the time of significant seismic events that occur along their margins. Here, we present evidence that large earthquakes are capable of modifying the motions of entire microplates. We use high precision Global Navigation Satellite System (GNSS) position time-series covering the periods 2001–2004 and 2014–2017 to demonstrate that, contrary to the tenet above, the South China microplate motion changed after the 2008 MW\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_W$$\\end{document} 7.9 Great Wenchuan earthquake. The GNSS data and associated uncertainties indicate a plate motion slowdown of up to 20% that is beyond the possible impact of data noise and is thus tectonically meaningful. We use quantitative models of torque balance to show that generating this kinematic change requires a force upon the South China microplate compatible with that imparted by the Great Wenchuan earthquake of 2008. The existence of a kinematic signal linked to the earthquake cycle that impacts an entire microplate might offer an additional, novel perspective to assessing the hazards of earthquake-prone tectonic regions.

Analysis of Urbanization-Induced Land Subsidence in the City of Recife (Brazil) Using Persistent Scatterer SAR Interferometry

The article addresses anthropogenic and geological conditions related to the development of soil subsidence in the western zone of Recife (Brazil). Over the past 50 years, human activity has intensified in areas previously affected by soft soils (clay, silt, and sandstone) resulting in subsidence due to additional loads (landfills and constructions). The duration of the settlement process can be significantly influenced by the specific characteristics of the soil composition and geological conditions of the location. This work presents, for the first time, accurate InSAR time series maps that describe the spatial pattern and temporal evolution of the settlement, as well as the correlation with the geological profile, and validation with Global Navigation Satellite System (GNSS) data. Persistent Scatterer Interferometry (PS-InSAR) was employed in the analysis of Single Look Complex (SLC) images generated by 100 ascending COSMO-SkyMed (CSK) and 65 PAZ (32 ascending, and 33 descending) from the X-band, along with 135 descending Sentinel-1 (S1) acquisitions from the C-band. These data were acquired over the period from 2011 to 2023. The occurrence of subsidence was identified in several locations within the western region, with the most significant displacement rates observed in the northern, central, and southern areas. In the northern region, the displacement rates were estimated to be approximately −20 mm/year, with the Várzea and Caxangá neighborhoods exhibiting the highest rates. In the central region, the displacement rates were estimated to be approximately −15 mm/year, with the Engenho do Meio, Cordeiro, Torrões, and San Martin neighborhoods exhibiting the highest rates. Finally, in the southern region, the displacement rates were estimated to be up to −25 mm/year, with the Caçote, Ibura, and Ipsep neighborhoods exhibiting the highest rates. Additionally, east–west movements were observed, with velocities reaching up to −7 mm/year toward the west. These movements are related to the lowering of the land. The study highlights that anthropogenic effects in the western zone of Recife contribute to the region’s vulnerability to soil subsidence.