GNSS Data Research Articles

Monitoring Horizontal Displacements with Low-Cost GNSS Systems Using Relative Positioning: Performance Analysis

Monitoring horizontal displacements, such as landslides and tectonic movements, holds great importance and high-cost geodetic GNSS equipment stands as a crucial tool for the precise determination of these displacements. As the utilization of low-cost GNSS systems continues to rise, there is a burgeoning interest in evaluating their efficacy in measuring such displacements. This evaluation is particularly vital as it explores the potential of these systems as alternatives to high-cost geodetic GNSS systems in similar applications, thereby contributing to their widespread adoption. In this study, we delve into the assessment of the potential of the dual-frequency U-Blox Zed-F9P GNSS system in conjunction with a calibrated survey antenna (AS-ANT2BCAL) for determining horizontal displacements. To simulate real-world scenarios, the Zeiss BRT 006 basis-reduktionstachymeter was employed as a simulation device, enabling the creation of horizontal displacements across nine different magnitudes, ranging from 2 mm to 50 mm in increments of 2, 4, 6, 8, 10, 20, 30, 40, and 50 mm. The accuracies of these simulated displacements were tested through low-cost GNSS observations conducted over a 24 h period in open-sky conditions. Additionally, variations in observation intervals, including 3, 6, 8, and 12 h intervals, were investigated, alongside the utilization of the relative positioning method. Throughout the testing phase, GNSS data were processed using the GAMIT/GLOBK GNSS (v10.7) software, renowned for its accuracy and reliability in geodetic applications. The insightful findings gleaned from these extensive tests shed light on the system’s capabilities, revealing crucial information regarding its minimum detectable displacements. Specifically, the results indicate that the minimum detectable displacements with the 3-sigma rule stand at 22.8 mm, 11.7 mm, 8.7 mm, and 4.8 mm for 3 h, 6 h, 8 h, and 12 h GNSS observations, respectively. Such findings are instrumental in comprehending the system’s performance under varying conditions, thereby informing decision-making processes and facilitating the adoption of suitable GNSS solutions for horizontal displacement monitoring tasks.

A New Trajectory Reduction Method for Mobile Devices Operating Both Online and Offline

AbstractHighly accurate location data have become essential in nearly all contemporary global applications, including but not limited to route planning, processing traffic data, identifying common routes, map matching, and enhancing agricultural productivity. However, the abundance of unnecessary and redundant data leads to various challenges, especially concerning storage, processing, and transmission. Despite the existence of numerous studies aimed at addressing these GNSS data management challenges, the reduction problem is either partially resolved, or enhancements are made to existing solutions in nearly all of them. In this study, a novel reduction method is introduced, offering both a high reduction rate and accuracy, suitable for operation on mobile devices in both offline and online modes. The proposed method uses windowing with reference points during the decision phase to decrease the number of points. By utilizing the angle and its threshold between the decision point and reference points, we achieved a method characterized by low algorithmic complexity and a high reduction rate, suitable for online operation on mobile devices. Experiments and comparisons revealed that the proposed method had a 91.01% reduction in GNSS data which is 7.73% lower, a 5.8744e$$-$$ - 04 RMSE error which is 2e+7 times better, and a 14.54 ms running time which is 25% faster than RDP algorithm. The results indicate that incorporating the proposed method into current methodologies could be beneficial, particularly in scenarios where real-time, high-precision location data are essential.

The 1980 Aparejo Glacier catastrophic detachment: new insights and current status

The catastrophic detachment of Aparejo Glacier (one of the three known cases in the Andes) took place on 1 March 1980 and resulted in the removal of an ice volume initially estimated to be 7.2 Mm3, which originally was 1.0 km long and covered an area of 0.2 km2. The event caused the sudden mobilization of the sliding mass 3.7 km down valley at an estimated speed of 110 km/h, causing remarkable geomorphological changes, including the obliteration of most of the glacier. 40 years after the event, we analyze new evidence: 3 ground surveys carried out in 2015 and 2016; DEMs and glacier outlines compiled from orthorectified aerial imagery pre-and post-event; GNSS data; Ground Penetrating Radar (GPR) data; a terrestrial LiDAR scan survey of 2020, together with detailed interviews with 2 direct witnesses of the event, terrestrial and helicopter-borne photographs acquired 12 days after the sudden detachment. The combined interpretation of these new data, allow us to make a more precise estimation of the pre-detachment glacier volume, 12.9 ± 0.6 × 106 m3 and the detached ice volume of 11.7 ± 0.6 × 106 m3 (90% of the total volume of the glacier). We also show that in the 40-year period Aparejo Glacier has recovered 12.4% of the original glacier volume, with a mean ice thickness of 19.5 m and a maximum of 40 m according to GPR data, being preserved within the same basin as the detached glacier. In recent years, the glacier has shown a mean elevation change of −3.7 ± 1.2 m during the 2015–2020 period, with maximum thinning values greater than 8 m, which are probably caused by enhanced ablation due to climate warming and reduced precipitation during the current megadrought which started in 2010 and has lasted more than 1 decade. We conclude that under the projected scenarios of climate warming and reduced precipitation for central Chile, the risk associated to a new detachment of Aparejo Glacier is unlikely.

Tracking magma pathways and surface faulting in the Southwest Rift Zone and the Koaʻe fault system (Kīlauea volcano, Hawai ‘i) using photogrammetry and structural observations

Volcanic islands are often subject to flank instability, resulting from a combination of magmatic intrusions along rift zones and gravitational spreading causing extensional faulting at the surface. Here, we study the Koaʻe fault system (KFS), located south of the summit caldera of Kīlauea volcano in Hawaiʻi, one of the most active volcanoes on Earth, prone to active faulting, episodic dike intrusions, and flank instability. Two rift zones and the KFS are major structures controlling volcanic flank instability and magma propagation. Although several magmatic intrusions occurred over the KFS, the link between these faults, two nearby rift zones and the flank instability, is still poorly studied. To better characterize the KFS and its structural linkage with the surrounding fault and rift zones, we performed a detailed structural analysis of the extensional fault system, coupled with a helicopter photogrammetric survey, covering part of the south flank of Kīlauea. We generated a high-resolution DEM (~ 8 cm) and orthomosaic (~ 4 cm) to map the fracture field in detail. We also collected ~ 1000 ground structural measurements of extensional fractures during our three field missions (2019, 2022, and 2023). We observed many small, interconnected grabens, monoclines, rollover structures, and en-echelon fractures that were in part previously undocumented. We estimate the cumulative displacement rate across the KFS during the last 600 ~ 700 years and found a decrease toward the west of the horizontal component from 2 to 6 cm per year, consistent with GNSS data. Integrating morphology observations, fault mapping, and kinematic measurements, we propose a new kinematic model of the upper part of the Kīlauea’s south flank, suggesting a clockwise rotation and a translation of a triangular wedge. This wedge is bordered by the extensional structures (ERZ, SWRZ, and the KFS), largely influenced by gravitational spreading. These findings illustrate a structural linkage between the two rift zones and the KFS, the latter being episodically affected by dike intrusions.

A novel outlier detection method based on Bayesian change point analysis and Hampel identifier for GNSS coordinate time series

The identification and removal of outliers in time series are important problems in numerous fields. In this paper, a novel method (BCP-HI) is proposed to enhance the accuracy of outlier detection in GNSS coordinate time series by combining Bayesian change point (BCP) analysis and the Hampel identifier (HI). By using BCP, change points (cps) in the time series are lidentified, and so the time series is divided into subsegments that have properties of a normal distribution. In each of these separated segments, outliers are detected using HI. Each data element identified as an outlier is corrected by a median filter of window size (w) to obtain the corrected signal. The BCP-HI method was tested on both simulated and real GNSS coordinate time series. Outliers from three different synthetic test datasets with different sampling frequencies and outlier amplitudes were detected with approximately 98% accuracy after processing. After this process, Signal-to-Noise Ratio (SNR) increased from 0.0084 to 10.8714 dB and Root Mean Square (RMS) decreased from 24 to 23 mm. Similarly, for real GNSS data, approximately 98% accuracy was achieved, with an increase in SNR from 0.0003 to 4.4082 dB and a decrease in RMS from 7.6 to 6.6 mm observed. In addition, the output signals after BCP-HI were examined graphically using Lomb–Scargle periodograms and it was observed that clearer power spectrum distributions emerged. When the input and output signals were examined using the Kolmogorov–Smirnov (KS) test, they were found to be statistically similar. These results indicate that the BCP-HI algorithm effectively removes outliers, and enhances processing accuracy and reliability, and improves signal quality.

Analysis of factors affecting the estimation of the multi-GNSS satellite differential code biases (SDCBs)

The satellite hardware differential code biases (SDCBs) are one of the important factors affecting the accuracy of GNSS-based ionospheric total electron content (TEC) estimation. The accuracy of SDCB estimates is directly influenced by the data processing methodology. To analyze the impact of various factors on multi-GNSS SDCB estimation, we conducted a comprehensive analysis of the number of contributed stations, the data sampling rate, the contributed satellite systems, and the cut-off elevation angle. Two sets of GNSS data obtained from the International GNSS Service (IGS) multi-GNSS experiment (MGEX) network, covering both high and low solar activities during the ascending phase of solar cycle 25, were processed to estimate multi-GNSS SDCBs. The results indicate that the number of contributed stations is the primary factor that influences multi-GNSS SDCB estimation. To achieve a balance between computational efficiency and accuracy, a data sampling rate of 540 s is recommended, which results in a multi-GNSS SDCB root-mean-square (RMS) error of less than 0.01 ns compared to a 60-second sampling rate. The stability of SDCBs is hardly affected by incorporating observations from multiple GNSS systems, and the differences of the standard deviations of SDCBs for quad-, triple- and dual-system solutions are below 0.002 ns. Additionally, the results indicate that the optimal cutoff elevation angle for multi-GNSS SDCB estimation is between 20° and 30°, which ensures the best stability in the estimated multi-GNSS SDCBs.

Slip distribution of the 2024 Noto Peninsula earthquake (MJMA 7.6) estimated from tsunami waveforms and GNSS data

AbstractThe 1 January 2024 Noto-Hanto (Noto Peninsula) earthquake (MJMA 7.6) generated strong ground motion, large crustal deformation and tsunamis that caused significant damage in the region. Around Noto Peninsula, both offshore submarine and partially inland active faults have been identified by previous projects: Ministry of Land, Infrastructure, Transport and Tourism (MLIT) and Japan Sea Earthquake and Tsunami Research Project (JSPJ). We inverted the tsunami waveforms recorded on 6 wave gauges and 12 tide gauges around Sea of Japan and the GNSS data recorded at 53 stations in Noto Peninsula to estimate the slip amount and seismic moment on each of active faults. The results show that the 2024 coseismic slips were 3.5 m, 3.2 m, and 3.2 m on subfaults NT4, NT5 and NT6 of the JSPJ model, located on the northern coast of Noto Peninsula and dipping toward southeast. A smaller slip, 1.0 m, estimated on NT8 on the southwestern end of the 2024 rupture, may be attributed to its previous rupture during the 2007 Noto earthquake. The total length of these four faults is ~ 100 km, and the seismic moment is 1.90 × 1020 Nm (Mw = 7.5). Almost no slip was estimated on the northeastern subfaults NT2 and NT3, which dip northwestward, opposite to NT4–NT5–NT6, and western subfault NT8. Aftershocks including the MJMA 6.1 event occurred in the NT2–NT3 region, but they are smaller than the potential magnitude (Mw 7.1) those faults can release in a tsunamigenic earthquake. Similar features are also found for the MLIT model; the 2024 slip was only on F43 along the northern coast of Noto Peninsula, and northeastern F42 did not rupture, leaving potential for future event. Graphical Abstract

Application of Deep Learning in Sea Surface Height Estimation of GNSS Data Sets

Application of Deep Learning in Sea Surface Height Estimation of GNSS Data Sets

An investigation of PPP degradations over Asia-Pacific region during Tonga volcanic eruption on 15 January 2022

On 15 January 2022, a significant volcanic eruption occurred at Hunga Tonga-Hunga Ha’apai (HTHH), causing global ionospheric disturbances. To investigate ionospheric anomalies and the kinematic GNSS PPP responses to this volcanic eruption over the Asia-Pacific region, the rate of the total electron content index (ROTI) map derived from about 1100 GNSS stations data and ionospheric scintillation indices S4 and σφ are used. We first selected data from six stations for kinematic PPP experiments. Multi-constellation and multi-frequency Precision Positioning (GAMP) software and GNSS analysis software are employed in the process. We then found that the positioning errors of three directions (east, north, up) during disturbance time are larger than those on an ionospheric quiet day (3 January 2022). To explore the reasons for PPP degradation, we conduct a quality analysis of GNSS data on these two days, including carrier-to-noise power-density ratio (C/N0) and the number of cycle slips. The number of satellites affected by cycle slips increased obviously in the disturbance time. The statistics of cycle slip occurrence rate at low latitudes is 12.35 % while that on ionospheric quiet day is only 6.05 %. The strategy of expanding the cycle slip detection thresholds from 0.15 m to 1.5 m for phase geometry free (PGF) combination and 5 cycles to 12 cycles for Hatch–Melbourne-Wübbena (HMW) combination failed to significantly improve the PPP solutions based on data from 357 stations. It was found that only increasing the detection thresholds could not mitigate the PPP degradations. Our investigations contribute to the study of space weather and kinematic GNSS PPP responses to volcanic eruptions.

Representation and interpretation about underwater sound speed gradient field in the GNSS-A observation

SUMMARY The Global Navigation Satellite System—Acoustic ranging combination technique (GNSS-A) is a seafloor geodetic observation technique that achieves an accuracy of centimetres by combining high-rate GNSS data with acoustic ranging. The technique determines the seafloor position by acoustic ranging between the sea surface and multiple seafloor stations, using GNSS data from the sea surface station. Here, the gradient state of the underwater sound speed structure (SSS) is a significant source of error. The open-source software GARPOS can reduce the effect from underwater gradient structures by retrieving the underwater disturbance as a parameter projected onto the sea surface and seafloor. To evaluate the effects of underwater disturbances, a quantitative comparison of the model parameters is necessary. In this study, we developed a representation method to evaluate features of the ocean field. Here, the expression method was described in the order of a formulation and an interpretation in the case of a 2-D cross-section and extension to the case of an actual 3-D field. This method makes it possible to evaluate SSS states in the GNSS-A observations. As an example, we showed the correlation between the anomaly of the expressed ocean state and the anomaly of the seafloor station position, showing that this expression method is an effective index for correcting bias errors. Additionally, we used the data from sites located in the Kuroshio area, a strong current near Japan, to show that the ocean state can be quantitatively interpreted using this expression method.

A Cloud-native Approach for Processing of Crowdsourced GNSS Observations and Machine Learning at Scale: A Case Study from the CAMALIOT Project

The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency multi-constellation GNSS observations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. In addition, such large datasets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) was an ESA NAVISP Element 1 project (NAVISP-EL1-038.2) with activities aiming to address the aforementioned points related to GNSS community data and their exploitation for scientific applications with the use of Machine Learning (ML). This contribution provides an overview of the CAMALIOT project with information on the designed and implemented cloud-native software for GNSS processing and ML at scale, developed Android application for retrieving GNSS observations from the modern generation of smartphones through dedicated crowdsourcing campaigns, related data ingestion and processing, and GNSS analysis concerning both conventional and smartphone GNSS observations. With the use of the developed GNSS engine employing an Extended Kalman Filter, example processing results related to the Zenith Total Delay (ZTD) and Slant Total Electron Content (STEC) are provided based on the analysis of observations collected with geodetic-grade GNSS receivers and from local measurement sessions involving Xiaomi Mi 8 that collected GNSS observations using the developed Android application. For smartphone observations, ZTD is derived in a differential manner based on a single-frequency double-difference approach employing GPS and Galileo observations, whereas satellite-specific STEC time series are obtained through carrier-to-code leveling based on the geometry-free linear combination of observations from both GPS and Galileo constellations. Although the ZTD and STEC time series from smartphones were derived on a demonstration basis, a rather good level of consistency of such estimates with respect to the reference time series was found. For the considered periods, the RMS of differences between the derived smartphone-based time series of differential zenith wet delay and reference values were below 3.1 mm. In terms of satellite-specific STEC time series expressed with respect to the reference STEC time series, RMS of the offset-reduced differences below 1.2 TECU was found. Smartphone-based observations require special attention including additional processing steps and a dedicated parameterization in order to be able to acquire reliable atmospheric estimates. Although with lower measurement quality compared to traditional sources of GNSS data, an augmentation of ground-based networks of fixed high-end GNSS receivers with GNSS-capable smartphones would however, form an interesting source of complementary information for various studies relying on GNSS observations.

German and European Ground Motion Service: a Comparison

Since the end of 2022, two ground motion services that cover the complete area of Germany are available as web services: the German Ground Motion Service (Bodenbewegungsdienst Deutschland, BBD) provided by the Federal Institute for Geosciences and Natural Resources (BGR), and the first release of the European Ground Motion Service (EGMS) as part of the Copernicus Land Monitoring Service. Both services are based on InSAR displacement estimations generated from Sentinel‑1 data. It would seem relevant to compare the products of the two services against one another, assess the data coverage they provide, and investigate how well they perform compared to other geodetic techniques. For a study commissioned by the surveying authority of the state of Baden-Württemberg (Landesamt für Geoinformation und Landentwicklung Baden-Württemberg, LGL), BBD and EGMS data from different locations in Baden-Württemberg, Saarland, and North Rhine-Westphalia (NRW) were investigated and validated against levelling and GNSS data. We found that both services provide good data quality. BBD shows slightly better calibration precision than EGMS. The coverage provided by EGMS is better than that of BBD on motorways, federal roads, and train tracks of the Deutsche Bahn. As an example, where both services have difficulties in determining the correct displacements, as they cannot be described well by the displacement models used for processing, we present the test case of the cavern field at Epe (NRW). Finally, we discuss the implications of our findings for the use of the products of BBD and EGMS for monitoring tasks.

Comparative Analysis of Methods to Estimate Geodetic Strain Rates from GNSS Data in Italy

Our ability to estimate surface deformation rates in the Central Mediterranean has considerably enhanced in the last decade thanks to the growth of continuous Global Navigation Satellite System (GNSS) networks. Focusing on the Apennine/Alpine seismogenic belt, this area offers the opportunity to test the use of geodetic strain rates for constraining active tectonic processes and for seismic hazard assessments. Given the importance of geodetic strain rate models in modern hazard estimation approaches, however, one has to consider that different approaches can provide significantly different strain rate maps. Despite the increasing availability of GNSS velocity data, in fact, strain rate models can significantly differ, because of the spatial heterogeneity of GNSS stations locations and inherent strategies in computing strain rates. Using a dense GNSS velocity dataset, this study examines three methods for estimating horizontal strain rates, described in the recent literature and selected to represent approaches of increasing mathematical complexity. Advantages, drawbacks and optimal settings of each method are discussed. The main result is an ensemble of strain rate models that enable the evaluation of epistemic uncertainties in seismicity rates models constrained by geodetic velocities.

An integration scheme of simultaneous cycle slips determination combining improved geometry-free combination and TDCP model for undifferenced GNSS data

Fast and reliable cycle slip determination can ensure successive ambiguity resolution and precise positioning. Generally, it is not difficult to determine big cycle slips using the linear combination of observations, for instance, the geometry-free (GF) combination and the Hatch–Melbourne–Wübbena (HMW) combination. However, the participation of pseudorange observations may fail to identify small cycle slips. This contribution proposes an integration scheme combining the improved geometry-free (IGF) combination and time-difference carrier phase (TDCP) model to determine simultaneous cycle slips for undifferenced kinematic data. The IGF combination, which is improved from the modified geometry-free (MGF) combination and utilizes the Gauss floor function to take the decimal part of GF, can directly determine small cycle slips on a specific frequency. The TDCP model is used to estimate the differences of position and clock error between adjacent epochs by least-square adjustment using clean phase observations, and repair the remaining cycle slips by the predicted TDCP measurements obtained from the predetermined parameters. The proposed method is tested against 1 Hz kinematic dataset with simulation and highway dataset with real cycle slips. In the simulation test, all cycle slips can be correctly repaired by IGF for L1/L5, E1/E5 and E1/E5a. These combinations are unavailable for MGF. Compared to the HMW-GF method, the repair success rate of IGF improves from 96.80% to 99.16%, and the number of incorrect cases reduces from 9650 to 2535. The IGF-TDCP integration scheme can further improve the performance of IGF, whose repair success rate is more than 99.97% and incorrect cases are 38. The highway test shows that the proposed method effectively processes simultaneous cycle slips on more than half of the tracking satellites caused by the overpasses, even in the case of simultaneous 3 second data gaps on 5 satellites of 6 tracking satellites.

Comparative Evaluation of LiDAR systems for transport infrastructure: case studies and performance analysis

ABSTRACT Mobile laser scanners are vital for intelligent transport infrastructure, capturing detailed 3D road representations, but their accuracy depends on factors like sensor positioning and environment. This study compares two van-mounted Mobile Laser Scanners (MLS): the dual head Lynx Mobile Mapper and the single head VUX-1 HA, along with the terrestrial laser scanner Faro Focus XX30. Using point cloud reference data from Faro Focus XX30 and GNSS data from Trimble R8, performance is assessed in road, urban, and semi-urban environments. Accuracy is measured by the difference between Trimble GNSS and MLS coordinates. Geometric features of each LiDAR are compared, and mapping tasks in road and urban areas are performed using a machine learning classifier. Results show the MLS-single head scanner achieves satisfactory accuracy in roads and semi-urban areas, while Faro performs better in urban settings for classification. MLS-single head excels in road environments, while Faro is superior in urban ones. This analysis aids researchers and professionals in selecting the appropriate mobile laser scanner for mapping transport infrastructure, providing valuable insights into MLS systems’ comparative performance across different environments.

SEISMICITY AND CRUSTAL SEISMOTECTONIC DEFORMATIONS OF THE ANATOLIAN PLATE (TURKEY)

The paper deals with the study of seismicity and the crustal stress-strain state of Turkey whose tectonics is caused by relative movements of the African, Aegean, Arabian, Anatolian, Black Sea and Eurasian plates. Seismotectonic deformations (STD) were calculated using the approaches proposed by Yu.V. Riznichenko and S.L. Yunga. The distribution of seismicity, average annual STD rate (STD intensity), seismogenic rupture concentration parameter and the Tsallis parameter was plotted from the earthquake catalog data analysis (more than 300000 events for 1900–2022). The representative part includes the events with М≥2.5. The distribution of the above-mentioned parameters was calculated for a 0–40 km layer wherein most of the earthquakes are located. Deep-focus earthquakes occur at a depth to 170 km. The maximum annual number of earthquakes (more than 90) was recorded in Balikesir Province. Seismicity and STD intensity analysis was made on the periods of 1997–2009 and 2010–2022 for the western and eastern parts of Turkey and for the destructive February 6, 2023 earthquake area. The number of earthquakes recorded in the western part of Turkey (75 %) largely exceeds that recorded in the eastern part (25 %). In the period 2010–2022, there was an increase in the number of earthquakes in the eastern part of Turkey relative to the period 1997–2009, which made up 38 % of the total number of earthquakes. The studies of STD direction are based on the earthquake focal mechanism data (807 events for 1976–2023). STD intensity in the February 6, 2023 earthquake area, with regard to the February 6, 2023 earthquakes, is 1.34⋅10–6 year–1, and the background intensity for the entire period of observation (1900–2022) is –10–10 year–1. The STD maps show the diversity of deformation settings in the study area. Turkey is largely dominated by the horizontal shear mode (eastern and central parts). The extension mode manifests itself in the southwestern part. The western part of Turkey and the southern part of the East Anatolian fault zone are characterized by transtension mode. The Lake Van area is characterized by transpression mode. The shortening axis direction changes from sublatitudinal in the west of Turkey to submeridional in the east. STD tensors served as a base for plotting the distributions of the Lode – Nadai coefficient με and the vertical component. Based on the seismic and GNSS data, a comparison was made between the shortening and elongation axes directions.

IONOSPHERIC DISTURBANCES OVER PRIMORSKY KRAI CAUSED BY THE ERUPTION OF THE HUNGA TONGA-HUNGA HA’APAI VOLCANO ON JANUARY 15, 2022

The explosive eruption of the Hunga Tonga-Hunga Haapai volcano occured on January 15, 2022 at 04:02 UTC led to generation of covolcanic ionospheric disturbances that spread over long distances. Using GNSS data obtained at permanent stations of the IGS network and sites located on the territory of Primorsky Krai, the search and analysis of ionospheric disturbances over the territory of Primorsky Krai and adjacent areas was performed. The velocity of the covolcanic ionospheric disturbances recorded over the Primorsky Krai reached about 340 m/s, and the average amplitude was equal to 1.0 TECU. The results obtained from GNSS-data were also compared with the results of observation data obtained by a laser strainmeters (oriented in the directions "north – south" and "east – west") and a laser nanobarograph located at the Schultz Cape (the south of Primorsky Krai). As a comparison result, time delays between the moments of fixation of disturbances in the troposphere and ionosphere were found. The delay between the first peaks of disturbances was equal to ~50 min.

GNSS ASSISTED PHOTOGRAMMETRIC RECONSTRUCTION FROM COMBINED 360° VIDEOS AND UAV IMAGES

Abstract. This paper introduces an integrated approach utilizing ground data consists of videos captured with a 360° (spherical) camera and aerial data acquired with a UAV equipped with a RTK GNSS module to reconstruct a portion of a small-town city center and/or a cultural heritage site. Previous research has demonstrated that image blocks oriented with RTK data on camera position can reach centimeter accuracies and can be efficiently used to reconstruct large areas and single monuments. However, some areas like porches, narrow passages and streets cannot be properly reconstructed from an aerial point of view. Conversely, ground-based 360° images offer detailed insights into the terrain and features that may be obscured from an aerial perspective. Integration of those two points of view can increase spatial resolution and coverage for 3D reconstruction. Indeed, the UAV captures large-scale features and topography, while ground-based 360° images focus on intricate details and ground-level characteristics. The possibility to exploit GNSS data acquired by UAV may also be used for GNSS-assisted image orientation with the aim of reducing or even avoiding, in specific situations, the need for GCPs. The paper explores practical applications of such data integration in the cultural heritage domain demonstrating the efficacy of the integrated approach in scenarios with complex architectures and inaccessible areas.

Source parameter inversion of the 2021 MW7.4 Maduo earthquake and stress transfer in the eastern Bayan Har block

In order to explore the constraint degree of GNSS and InSAR data on the Maduo earthquake under the layered earth model structure, and to understand the cause of the Maduo earthquake and the danger of the surrounding area, this paper uses D-InSAR technology to obtain the InSAR co-seismic deformation field of the Maduo earthquake on May 22,2021. Based on GNSS and InSAR data, the co-seismic slip model and fault plane stress distribution of the Maduo earthquake are jointly inverted. This paper calculates the Coulomb stress changes caused by 14 M ≥ 7 strong earthquakes co-seismic, post-seismic viscoelastic relaxation and inter-seismic tectonic stress loading of 19 fault segments in the Bayan Har block research area (96°E-106°E, 29°N-36°N) since 1900. The results show that: (1) The maximum Line Of Sight (LOS) deformation obtained based on the ascending and descending InSAR data was about 0.9 m. The maximum slip amount of GNSS + InSAR joint inversion is 4.87 m, which is located in the Dongcao Along Lake section (∼98.6°E). Combined with the stress drop distribution of the fault surface, it is found that the Maduo earthquake is dominated by strike-slip. (2) The historical strong earthquakes delayed the Maduo earthquake by nearly 103 years. The 1937 Huashixia earthquake, the 1947 Dari earthquake, the 1963 Dulan earthquake, and the 1973 Luhuo earthquake had the greatest stress unloading effect, which was equivalent to delaying the Maduo earthquake by 53 years, 21 years, 11 years, and 17 years, respectively. The cumulative Coulomb stress at the source location of the Maduo earthquake reached 0.0112 MPa. Most area of the fault sections such as the East Kunlun Fault, the Maduo-Gande Fault, the western and eastern sections of the Dari Fault, the western and eastern sections of the Qingshuihe Fault, the Ganzi-Yushu Fault, and the eastern section of the Kunlunshankou-Jiangcuo Fault are in the Coulomb stress loading area, which should be paid attention to.

An Innovative Sensor Integrated with GNSS and Accelerometer for Bridge Health Monitoring

This paper presents an innovative integrated sensor that combines GNSS and a low-cost accelerometer for bridge health monitoring. GNSS and accelerometers are both significant and effective sensors for structural monitoring, but they each have limitations. The sampling rate of GNSS data is relatively low, making it challenging to capture high-frequency vibrations, while accelerometers struggle with low-frequency signals and are susceptible to environmental changes. Additionally, GNSS receivers and accelerometers are often installed separately, leading to challenges in data fusion processing due to differing temporal and geospatial references. The proposed integrated sensor addresses these issues by synchronizing GNSS and an accelerometer’s time and geospatial coordinate reference. This allows for a more accurate and reliable deformation and vibration measurement for bridge monitoring. The performance of the new sensor was assessed using a high-quality/cost Leica GM30 GNSS receiver and a Sherborne A545 accelerometer. Experiments conducted on the Wilford suspension bridge demonstrate the effectiveness of this innovative integrated sensor in measuring deformation and vibration for bridge health monitoring. The limitation of the low-cost MEMS (Micro Electromechanical System) accelerometer for the weak motion frequency detection is also pointed out.