Delay Correction Research Articles

Tropospheric delay estimation and influencing factors analysis of GNSS multi-system

Abstract In this paper, the tropospheric delay parameters are estimated by GNSS regional network solution, and the different factors affecting the estimation of tropospheric delay parameters are analyzed experimentally. The high-precision tropospheric delay correction file (.ztd) provided by IGS and the traditional model estimation of the GNSS regional network are compared to verify the final positioning accuracy. The main factors affecting the estimation of tropospheric delay parameters and the conditions conducive to the estimation of tropospheric delay parameters in the data solution process are obtained. The experimental results show that: (1) GPS has the highest tropospheric delay estimation accuracy in the European region, Galileo is slightly worse than GPS, and BDS has the lowest tropospheric delay estimation accuracy; (2) The tropospheric delay estimation accuracy of post-product in different satellite precision products is better than that of real-time products, and CLKM2 is better than CLK93 in real-time products. With the increase of satellite systems, the accuracy of tropospheric delay estimation is gradually improved. Compared with the GPS single system, the accuracy of the GPS+ Galileo+BDS triple system is improved by about 7.2%, 7.2%, and 7.5%; (3) The tropospheric delay estimation accuracy of the European region is better than that of the Australian region; (4) Based on the BDS whole system data solution, the high-precision tropospheric delay correction file (.ztd ) has better positioning accuracy, and the tropospheric delay estimated by the GNSS observation data through the traditional Saastamoninen model of the regional network can also meet the current high-precision positioning requirements in terms of positioning accuracy. The horizontal direction is better than 2 cm, and the elevation direction is better than 4 cm.

Subsidence detection in southwest Guangdong–Hong Kong–Macao Greater Bay Area using InSAR with GNSS corrected tropospheric delays

As one of the biggest bay areas in the world, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is subject to subsidence due to thick sedimentary. In this study, we combine 154 Sentinel-1 A/B images acquired between 2017 and 2022 with Global Navigation Satellite System (GNSS) observations to characterize the subsidence in southwestern GBA. Specifically, we use GNSS to establish the Iterative Tropospheric Decomposition (ITD) model for zenith tropospheric delay correction and the model is improved using random forest algorithm to alleviate errors caused by unreasonable interpolation methods, showing better performance than Generic Tropospheric Correction Online Service (GACOS) product. At GNSS stations with complete data, the improved model utilizing GNSS data achieves a Pearson correlation coefficient of 0.659, in contrast to a coefficient of only 0.434 using GACOS. The maximum subsidence rate detected is up to 11.32 cm/year in Zhuhai, accompanied by noticeable seasonal variations correlated with precipitation. In addition to the sediment consolidation and compaction, human activities such as agriculture, construction, and land reclamation further exacerbate subsidence in the GBA. This study enhances the utility of GNSS in improving tropospheric delay correction performance and providing more accurate observations of subsidence.

In-field Phasing at the Upgraded GMRT

In time-domain radio astronomy with arrays, voltages from individual antennas are added together with proper delay and fringe correction to form the beam in real time. In order to achieve the correct phased addition of antenna voltages, one has to also correct for the ionospheric and instrumental gains. Conventionally this is done using observations of a calibrator source located near to the target field. This scheme is suboptimal since it does not correct for the variation of the gains with time and position in the sky. Further, since the ionospheric phase variation is typically most rapid at the longest baselines, the most distant antennas are often excluded while forming the beam. We present here a different methodology (“in-field phasing”), in which the gains are obtained in real-time using a model of the intensity distribution in the target field, which overcomes all of these drawbacks. We present observations with the upgraded Giant Metrewave Radio Telescope (uGMRT) which demonstrates that in-field phasing does lead to a significant improvement in sensitivity. We also show, using observations of the millisecond pulsar J1120−3618 that this in turn leads to a significant improvement of measurements of the Dispersion Measure and Time of Arrival. Finally, we present test observations of the GMRT discovered eclipsing black widow pulsar J1544+4937 showing that in-field phasing leads to improvement in the measurement of the cut-off frequency of the eclipse.

Analysis of Ionospheric Delay Correction Model Performance During Geomagnetic Storms

AbstractIonospheric delay, as one of the largest error sources in radio propagation, can only be corrected for this error using the ionospheric delay correction model for Global Navigation Satellite System (GNSS) single‐frequency users. In this paper, the 2021 geomagnetic storm event is selected, and based on the measured ionospheric data from the GNSS observatory, the perturbation of the ionosphere by the geomagnetic storm event is analyzed, and it is found that the response of the ionosphere to the geomagnetic storm has obvious differences in the response characteristics and response time in different latitude regions. The performance of the global ionospheric map (GIM), the empirical model, and the broadcast ionospheric model during the geomagnetic storm‐induced ionospheric perturbation is analyzed and the change in the accuracy of each ionospheric model during the geomagnetic storm‐induced ionospheric perturbation is investigated, using the measured electron content of the GNSS as a benchmark. The results show that there is good agreement between the GIM products and the measured electron content during the period of ionospheric calm and the period of ionospheric perturbation. It is worth noting that geomagnetic storms do not necessarily lead to a decrease in the accuracy of ionospheric delay‐correction models, and in some cases, the models that were originally under‐accurate show a tendency to improve their accuracy during the period of perturbation instead. Neither the broadcast ionospheric model nor the electron content of the empirical model output responds to geomagnetic storm‐induced ionospheric perturbations.

Investigation of the Contribution of Five Broadcast Ionospheric Models (GPSK, NTCMG, NEQG, BDGIM, and BDSK) and IRTG to GNSS Positioning During Different Solar Activities in Solar Cycle 25

AbstractAdditional ionospheric information is essential for mitigating errors in single‐frequency (SF) Global Navigation Satellite Systems (GNSS) positioning. The increasing number of low‐cost dual‐frequency (DF) receiver users faces limitations in tracking DF observables compared to traditional geodetic receivers. Consequently, ionospheric correction algorithms (ICAs) are also essential for low‐cost DF devices in hybrid‐frequency positioning. To evaluate the performance of commonly used ICAs during solar cycle 25, our study presents a global statistical investigation of the contribution of five broadcast ionospheric models (BIMs) and the International GNSS Service (IGS) combined real‐time global ionospheric maps (IRTG) to the positioning domain, covering both quiet and perturbed ionospheric conditions. The BIMs investigated include the GPS Klobuchar (GPSK), Galileo NequickG (NEQG), NTCM‐GlAzpar (NTCMG), BDS‐2 Klobuchar (BDSK), and BeiDou Global Ionospheric delay correction Model (BDGIM). Experimental results from standard point positioning indicate that IRTG demonstrates superior overall accuracy compared to all BIMs, with a mean 3D root mean squared (RMS) of 2.76 m during perturbed period. Specifically, GPSK, NTCMG, NEQG, BDGIM, and BDSK exhibit RMS values of 2.03, 1.67, 1.72, 1.62, and 2.36 m during quiet conditions, and 4.02, 3.17, 2.86, 3.14, and 4.44 m during perturbed conditions, respectively. Among the BIMs, NEQG demonstrates superior performance at middle and high latitudes but exhibits lower accuracy than NTCMG and BDGIM at low latitudes during daytime under quiet conditions. BDGIM performs slightly better than NTCMG at low latitudes but slightly worse at high latitudes. BDSK shows notable improvement for high‐ and mid‐latitude regions since 3 June 2020.

Multi‐Frequency SuperDARN Interferometer Calibration

AbstractThe ground‐based, high‐frequency radars of the Super Dual Auroral Radar Network (SuperDARN) observe backscatter from ionospheric field‐aligned plasma irregularities and features on the Earth's surface out to ranges of several thousand kilometers via over‐the‐horizon propagation of transmitted radio waves. Interferometric techniques can be applied to the received signals at the primary and secondary antenna arrays to measure the vertical angle of arrival, or elevation angle, for more accurate geolocation of SuperDARN observations. However, the calibration of SuperDARN interferometer measurements remains challenging for several reasons, including a 2π phase ambiguity when solving for the time delay correction factor needed to account for differences in the electrical path lengths between signals received at the two antenna arrays. We present a new technique using multi‐frequency ionospheric and ground backscatter observations for the calibration of SuperDARN interferometer data, and demonstrate its application to both historical and recent data.

Study of high-precision time transfer method enhanced by PPP-AR/PPP-RTK

Abstract With the continuous development and improvement of Global Navigation Satellite Systems (GNSS), the use of GNSS for high-precision time transfer has become increasingly mature. To delve into the contribution of information-enhanced GNSS PPP to time transfer performance, this paper focuses on the comprehensive evaluation and analysis of the time transfer performance of PPP-AR and PPP-RTK. Using GPS as a case study, experimental results demonstrate that the average success fixing rate of PPP integer ambiguity resolution across five stations is 93.57%, with initial fixing times consistently below 20 minutes. The stability of the station clock offset sequence of PPP-AR is better than that of PPP floating solution. Compared with the PPP floating solution, the average improvement of PPP-AR stability is 16.54%. Furthermore, using PPP-AR for time transfer not only enhances the stability of the time transfer link clock offset sequence, but also improves its noise level. The stability is increased by 14.41% on average, and the noise level is improved by 9.11% on average. PPP-RTK also improves the stability and noise level of time transfer even better than PPP-AR. However, as the reference network scale increases, the accuracy of the interpolated atmospheric delay correction decreases, impacting the performance of PPP-RTK.

Nonlinear standing waves for assessing material nonlinearity in thin samples

The second harmonic generation (SHG) technique offers a quantitative damage parameter known as the acoustic nonlinearity parameter (β) capable of detecting the change in the inherent material nonlinearity. However, current SHG methods, in particular, those used for measuring β in construction materials, have an unresolved issue in their application due to limited sample sizes. The restricted sample dimensions lead to the generation of boundary-reflected waves, which hinder the selective detection of propagating waves and thus the precise evaluation of material nonlinearity through β. Furthermore, the use of large samples limits the compatibility of the SHG method with other characterization modalities, such as mechanical tests, X-ray diffraction, and computerized tomography. To address this issue, this paper introduces a new SHG method that is based on the use of nonlinear standing waves – the dominant longitudinal standing waves in a forced-free configuration. The corrections for phase delay and attenuation effect of each reflected wave are made, enabling accurate measurements of β in thin samples with no requirement in the thickness-wavelength ratio. The measured β is then employed to quantify the microstructural modification in cement paste induced by thermal damage, validating the proposed method as a promising tool for quantifying microstructural changes in materials.

Refined InSAR Mapping Based on Improved Tropospheric Delay Correction Method for Automatic Identification of Wide-Area Potential Landslides

Slow-moving landslides often occur in areas of high relief, which are significantly affected by tropospheric delay. In general, tropospheric delay correction methods in the synthetic-aperture radar interferometry (InSAR) field can be broadly divided into those based on external auxiliary information and those based on traditional empirical models. External auxiliary information is hindered by the low spatial–temporal resolution. Traditional empirical models can be adaptable for the spatial heterogeneity of tropospheric delay, but are limited by preset window sizes and models. In this regard, this paper proposes an improved tropospheric delay correction method based on the multivariable move-window variation model (MMVM) to adaptively determine the window size and the empirical model. Considering topography and surface deformation, the MMVM uses multivariate variogram models with iterative weight to determine the window size and model, and uses the Levenberg–Marquardt (LM) algorithm to enhance convergence speed and robustness. The high-precision surface deformation is then derived. Combined with hotspot analysis (HSA), wide-area potential landslides can be automatically identified. The reservoir area of the Baihetan hydropower station in the lower reaches of the Jinsha River was selected as the study area, using 118 Sentinel-1A images to compare with four methods in three aspects: corrected interferograms, derived deformation rate, and stability of time-series deformation. In terms of mean standard deviation, the MMVM achieved the lowest value for the unwrapped phase in the non-deformed areas, representing a reduction of 56.4% compared to the original value. Finally, 32 landslides were identified, 16 of which posed a threat to nearby villages. The experimental results demonstrate the superiority of the proposed method and provide support to disaster investigation departments.

Assessing Slip Rates on the Xianshuihe Fault Using InSAR with Emphasis on Phase Unwrapping Error and Atmospheric Delay Corrections

Located on the southeastern periphery of the Tibetan Plateau, the Xianshuihe fault (XSHF) is an active left-lateral strike-slip fault renowned for its frequent and intensive seismic activities. This highlights the necessity of employing advanced geodetic methodologies to precisely evaluate the fault kinematics and seismic hazard potential along this fault. Among these techniques, interferometric synthetic aperture radar (InSAR) stands out for its high spatial resolution and regular revisit intervals, enabling accurate mapping of interseismic deformation associated with fault motion. However, the precision of InSAR in measuring deformation encounters several challenges, particularly artifacts stemming from phase unwrapping errors and atmospheric phase delays. In this study, we utilize ascending and descending Sentinel-1 InSAR images spanning from January 2017 to January 2023 to drive the line-of-sight (LOS) mean crustal velocities associated with the XSHF with emphasis on phase unwrapping errors and atmospheric delay corrections. Then, the reliability of the derived LOS velocities is assessed using independent observations from the Global Navigation Satellite System (GNSS). The inferred fault slip rate along the XSHF shows significant along-strike variations, gradually decreasing from ~11.1 mm/yr at the Luhuo section to ~6.6 mm/yr at the Kangding section and then sharply increasing to ~13.0 mm/yr towards its eastern terminus at the Moxi section. The fault locking depth shows similar along-strike variations, decreasing from ~19.5 km in the northwestern part to ~4.8 km at the Kangding section, before increasing to 19.6 km at the Moxi segment. Notably, apparent surface fault creeping, characterized by a slip rate of ~2.7 mm/yr, is observed at the Kangding segment, likely resulting from postseismic slip following the 2014 Mw 6.3 Kangding earthquake.

Implementation and testing of single point positioning on IRNSS/NavIC receiver data

SummaryIndia has developed its own regional navigation satellite system called the Indian Regional Navigation Satellite System (IRNSS), also known as Navigation with Indian Constellation (NavIC). IRNSS/NavIC provides standard positioning and timing services for civilians and restricted services for authorized users. In this paper, single point positioning is implemented on the NavIC receiver data. Estimation techniques of the least squares method and extended Kalman filter are implemented after applying broadcast‐based clock corrections, orbit computation, relative time correction, and ionosphere delay correction for single and dual frequency. A data extraction and storage module is developed to store computed parameters at every intermediate stage in separate files. This is followed by an error plotter module to plot the extracted parameters for each satellite and to separately analyze their performance and the obtained errors. The estimated receiver position is visualized on Google Earth. The absolute error in both estimation techniques ranges between 5 and 15 m throughout the period.

Saastamoinen and Marini-Murray's Study of the Tropospheric Delay Model in Shijiazhuang

In addition to the non-dispertic medium, the tropospheric medium changes with time, and it is difficult to use a simple and unified mathematical model to determine the degree of change, especially in the long baseline relative positioning, the tropospheric delay between two stations often shows a weak spatial correlation. In this paper, this study will briefly introduce the main methods to reduce or eliminate various kinds of errors in order to build a real-time tropospheric delay model. The main body of this paper is to introduce and discuss the existing direct calculation model and mapping function of tropospheric delay. Based on the meteorological data of Shijiazhuang meteorological station provided by the official website of China Meteorological Administration, the appropriate tropospheric delay correction model and projection function (Saastamoinen +Marini-Murray model) are selected. The hourly tropospheric delay correction model along the propagation path direction in Shijiazhuang city on April 1, 2023 is established, and the accuracy and reliability of the model are analyzed.

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.

Analyzing the spatial variation characteristics of grid TEC using long-term GIM data

Global ionosphere maps (GIM), a grid model generated based on observations from global GNSS stations, is often used for ionospheric delay correction and total electron content (TEC) analysis, and is an important data for ionospheric modeling and code bias estimation, as well as related applications. Understanding the spatial variation characteristics of the grid TEC in GIM data can help the better use of GIM data. In this paper, the GIM data of CODE (Center for Orbit Determination in Europe) analysis center for a total of 15 years from 2008 to 2022 are used to analyze the variation of TEC of adjacent grid points and the difference of TEC of four grid points in each grid. The results show that the mean values of the grid TEC variations range from about 0.26–1.86 TECu in the longitude direction and 0.36–2.76 TECu in the latitude direction, and most of the differences in the TECs of neighboring grid points are within ± 2 TECu. In the low solar activity years, the STD of the TEC of the four grid points in most of the grids in the GIM data is less than 1.2 TECu, indicating that the TEC values of the four grid points in most of the grids in their GIMs are relatively close to each other and the differences are small, whereas the differences are larger in the high solar activity years. In other words, the TEC values of the areas covered by the GIM grids in the low solar activity years are close. The results of the study can provide some references and lessons for the interpolation and application of the TEC of the GIM grid, and the TEC modeling of the single-station area.

Single-voxel delay map from long-axial field-of-view PET scans.

We present an algorithm to estimate the delay between a tissue time-activity curve and a blood input curve at a single-voxel level tested on whole-body data from a long-axial field-of-view scanner with tracers of different noise characteristics. Whole-body scans of 15 patients divided equally among three tracers, namely [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, which were used in development and testing of the algorithm. Delay times were estimated by fitting the cumulatively summed input function and tissue time-activity curve with special considerations for noise. To evaluate the performance of the algorithm, it was compared against two other algorithms also commonly applied in delay estimation: name cross-correlation and a one-tissue compartment model with incorporated delay. All algorithms were tested on both synthetic time-activity curves produced with the one-tissue compartment model with increasing levels of noise and delays between the tissue activity curve and the blood input curve. Whole-body delay maps were also calculated for each of the three tracers with data acquired on a long-axial field-of-view scanner with high time resolution. Our proposed model performs better for low signal-to-noise ratio time-activity curves compared to both cross-correlation and the one-tissue compartment models for non-[15O]H2O tracers. Testing on synthetically produced time-activity curves showed only a small and even residual delay, while the one-tissue compartment model with included delay showed varying residual delays. The algorithm is robust to noise and proves applicable on a range of tracers as tested on [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, and hence is a viable option offering the ability for delay correction across various organs and tracers in use with kinetic modeling.

A Two-Step Regional Ionospheric Modeling Approach for PPP-RTK.

In the precise point positioning/real-time kinematic (PPP-RTK) technique, high-precision ionospheric delay correction information is an important prerequisite for rapid PPP convergence. The commonly used ionospheric modeling approaches in the PPP-RTKs only take the trend term of the ionospheric total electron content (TEC) variations into account. As a result, the residual ionospheric delay still affects the positioning solutions. In this study, we propose a two-step regional ionospheric modeling approach that involves combining a polynomial fitting model (PFM) and a Kriging interpolation (KI) model. In the first step, a polynomial fitting method is used to model the trend term of the ionospheric TEC variations. In the second step, a KI method is used to compensate for the residual term of the ionospheric TEC variations. Datasets collected from continuously operating reference stations (CORSs) in Hunan Province, China, are used to validate the PFM/KI method by comparing with a single PFM method and a combined PFM and inverse distance weighting interpolation (IDWI) method. The experimental results show that the two-step PFM/KI modeled ionospheric delay achieves an average root mean square (RMS) error of 1.8 cm, which is improved by about 48% and 23% when compared with the PFM and PFM/IDWI methods, respectively. Regarding the positioning performance, the PPP-RTK with the PFM/KI method takes an average of 1.8 min or 4.0 min to converge to a positioning accuracy of 1.3 cm or 2.5 cm in the horizontal and vertical directions, respectively. The convergence times are decreased by about 18% and 14% in the horizontal direction and 9% and 5% in the vertical direction over the PFM and the PFM/IDWI methods, respectively.

High-Temporal-Resolution Kinetic Modeling of Lung Tumors with Dual-Blood Input Function Using Total-Body Dynamic PET.

The lungs are supplied by both the pulmonary arteries carrying deoxygenated blood originating from the right ventricle and the bronchial arteries carrying oxygenated blood downstream from the left ventricle. However, this effect of dual blood supply has never been investigated using PET, partially because the temporal resolution of conventional dynamic PET scans is limited. The advent of PET scanners with a long axial field of view, such as the uEXPLORER total-body PET/CT system, permits dynamic imaging with high temporal resolution (HTR). In this work, we modeled the dual-blood input function (DBIF) and studied its impact on the kinetic quantification of normal lung tissue and lung tumors using HTR dynamic PET imaging. Methods: Thirteen healthy subjects and 6 cancer subjects with lung tumors underwent a dynamic 18F-FDG scan with the uEXPLORER for 1 h. Data were reconstructed into dynamic frames of 1 s in the early phase. Regional time-activity curves of lung tissue and tumors were analyzed using a 2-tissue compartmental model with 3 different input functions: the right ventricle input function, left ventricle input function, and proposed DBIF, all with time delay and dispersion corrections. These models were compared for time-activity curve fitting quality using the corrected Akaike information criterion and for differentiating lung tumors from lung tissue using the Mann-Whitney U test. Voxelwise multiparametric images by the DBIF model were further generated to verify the regional kinetic analysis. Results: The effect of dual blood supply was pronounced in the high-temporal-resolution time-activity curves of lung tumors. The DBIF model achieved better time-activity curve fitting than the other 2 single-input models according to the corrected Akaike information criterion. The estimated fraction of left ventricle input was low in normal lung tissue of healthy subjects but much higher in lung tumors (∼0.04 vs. ∼0.3, P < 0.0003). The DBIF model also showed better robustness in the difference in 18F-FDG net influx rate [Formula: see text] and delivery rate [Formula: see text] between lung tumors and normal lung tissue. Multiparametric imaging with the DBIF model further confirmed the differences in tracer kinetics between normal lung tissue and lung tumors. Conclusion: The effect of dual blood supply in the lungs was demonstrated using HTR dynamic imaging and compartmental modeling with the proposed DBIF model. The effect was small in lung tissue but nonnegligible in lung tumors. HTR dynamic imaging with total-body PET can offer a sensitive tool for investigating lung diseases.

Quantitative analysis of the tropospheric delay differences for 1–300 GHz microwave signals with ground-based microwave radiometric profiles

Tropospheric delay is a significant source of error in various microwave measurement technologies. As the tropospheric delay differences associated with the signal frequency can be neglected for signals near the L band, the Global Navigation Satellite System (GNSS) is frequently used to assist other microwave measurements on tropospheric delay correction for its high precision and all-weather capabilities. However, when the signal frequency differences are significant, further analysis of tropospheric delay related to signal frequency is required for higher accuracy in long-distance propagation. This study quantitatively analyzed the tropospheric delay differences from L-band to millimeter band (1–300 GHz) affected by dry air, water vapor, and liquid water, based on Microwave Radiometer (MWR) measurement profiles and the Millimeter-wave Propagation Model93 (MPM93) at Shanghai. Relative to the GNSS L-band signal of 1.2 GHz, the RMS of tropospheric delay differences over 1–300 GHz varies from 0.033 mm to 9.436 mm, with the maximum tropospheric delay differences up to 18.03 mm at 300 GHz. Tropospheric delay differences strongly correlate with surface temperature and surface pressure, except for some of the atmosphere absorption bands. These quantitative analyses are helpful for the comprehensive application of GNSS to assist other microwave measurement techniques.

Mitigation of tropospheric delay induced errors in TS-InSAR ground deformation monitoring

ABSTRACT Interferometric Synthetic Aperture Radar (InSAR) is capable of detecting crust deformation. However, the accuracy is limited by spatiotemporal changes in the lower troposphere. In this paper, we constructed a periodic zenith total delay negative exponential function (PZTD-NEF) model of atmospheric spatiotemporal variation characteristics based on ERA-5 data to alleviate the temporal oscillation bias introduced by tropospheric delay and improve the accuracy of time series InSAR (TS-InSAR) inversion of surface deformation. We evaluated the model’s performance using the phase standard deviation (STD), atmospheric delay correlation coefficient with topography and the spatial structure function. The results were compared with a linear topography-dependent empirical model, generic atmospheric correction online service (GACOS) and ERA-5 methods. Our method reduces the STD of the phase of 83% of the interferograms by 12.8%. For vertical stratification delay correction, the correlation between the proposed method and the Linear, GACOS, and ERA-5 reached 0.734, 0.708, and 0.729, respectively. We found that accounting for spatiotemporal variation characteristics of tropospheric delay can alleviate the seasonal oscillations of vertical stratification delay and improve the accuracy of the deformation time series solution by 40.04%. We also used the Kunming continuous operation reference station system (KMCORS) to verify the displacement results of our method.

Assessment of the three representative empirical models for zenith tropospheric delay (ZTD) using the CMONOC data

The precise correction of atmospheric zenith tropospheric delay (ZTD) is significant for the Global Navigation Satellite System (GNSS) performance regarding positioning accuracy and convergence time. In the past decades, many empirical ZTD models based on whether the gridded or scattered ZTD products have been proposed and widely used in the GNSS positioning applications. But there is no comprehensive evaluation of these models for the whole China region, which features complicated topography and climate. In this study, we completely assess the typical empirical models, the IGGtropSH model (gridded, non-meteorology), the SHAtropE model (scattered, non-meteorology), and the GPT3 model (gridded, meteorology) using the Crustal Movement Observation Network of China (CMONOC) network. In general, the results show that the three models share consistent performance with RMSE/bias of 37.45/1.63, 37.13/2.20, and 38.27/1.34 mm for the GPT3, SHAtropE and IGGtropSH model, respectively. However, the models had a distinct performance regarding geographical distribution, elevation, seasonal variations, and daily variation. In the southeastern region of China, RMSE values are around 50 mm, which are much higher than that in the western region, approximately 20 mm. The SHAtropE model exhibits better performance for areas with large variations in elevation. The GPT3 model and the IGGtropSH model are more stable across different months, and the SHAtropE model based on the GNSS data exhibits superior performance across various UTC epochs.