GNSS Data Research Articles

3D deformation analysis for earth dam monitoring based on terrestrial laser scanning (TLS) and the iterative closest point (ICP) algorithm

Introduction: The conventional 3-D point cloud-based deformation analysis methods, such as the shortest distance (SD), cloud-to-cloud (C2C), and multiscale model-to-model cloud comparison (M3C2), essentially regard the closest distance between two periods of point cloud data as the deformation, rather than the true position of the same point in 3-D space before and after deformation.Methods: This paper proposes a method based on the ICP algorithm to calculate the differences between the chunked multi-period point clouds to recognizes the 3-D deformations.Results and discussion: The results show that the obtained results are very close to the GNSS data but with a much larger spatial monitoring range. The accuracy is higher than that of the SD method. Moreover, we analyze the statistical relationship between the point cloud block size and the deformation vector error and determine the optimal block size. The aim of this article is to optimize the deformation analysis method and improve its accuracy to provide techniques and ideas for the wider surface deformation monitoring research field. For instance, combining this method with data from contact methods constructs a 3D overall deformation model of the mountain, enabling real-time monitoring and early warning of debris flows.

VR map construction for orchard robot teleoperation based on dual-source positioning and sparse point cloud segmentation

The unstructured nature of orchard environments presents significant challenges for autonomous navigation of orchard robots. Teleoperation, combined with virtual reality (VR), has emerged as a promising solution to overcome the limitations of on-board autonomous navigation capabilities in general-purpose robots. However, constructing accurate and semantically meaningful VR maps for orchard environments remains a challenge. This study proposes a novel VR map construction framework for orchard robot teleoperation visualization. It comprises two key components: dual-source combined positioning and semantic segmentation based on sparse point clouds. First, a novel sliding window-based data fusion approach is proposed to address the positioning challenges in semi-obstructed orchard environments. This approach combines GNSS data and laser matching to achieve robust and accurate positioning for orchard robots. Second, the framework capitalizes on the installation characteristics of mobile robot radars to achieve real-time classification of sparse point clouds. This allows for the removal of unstable elements, such as leaves, which would hinder effective teleoperation. By integrating these components, a VR map suitable for VR teleoperation of orchard robots is achieved. To validate the effectiveness of the proposed method, a VR teleoperation platform was constructed. The experimental results demonstrate that the method successfully fulfills the fundamental requirements for map visualization during VR remote operation of orchard robots. Furthermore, this study provides a valuable reference for the application of digital twins in agricultural robotics.

$$C/{N}_{0}$$ estimation based on acquisition correlation ratio for short GNSS data

$$C/{N}_{0}$$ estimation based on acquisition correlation ratio for short GNSS data

Exploring groundwater depletion and land subsidence dynamics in Taiwan’s Choushui river alluvial fan: insights from integrated GNSS and hydrogeological data analysis

The Choushui River Alluvial Fan (CRAF) is a major agricultural area in Taiwan with heavy groundwater usage. The extraction of groundwater here has caused land subsidence, which is now a significant global environmental issue. This study analyzes land subsidence in the CRAF by integrating hydrogeological data from 233 groundwater monitoring stations across four aquifers (CRAF Groundwater_NET) and data from 50 continuous GNSS stations (CRAF GNSS_NET). We developed an automated processing flow for GNSS static surveying within CRAF GNSS_NET, and further employed a time-series fitting method to examine the long-term trends and annual changes for both GNSS and groundwater level data. Our analysis of the time-series data from the past decade identifies areas of significant groundwater level depletion and subsidence hotspots. We explore the relationship between groundwater level variations and surface displacements within CRAF, utilizing GNSS data to analyze horizontal and vertical displacement trends, as well as annual changes. We integrate these findings with hydrogeological data to understand regional subsidence patterns. Our results indicate that CRAF is characterized by distinct hydrogeological features. The study finds that the amplitudes of annual changes in both groundwater level and vertical displacement generally increase from northeast to southwest in the analyzed region. One particular area shows significant groundwater level decline, with the most severe rate recorded at 0.54 m/year. Similarly, GNSS analysis indicates pronounced subsidence trends in the same area, with rates ranging from 4.2 to 5.2 cm/year. These findings highlight the critical need for the development of effective groundwater management strategies to ensure sustainable use of groundwater resources and to implement mitigation measures against land subsidence in similar multiple-aquifer settings.

Динамика атмосферного влагосодержания по ГНСС-наблюдениям над территорией Восточно-Европейской платформы

GNSS data from the base maintained at the Pulkovo Observatory (VDB) and a similar base from the Nevada Geodetic Laboratory (NGL, USA) were used for research of the long-term dynamics of atmospheric precipitable water vapor over the territory of the East European Craton. The atmospheric precipitable water vapor is 0.03 mm/year.

LEO enhanced GNSS (LeGNSS) precise point positioning with emphasis on model comparison

With the rapid construction of the low-earth-orbit (LEO) constellation, LEO Enhanced Global Navigation Satellite System (LeGNSS) has become a hotspot in the research of GNSS applications and services, especially for fast convergence of precise point positioning (PPP). In this study, we analyze the impact of LEO satellites on the spatial geometry based on simulated LEO and real GNSS data, and evaluate the LeGNSS PPP performance with ionosphere-free (IF), undifferenced and uncombined (UDUC), and ionosphere-weighted (IW) models. Eight Multi-GNSS Experiment (MGEX) stations are selected for static and kinematic experiments. Results show that Position Dilution of Precision (PDOP) values of LeGNSS are about 0.5–1.2, which are improved by 38 % and 22 % compared with GPS-only and combined GPS, Galileo, and BDS (G\\E\\C) solutions. With LEO satellites, the positioning accuracy in east (E), north (N), and up (U) directions are improved by about 50 %, 30 %, and 50 % for the static mode, and about 35 %, 30 %, and 67 % for the kinematic mode. Moreover, the positioning accuracies of the IF, UDUC, and IW models are comparable. For convergence time, the static and kinematic results with LEO satellites are about 1.1 and 1.3 min, respectively, while those without LEO satellites are longer than 10.0 min. In addition, the model with inter-system bias (ISB) constraints can reduce the convergence time by within 1.0 min.

Tectonic deformation in El Salvador from combined InSAR and GNSS data

The country of El Salvador, located at the convergent boundary of the Cocos and Caribbean plates, is subject to frequent seismic events and complex surface deformation. This paper presents the first Interferometric Synthetic Aperture Radar (InSAR)-based velocity field of El Salvador produced by combining InSAR and new GNSS data, which allows us to gain a deeper understanding of the intricate crustal deformation processes in the region. We use L-Band ALOS PALSAR images from five ascending and two descending tracks, as well as 171 continuous and episodic GNSS stations across El Salvador and the Caribbean. The inversion of GNSS and InSAR velocities along fault-perpendicular profiles enable us to assess the accommodation of tectonic deformation along the El Salvador Fault Zone (ESFZ), providing a better constraint on the behaviour of its main structures. Additionally, we are able to better understand how the deformation is transmitted at the western (Jalpatagua-Ipala) and eastern (Fonseca Gulf) terminations of the ESFZ.

Sea Surface Height Measurements Based on Multi-Antenna GNSS Buoys.

Sea level monitoring is an essential foundational project for studying global climate change and the rise in sea levels. Satellite radar altimeters, which can sometimes provide inaccurate sea surface height data near the coast, are affected by both the instrument itself and geophysical factors. Buoys equipped with GNSS receivers offer a relatively flexible deployment at sea, allowing for long-term, high-precision measurements of sea surface heights. When operating at sea, GNSS buoys undergo complex movements with multiple degrees of freedom. Attitude measurements are a crucial source of information for understanding the motion state of the buoy at sea, which is related to the buoy's stability and reliability during its development. In this study, we designed and deployed a four-antenna GNSS buoy with both position and attitude measurement capabilities near Jimiya Wharf in Qingdao, China, to conduct offshore sea surface monitoring activities. The GNSS data were processed using the Precise Point Positioning (PPK) method to obtain a time series of sea surface heights, and the accuracy was evaluated using synchronous observation data from a small sea surface height radar. The difference between the GNSS buoy and the full-time radar was calculated, resulting in a root-mean-square error (RMSE) of 1.15 cm. Concurrently, the attitude of the GNSS buoy was calculated using multi-antenna technology, and the vertical elevation of the GNSS buoy antenna was corrected using the obtained attitude data. The RMSE between the corrected GNSS buoy data and the high ground radar was 1.12 cm, indicating that the four-antenna GNSS buoy can not only acquire high-precision coastal sea level data but also achieve synchronous measurement of the buoy's attitude. Furthermore, the data accuracy was also improved after the sea level attitude correction.

Past and Future Impacts of the Relative Sea Level Rise on the Seafront of Ancient Delos (Cyclades, Greece) and Flooding Scenarios by 2150

Sea level rise due to global warming is a continuing and, disappointingly, accelerating process which has already affected and will further impact coastal lowlands and the social and economic activities in these areas. Delos Island, situated in the middle of the Cyclades in the Aegean Sea, was considered the most sacred of all islands in ancient Greek culture and was a trading hub for the entire eastern Mediterranean. Uninhabited since the 7th century AD, and consistently the focus of research and touristic attention, the island is designated as an archaeological site and inscribed on the UNESCO World Heritage List. Previous studies on the relative sea level (rsl) changes suggest a steadily rising rsl during the last 6300 years, starting from a sea level of −4.80 ± 0.20 m in the Late Neolithic. The seafront of the ancient city of Delos is subject to the effects of rsl rise, which have caused significant coastline retreat and exposure to the northerly winds and waves, whereas parts of the coastal lowland, where the remains of the ancient city lie, are inundated, forming extended wetlands. The future impacts of rsl rise on the seafront of ancient Delos are illustrated on very-high-resolution digital surface models, evaluating both the flooding risk under different climatic projections, as provided by the IPCC AR6 report, and the ongoing land subsidence, as recorded by GNSS data. An rsl rise ranging from 87 cm (SSP1-2.6 scenario) to 148 cm (SSP5-8.5 scenario) is anticipated by 2150, requiring both resilience strategies and adaptation solutions as well as mitigation policies to cope with the effects of climate change.

An InSAR‐GNSS Velocity Field for Iran

AbstractWe present average ground‐surface velocities and strain rates for the 1.7 million km2 area of Iran, from the joint inversion of InSAR‐derived displacements and GNSS data. We generate interferograms from 7 years of Sentinel‐1 radar acquisitions, correct for tropospheric noise using the GACOS system, estimate average velocities using LiCSBAS time‐series analysis, tie this into a Eurasia‐fixed reference frame, and perform a decomposition to estimate East and Vertical velocities at 500 m spacing. Our InSAR‐GNSS velocity fields reveal predominantly diffuse crustal deformation, with localized interseismic strain accumulation along the North Tabriz, Main Kopet Dagh, Main Recent, Sharoud, and Doruneh faults. We observe signals associated with recent groundwater subsidence, co‐ and postseismic deformation, active salt diaprism, and sediment motion. We derive high‐resolution strain rate estimates on a country‐ and fault‐scale, and discuss the difficulties of mapping diffuse strain rates in areas with abundant non‐tectonic and anthropogenic signals.

GREAT: A scientific software platform for satellite geodesy and multi-source fusion navigation

The software platform named GNSS + REsearch, Application and Teaching (GREAT) has been designed and developed by Wuhan University for satellite geodesy and multi-source fusion navigation. GREAT is a comprehensive software platform based on C++ language, with modular structure, cross-platform capabilities and efficient performance. The software mainly focuses on space geodetic data processing, precise positioning and orbit determination, and multi-source fusion navigation. With the integration of multiple space geodetic techniques at observation level, GREAT can achieve the determination of global geodetic parameters. Meanwhile, GREAT can also achieve real-time precise orbit and clock determination by processing GNSS data from globally distributed stations, and the accuracies are about 3–6 cm for orbit and 0.1–0.4 ns for clock. Based on high-precision orbit and clock products, centimeter-level onboard positioning accuracies can be achieved with PPP-RTK in open-sky scenarios. Furthermore, centimeter-level multi-source fusion navigation can be achieved in urban scenarios though the integrated system of PPP-RTK/MEMS/VIS/LiDAR. Additionally, efficiency optimization algorithms such as parallel computing are applied to enable the software to handle the data-intensive and calculation-intensive situations.

Using Activity Measures and GNSS Data from a Virtual Fencing System to Assess Habitat Preference and Habitat Utilisation Patterns in Cattle.

There has been an increased focus on new technologies to monitor habitat use and behaviour of cattle to develop a more sustainable livestock grazing system without compromising animal welfare. One of the currently used methods for monitoring cattle behaviour is tri-axial accelerometer data from systems such as virtual fencing technology or bespoke monitoring technology. Collection and transmission of high-frequency accelerometer and GNSS data is a major energy cost, and quickly drains the battery in contemporary virtual fencing systems, making it unsuitable for long-term monitoring. In this paper, we explore the possibility of determining habitat preference and habitat utilisation patterns in cattle using low-frequency activity and location data. We achieve this by (1) calculating habitat selection ratios, (2) determining daily activity patterns, and (3) based on those, inferring grazing and resting sites in a group of cattle wearing virtual fencing collars in a coastal setting with grey, wooded, and decalcified dunes, humid dune slacks, and salt meadows. We found that GNSS data, and a measure of activity, combined with accurate mapping of habitats can be an effective tool in assessing habitat preference. The animals preferred salt meadows over the other habitats, with wooded dunes and humid dune slacks being the least preferred. We were able to identify daily patterns in activity. By comparing general trends in activity levels to the existing literature, and using a Gaussian mixture model, it was possible to infer resting and grazing behaviour in the different habitats. According to our inference of behaviour the herd predominantly used the salt meadows for resting and ruminating. The approach used in this study allowed us to use GNSS location data and activity data and combine it with accurate habitat mapping to assess habitat preference and habitat utilisation patterns, which can be an important tool for guiding management decisions.

The 2021 La Palma (Canary Islands) Eruption Ending Forecast Through Magma Pressure Drop

AbstractForecast of eruptive activity is a core challenge in volcanology. Here, we present an actual forecast for the end of the 2021 La Palma eruption. Using continuous GNSS data, we identified a co‐eruptive quasi‐exponential deflation trend. Assuming mass conservation, magma upflow from an overpressurized reservoir drives the eruptive process. The forecast was carried out during the eruption, however there was uncertainty in the key percentage of drop in driving pressure necessary to stop this eruption. In hindcast, we explore how forecast uncertainty reduces with increase in ingested near‐real time data. We conclude that precise forecasts could have been possible, but only after twice a characteristic exponential decay time‐scale, providing error estimates of 45% of the actual duration. We verify the mass conservation assumption using eruptive material volumes and propose that the eruption dynamics was controlled by a main reservoir at a depth close to Moho discontinuity beneath Cumbre Vieja volcano.

Crowdsourcing RTK: a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution (AR) in precise positioning. However, traditional real-time precise positioning frameworks (i.e., NRTK and PPP-RTK) depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network. This results in limited public appeal. With the mass production of autonomous driving vehicles, more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps. In this study, we propose a new GNSS positioning framework that relies on dual base stations, massive vehicle GNSS data, and crowdsourced atmospheric delay correction maps (CAM). The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way. Specifically, the map consists of between-station single-differenced ionospheric and tropospheric delays. We introduce the whole framework of CAM initialization for individual vehicles, on-cloud CAM maintenance, and CAM-augmented user-end positioning. The map data are collected and preprocessed in vehicles. Then, the crowdsourced data are uploaded to a cloud server. The massive data from multiple vehicles are merged in the cloud to update the CAM in time. Finally, the CAM will augment the user positioning performance. This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other. We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales. We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.

Immature characteristics of the East Anatolian Fault Zone from SAR, GNSS and strong motion data of the 2023 Türkiye–Syria earthquake doublet

On February 6, 2023, an Mw 7.9 earthquake occurred in the western section of the East Anatolia Fault Zone (EAFZ). It was subsequently followed by an Mw 7.7 earthquake on the northern branch of the EAFZ, known as the Sürgü Fault Zone. Coseismic deformation fields were derived for these earthquakes using joint evaluation of near-field strong motion data, Global Navigation Satellite System data, and Synthetic Aperture Radar datasets. The coseismic slip distribution model was determined through the joint kinematic finite fault inversion. The Mw 7.9 earthquake was a left-lateral strike-slip event, predominantly occurring at depths up to 20 km. The earthquake displayed three distinct asperities that correlate well with bends and stepovers along the EAFZ. The Mw 7.7 earthquake also exhibited left-lateral strike-slip characteristics, with a major asperity along the Çardak Fault featuring a maximum slip of approximately 9.5 m at depths between 0 and 24 km. The occurrence of this unanticipated large Mw 7.9 catastrophic seismic event on a fault with low-intermediate structural maturity is noteworthy. In the vicinity of immature faults with multiple jogs, stress tends to accumulate at barrier locations. When the accumulated stress near several adjacent barriers reaches a certain threshold, it may result in the transformation of multiple barriers into asperities, triggering cascading ruptures.

Observed meltwater-induced flexure and fracture at a doline on George VI Ice Shelf, Antarctica

Abstract Global Navigation Satellite System (GNSS) observations and ground-based timelapse photography obtained over the record-high 2019/2020 melt season are combined to characterise the flexure and fracture behaviour of a previously formed doline on George VI Ice Shelf, Antarctica. The GNSS timeseries shows a downward vertical displacement of the doline centre with respect to the doline rim of ~60 cm in response to loading from a central meltwater lake. The GNSS data also show a tens-of-days episode of rapid-onset, exponentially decaying horizontal displacement, where the horizontal distance between the doline rim and its centre increases by ~70 cm. We interpret this event as the initiation and/or widening of a fracture, aided by stress perturbations associated with meltwater loading in the doline basin. Viscous flexure modelling indicates that the meltwater loading generates tensile surface stresses exceeding 75 kPa. This, together with our timelapse photos of circular fractures around the doline, suggests the first such documentation of meltwater-loading-induced ‘ring fracture’ formation on an ice shelf, equivalent to the fracture type proposed as part of the chain-reaction lake drainage process involved in the 2002 breakup of the Larsen B Ice Shelf.

Analyzing Rinex Data Files Using the Python Programming Language

Abstract The techniques and tools developed for geodetic determinations have made it possible, over the past half century, to carry out measurements using global navigation satellite systems. As the accuracy and precision of positioning solutions, such as Fast Static and RTK, improve through technological advances, more applications will become available that can provide users with positioning information over time, autonomously verify the integrity of transmitted data, and ensure sufficient accuracy for their intended purposes. In our study for the interpretation, analysis, and visualization of raw and/or processed RINEX GNSS data recorded over time at a geodetic point using the information available from the Fast Static technique, we used the GeoRinex library from the Python programming language. This library converts data to xarray.data set, for easy use in processing parameter sets, from Rinex files: of ROMPOS reference stations and of the new B10 point resulting from measurements using the Fast Static technique: pseudorange (C1, C2, P1, P2....), carrier phase (L1, L2,…), doppler (D1, D2....) and signal strength (S1, S2....). All this information will help us to analyse and interpret the degradation of the parameters associated with Rinex version 2.11 epoch positioning files 12.02.2023, time interval 12:00-14:00 (fast static) and to understand their accuracy and behavior in different environments. Based on this study, our aim was to evaluate the error in determining the positioning accuracy of the B10 point located in a crowded and heavily trafficked area, which allows sufficient coverage of the GNSS satellites.

Seismic Moment Accumulation Rate From Geodesy: Constraining Kostrov Thickness in Southern California

AbstractSeismic moment accumulation rate is a fundamental parameter for assessing seismic hazard. It can be estimated geodetically from either fault‐based modeling, or strain rate‐based calculations, where fault‐based models largely depend on the rheological layering and the number of faults. The strain‐rate method depends on an unknown (Kostrov) thickness used to convert strain rate into moment rate. In Part 1 of this study, we use three published fault‐based models from southern California to establish the value of the Kostrov thickness such that the total moment from the strain‐rate approach, calculated from the fault model‐predicted strain rate, matches the fault‐based approach. Constrained thickness estimates of 7.3, 9.7, and 11.5 km (6.4–13.0 km, including uncertainties) suggest that the 11 km value used in previous studies may be too large and a lower value may be more accurate. In Part 2 we use calibrated values of Kostrov thickness, along with the latest compilation of GNSS velocity data, to partition moment rate into on‐fault and off‐fault moment rate, where off‐fault varies from 32%–43% of the total moment rate. The largest uncertainty is related to the method used to interpolate sparse GNSS data. Lastly, we compare our estimates of total moment rate (mean: 2.13 ± 0.42 × 1019 Nm/yr) with the historical seismic catalog. Results suggest that including uncertainties in Kostrov thickness brings fault‐based geodetic moment rate closer to the seismic moment release (particularly when aseismic afterslip is accounted for), while the (uncertain) values of off‐fault moment rate push geodetic moment rates to be larger than seismic moment rates.

A Multi-Level Robust Positioning Method for Three-Dimensional Ground Penetrating Radar (3D GPR) Road Underground Imaging in Dense Urban Areas

Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention.

Influence of different factors on coseismic deformation of the 2015 Mw7.8 earthquake in Nepal

In Geophysics, topographic factors are observations that can be directly measured, but they are often ignored to simplify the model. Studying the coseismic deformation caused by earthquakes helps accurately determine the epicenter's parameterization. It provides a reference for the reasonable layout of coseismic observation stations and GNSS observation stations. After the Mw7.8 earthquake in Nepal in 2015, GCMT, USGS, GFZ, CPPT, and other institutions released their epicenter parameter. However, according to their parameters, the coseismic displacements simulated by the spectral-element method are quite different from the GNSS observations. Firstly, this paper inverts the geometric parameters of the seismogenic fault with Nepal’s coseismic GNSS displacement. The spectral-element method determines the source's location and depth under the heterogeneous terrain and outputs the source parameters. Among the results of many studies, the surface source is more consistent with the generation mechanism of large earthquakes. Secondly, this paper calculates the fault slip distribution of this earthquake using SDM (Steepest Descent Method) based on GNSS and InSAR data, which is divided into 1500 subfaults, and the moment tensor of each subfault is calculated. This paper investigates the distribution characteristics of the coseismic deformation field of the 2015 Mw 7.8 earthquake in Nepal under three different models. The results show that the influence of topographic factors is ~ 20%, and the influence of heterogeneous factors is ~ 10%. This paper concludes that the influence of topographic factors is much more significant than that of heterogeneous factors, and the influence of both should be addressed in coseismic deformation calculations.