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Abstract

Atmospheric weighted mean temperature (Tm) is pivotal for converting zenith wet delay (ZWD) derived from global navigation satellite system (GNSS) signal to precipitable water vapor (PWV). Currently, most Tm models are developed based on radiosonde (RS) data or reanalysis data. These models are limited by the availability of measured surface temperature and the accuracy of input data used during modeling. Additionally, they face challenges in accounting for the diurnal effect on Tm. In this study, we innovatively use the latest AI weather model, Pangu-Weather, to estimate surface air temperature (Pangu-Ts) in 2016–2019. Compared with the measured surface temperature (RS-Ts) at RS stations, bias and root mean square error (RMSE) are − 0.75 K and 2.54 K, respectively. Subsequently, a Tm forecast model (RF-Tm) in China is developed based on this data using random forest (RF). The model only requires the input of time, 3D coordinates of stations, and predicted Pangu-Ts data to yield the forecasted Tm estimates. The validation results based on RS data show that bias of the RF-Tm is − 0.38 K and the RMSE is 2.47 K. Through comparison and validation with the Bevis model, GPT3, and the Tm forecast model (BP-Tm) developed using back propagation neural network (BPNN), RF-Tm demonstrates reductions in RMSE of 41.33%, 39.46%, and 2.76%, respectively. The mean values with theoretical RMSE and relative error of PWV derived from the RF-Tm are 0.171 mm and 0.90%. The RF-Tm proposed in this study can provide reliable and high-precision Tm estimation for real-time GNSS PWV retrieval.

Abstract

Jamming of Global Navigation Satellite System (GNSS) signals severely affects the security of critical infrastructures and applications. The localization of intentional jamming sources, jammers, is an important step in securing GNSS resilience as it provides the authorities with technical tools to prevent the jamming. However, jammers are difficult to localize in dense urban areas because the existence of multipath and non-line-of-sight propagation challenges conventional methods significantly. This challenge has not been comprehensively addressed in previous research. Motivated by this gap, a ray-tracing tool using 3-D city models is established to simulate jamming signal propagation with high precision and thereby augment the existing signal simulators, and measurements for localization are modeled by characterizing a commercial GNSS receiver under jamming conditions. Then, we propose a novel two-step strategy which consists of an ensemble subspace k-Nearest-Neighbor (KNN) as a raw-predictor and an improved gravitational searching algorithm (GSA) as a fine-optimizer. Based on this, two cloud-computing-based schemes using signal-matching and joint-localization in fine-optimizing stage are proposed. Finally, the proposed methods are evaluated in three typical urban areas, and their effectiveness and superiority over conventional least-squares method based on an empirical path-loss model are validated.

Abstract

Physics-informed neural networks (PINNs) provide a flexible and effective alternative for estimating seismic wavefield solutions due to their typical mesh-free and unsupervised features. However, their accuracy and training cost restrict their applicability. To address these issues, we propose a novel initialization for PINNs based on meta-learning to enhance their performance. In our framework, we first utilize meta-learning to train a common network initialization for a distribution of medium parameters (i.e., velocity models). This phase employs a unique training data container, comprising a support set and a query set. We use a dual-loop approach, optimizing network parameters through a bidirectional gradient update from the support set to the query set. Following this, we use the meta-trained PINN model as the initial model for a regular PINN training for a new velocity model, where the optimization of the network is jointly constrained by the physical and regularization losses. Numerical results demonstrate that, compared to the vanilla PINN with random initialization, our method achieves a much faster convergence speed, and also obtains a significant improvement in the results accuracy. Meanwhile, we showcase that our method can be integrated with existing optimal techniques to further enhance its performance.

Abstract

The development of optical clocks has experienced significant acceleration in recent years, positioning them as one of the most promising quantum optical sensors for next-generation gravimetric missions (NGGMs). This study investigates the feasibility of retrieving the temporal gravity field via improved optical clocks through a closed-loop simulation. It evaluates optical clock capabilities in temporal gravity field inversion by considering the clock noise characteristics, designing satellite formations, and simulating the performance of optical clocks. The results indicate that optical clocks exhibit higher sensitivity to low-degree gravity field signals. However, when the optical clock noise level drops below 1 × 10−19 \(/\sqrt{\uptau }\) (τ being the averaging time in seconds) in the satellite-to-ground (SG) mode or below 1 × 10−20 \(/\sqrt{\uptau }\) in the satellite-to-satellite (SS) mode, atmospheric and oceanic (AO) errors become the dominant source of error. At this noise level, optical clocks can detect time-variable gravity signals up to approximately degree 30. Compared to existing gravity measurement missions such as GRACE-FO, optical clocks exhibit consistent results in detecting signals below degree 20. If the orbital altitude is reduced to 250 km, the performance of optical clocks across all degrees aligns with the results of GRACE-FO. Furthermore, the study reveals that lowering the orbital altitude of satellite-based optical clocks from 485 to 250 km improves results by an average of 33%. Switching from the SS mode to the SG mode results in an average improvement of 51%, while each order-of-magnitude improvement in clock precision enhances results by an average of 60%. In summary, these findings highlight the tremendous potential of optical clock technology in determining Earth’s temporal gravity field and provide crucial technological support for NGGMs.

Abstract

The accuracy of satellite clock bias (SCB) directly affects the precision and reliability of positioning in Global Navigation Satellite System. Through precise clock bias prediction, positioning errors can be effectively reduced, and the overall reliability of the system can be improved. This paper proposes a deep learning model for SCB prediction based on the fusion of the Beluga Whale Optimization (BWO), Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an attention mechanism. The CNN is utilized to extract the spatiotemporal characteristic information from the clock bias sequence, while the BiGRU fully extracts relevant features through forward and backward propagation. The introduction of an attention mechanism aims to preserve essential features within the clock bias sequence to enhance feature extraction by both CNN and BiGRU networks. Additionally, the BWO is employed to optimize parameter selection in order to improve model accuracy. Experimental verification demonstrates that for the BeiDou Navigation Satellite System’s hydrogen-maser atomic clocks, the predicted clock bias for 6 h, 3 days, and 15 days are 0.078 ns, 0.475 ns, and 2.130 ns respectively, superior to the CNN-BiGRU-Attention, CNN-BiGRU, BiGRU, GRU, LSTM, BP, Kalman filter and ARIMA models.

On January 1st, 2024, a moment magnitude (Mw) 7.5 earthquake occurred on an active reverse fault in the northern part of Noto Peninsula, being one of the largest intraplate events recorded in Japan. In previous s...

Abstract

In this paper, we review the theoretical studies of the electromagnetic and other non-seismic phenomena accompanying earthquakes. This field of geophysical research is at the interception of several sciences: electrodynamics, solid-state physics, fracture mechanics, seismology, acoustic-gravity waves, magnetohydrodynamics, ionospheric plasma, etc. In order to make physics of these phenomena as transparent as possible, we use a simplified way of deriving some theoretical results and restrict our analysis to order-of-magnitude estimates. The main emphasis is on those theoretical models which give not only a qualitative, but also a quantitative, description of the observed phenomena. After some introductory material, the review is begun with an analysis of the causes of local changes in the rock conductivity occasionally observed before earthquake occurrence. The mechanisms of electrical conductivity in dry and wet rocks, including the electrokinetic effect, are discussed here. In the next section, the theories explaining the generation of low-frequency electromagnetic perturbations resulting from the rock fracture are covered. Two possible mechanisms of the coseismic electromagnetic response to the propagation of seismic waves are studied theoretically. Hereafter, we deal with atmospheric phenomena, which can be related to seismic events. Here we discuss models describing the effect of pre-seismic changes in radon activity on atmospheric conductivity and examine hypotheses explaining abnormal changes in the atmospheric electric field and in infrared radiation from the Earth, which are occasionally observed on Earth and from space over seismically active regions. In the next section, we review several physical mechanisms of ionospheric perturbations associated with seismic activity. Among them are acoustic-gravity waves resulting from the propagation of seismic waves and tsunamis and ionospheric perturbations caused by vertical acoustic resonance in the atmosphere. In the remainder of this paper, we discuss whether variations in radon activity and vertical seismogenic currents in the atmosphere can affect the ionosphere.

Abstract

Plasma flows with enhanced dynamic pressure, known as magnetosheath jets, are often found downstream of collisionless shocks. As they propagate through the magnetosheath, they interact with the surrounding plasma, shaping its properties, and potentially becoming geoeffective upon reaching the magnetopause. In recent years (since 2016), new research has produced vital results that have significantly enhanced our understanding on many aspects of jets. In this review, we summarise and discuss these findings. Spacecraft and ground-based observations, as well as global and local simulations, have contributed greatly to our understanding of the causes and effects of magnetosheath jets. First, we discuss recent findings on jet occurrence and formation, including in other planetary environments. New insights into jet properties and evolution are then examined using observations and simulations. Finally, we review the impact of jets upon interaction with the magnetopause and subsequent consequences for the magnetosphere-ionosphere system. We conclude with an outlook and assessment on future challenges. This includes an overview on future space missions that may prove crucial in tackling the outstanding open questions on jets in the terrestrial magnetosheath as well as other planetary and shock environments.

Abstract

Lateral inhomogeneity in the Earth’s mantle affects the tidal response. The current study reformulates the expressions for estimating the lateral inhomogeneity effects on tidal gravity with respect to the unperturbed Earth and supplements some critical derivation process to enhance the methodology. The effects of lateral inhomogeneity are calculated using several real Earth models. By considering the collective contributions of seismic wave velocity disturbances and density disturbance, the global theoretical changes of semidiurnal gravimetric factor are obtained, which vary from − 0.22 to 0.22% compared to those in a layered Earth model, about 1/2 of the ellipticity’s effect. The gravity changes caused by lateral-inhomogeneous disturbances are also computed and turn out to be up to 0.16% compared to the changes caused by tide-generating potential. The current study compares the influences of lateral inhomogeneity with rotation and ocean tide loading. The results indicate that the rotation and ellipticity on tidal gravity are the most dominant factors, the ocean tide loading is the moderate one, and the lateral inhomogeneity in the mantle has the least significant influence. Moreover, an anti-correlation between the effective elastic thickness and gravimetric factor change caused by lateral inhomogeneity is found, implying that it is difficult to generate tidal response at regions with high rigidity. We argue that the gravimetric factor change can be used as an effective indicator for lithospheric strength.

Abstract

Currently, the susceptibility of Global Navigation Satellite System (GNSS) signals underscores the importance of accurate GNSS time spoofing detection as a critical research area. Traditional spoofing detection methods have limitations in applicability, while the current learning-based algorithms are only applicable to the judgment of collected data, which is difficult to apply to real-time detection. In this paper, a real-time spoofing detection framework based on feature processing is proposed. The approach involves feature integration and correlation coefficient screening on each epoch of multi-satellite data. Additionally, special standardization strategy is employed to enhance the feasibility of real-time application. In the experimental phase, apart from utilizing the open dataset, an experimental platform is developed to generate dual-system data for experimentation purposes. Compared with the traditional clock difference detection method, this algorithm improves the detection performance by about 25%. Furthermore, the framework proposed can improve the detection F1 score of basic machine learning models and greatly reduce the computation time by more than ten times. On most datasets, models incorporating the framework achieved F1 scores of more than 99% and average response times of less than 10 μs. In summary, this study provides an effective intelligent solution for the application of real-time receiver spoofing detection.

Abstract

The regional sea level budget and interannual sea level changes around Taiwan and Philippines are studied using altimetry, GRACE, and in-situ hydrographic data during 1993‒2021. Results show that the average sea level trend around Taiwan and Philippines during 1993–2021 derived from the altimetric data is 3.6 ± 0.2 mm/yr. Over 2002–2021, the study shows closure of sea level budget in the eastern ocean of Taiwan and Philippines within the observed data uncertainties, and the ocean mass accounts for 88%–100% of the observed sea level rise. In contrast, the sea level budget is not closed in the western ocean of Taiwan and Philippines, probably due to the lack of complete coverage by in-situ ocean observing systems. In addition, both regional sea level anomalies and their steric component around Taiwan and Philippines exhibit pronounced interannual and decadal variabilities. The trade wind stress associated with El Niño–Southern Oscillation and Pacific Decadal Oscillation offers a compelling explanation for the interannual and decadal signals of sea level anomalies in the southern ocean of Taiwan, with negative correlations of − 0.78 to − 0.64, indicating that trade wind stress makes a negative contribution to interannual-to-decadal sea level variability. In the northwestern ocean of Taiwan, the sea level variation is strongly influenced by the local monsoon system and shallow bathymetry with an annual amplitude of 90.3 ± 2.9 mm, larger than those in other regions around Taiwan and Philippines, where ocean mass is dominant with a high correlation with the sea level (+ 0.75 to + 0.78).

Abstract

The development of Global Navigation Satellite Systems (GNSS) results in large spatial geodetic networks with a distinct range of accuracy. Thus, classification of the GNSS stations is needed to determine which stations are appropriate for geodetic applications. Additionally, advanced Machine Learning (ML) techniques have been proposed. However, ML algorithms may sometimes be less sensitive due to a lack of samples or anomalies in input data. Therefore, this study introduces an approach in which human-based supervision is integrated into ML processes to improve the ML model’s performance in classifying the continuous GNSS stations. The human factor influences the ML processes through two sampling strategies: “suggest-decide” and “correct-retrain”, where the accuracy of ML models will be improved via human-based corrections. The idea is that the unsupervised ML-based clustering techniques are driven by human-based supervision to create samples for training the supervised ML-based classification models. In this study, we develop a MATLAB app to automate the clustering and labeling processes. Our finding demonstrates that applying these sampling strategies can enhance the accuracy of the ML-based classification models from under 50 % up to \(\sim\) 99 % after re-training. Also, this study categorizes almost 9000 continuous monitoring stations in the Nevada database, of which 1900 stations in Europe serve as samples for training the ML-based classification models. Furthermore, the methodologies developed in this study can be applied to warning systems, which utilize internal and external human resources to correct errors, address unusual situations, and provide timely feedback for better performance of ML-based forecasts.

Abstract

Displacement-based modal analysis has been proven to yield more robust and reliable modal parameter identification results compared to acceleration-based modal analysis. Global navigation satellite systems (GNSS) under real-time kinematic (RTK) mode is a widely used dynamic displacement monitoring technique. Notably, the monitoring accuracy of GNSS is limited due to the existence of multiple error sources such as multipath effect and satellite shielding effect. Particularly, blind source separation (BSS) can determine structural modal parameters from output-only responses. This method is advantageous compared with conventional modal analysis method because it does not require any prior knowledge of the structure. However, common BSS methodologies are susceptible to the local minima problem and are sensitive to low signal-to-noise ratio (SNR) signals. To address the aforementioned problems, this study first presents a combination filter strategy of Chebyshev and wavelet threshold (WT) to estimate the structural dynamic displacement based on GNSS RTK measurement. Then, a swarm-enhanced blind identification approach is proposed to determine structural modal parameters from the estimated displacement. The core of this approach is to develop a robust K-means clustering approach with swarm intelligence optimization to estimate the mixing matrix (i.e., mode shape matrix). Finally, the developed approach is verified in a four-degree-of-freedom numerical model and then implemented to a field test of a long-span cable-stayed bridge in engineering practice. The results illustrate that the designed combination filter can effectively weaken the influence of GNSS-RTK background noise while retaining the components related to structural dynamic vibration. Meanwhile, comparing with the conventional BSS approach (i.e., sparse component analysis), the developed swarm-enhanced blind identification approach exhibits higher robustness and convergence accuracy in determining structural modal parameters.

Abstract

The continued operation of missions such as Mars Express, Mars Reconnaissance Orbiter, and the ExoMars Trace Gas Orbiter has greatly enhanced our knowledge of seasonal processes on Mars. The most apparent evidence of the importance of seasons on Mars on the large scale is annual variation in the sizes of the Martian polar caps. However, high resolution imaging has also shown that seasonal forcing can lead to small-scale phenomena that are continuously changing the topography and the surface photometry. These phenomena often have no terrestrial analogue and involve complex interactions between seasonal ices, atmosphere, and substrate (surface and sub-surface). Although we now have better understanding of many of these processes (occasionally as a result of laboratory simulation), direct proof of some hypotheses remains elusive. We provide a brief review of the phenomena and list a series of open questions.

Abstract

Gravity forward modeling provides important high-resolution information for the development of global gravity models, and can also be applied in many studies, e.g., topographic/isostatic effects computation and Bouguer anomaly maps compilation. In this paper, we present efficient spectral forward modeling approaches in the spheroidal harmonic domain, based on a single layer with constant density or volumetric layers with laterally varying density. With the binomial series expansion applied in spheroidal harmonic gravity forward modeling, the computational cost of these approaches is much lower than similar approaches. In both layering cases, we derive topographic potential models up to degree and order (d/o) 2190 by applying the approaches proposed here. Our methodology is evaluated by comparing these outcome models with other similar topographic potential models derived from spherical harmonic solutions. We find that topographic potentials from spheroidal and spherical harmonic approaches are in great agreement. Finally, the model named EHFM_Earth_7200 with a maximum degree of 7200 was derived by a layer-based approach. The evaluations by ground-truth data show that EHFM_Earth_7200 improves GO_CONS_GCF_2_DIR_R6 by 4% over Antarctica, and improves EGM2008 by ~ 34% over northern Canada. A global map of Bouguer gravity anomaly was also compiled with EHFM_Earth_7200 and EGM2008. As the main conclusion of this work, the new model EHFM_Earth_7200 is beneficial for investigating and modeling the Earth’s external gravity field, the new approaches have comparable accuracy to spherical harmonic approaches and are more suitable for practical use with guaranteed convergence regions because they are performed in the spheroidal harmonic domain.

Abstract

Previous surveys using the remote sensing (RS) method revealed significant structures in the area of the Western Carpathians. It has not yet been possible to verify and explain the results of these surveys, even though all the phenomena are regional in nature and show many morphological features that clearly indicate recent activity and deformations, including current earthquake foci. The aim of the article was to verify these phenomena and compare them with new findings. A method of combining geomorphological data with satellite image analysis and verification using Global Navigation Satellite Systems (GNSS) and geophysics data was used. In this work, results are presented confirming the existence of a previously identified nonlinear structure—the "gravity nappe" in the western part of the Low Tatras, and the largest tectonic system Muráň—Malcov is analyzed in detail. Similar structures and tectonic zones, on a smaller scale, can also be found in other areas of the Carpathians. For example, the gravity structure in the Lesser Carpathians and the Ukrainian flysch Carpathians or the linear boundaries interpreted as tectonic systems—the Myjava-Subtatrans, Hron and Transgemerian tectonic zones. Recent movement trends have been confirmed by newly unified data from EUREF Permanent Network (EPN) stations and GNSS campaigns carried out in the last two decades in the given area. Both types of analyzed structures are directly related to the occurring foci of earthquakes.

Abstract

Numerical methods, like the finite element method (FEM) or finite volume method (FVM), are widely used to provide solutions in many boundary value problems. In previous studies, these numerical methods have also been applied in geodesy but demanded extensive computations because the upper boundary condition was usually set up at the satellite orbit level, hundreds of kilometers above the Earth. The relatively large distances between the lower boundary of the Earth's surface and the upper boundary exacerbate the computation loads because of the required discretization in between. Considering that many areas, such as the US, have uniformly distributed airborne gravity data just a few kilometers above the topography, we adapt the upper boundary from the satellite orbit level to the mean flight level of the airborne gravimetry. The significant decrease in the domain of solution dramatically reduces the large computation demand for FEM or FVM. This paper demonstrates the advantages of using FVM in the decreased domain in simulated and actual field cases in study areas of interest. In the simulated case, the FVM numerical results show that precision improvement of about an order of magnitude can be obtained when moving the upper boundary from 250 to 10 km, the upper altitude of the GRAV-D flights. A 2–3 cm level of accurate quasi-geoid model can be obtained for the actual datasets depending on different schemes used to model the topographic mass. In flat areas, the FVM solution can reach to about 1 cm precision, which is comparable with the counterparts from classical methods. The paper also demonstrates how to find the upper boundary if no airborne data are available. Finally, the numerical method provides a 3D discrete representation of the entire local gravity field instead of a surface solution, a (quasi) geoid model.

Abstract

For about three decades, the Global Navigation Satellite System (GNSS) has been used for high-precision positioning in scientific and engineering applications, such as deformation monitoring for seismicity and volcano eruption. Such high-precision positioning applications require millimeter-level positioning accuracy. There are many man-made and natural reflective surfaces near the GNSS receiving antennas. GNSS signals can be reflected and then arrive at the GNSS antenna. The multipath effect occurs when the direct signal is mixed with the reflected signal at the GNSS receiver. Choke-ring antennas are designed to mitigate the multipath effect of reflected signals from below the horizontal plane of the GNSS receiving antenna. Moreover, GNSS receiving antennas at network/permanent stations are usually installed on tall pillars or monuments to prevent multipath from “ground” reflected signals. However, part of the reflected signals can still arrive at the GNSS antenna center and cause multipath errors in GNSS measurements. How much can the multipath effect be on the real-time GNSS-measured displacements in studies on seismicity and volcano eruption? This work investigates the below-the-horizon multipath effect of choke-ring antennas on GNSS carrier-phase measurements. Here we show the differenced carrier-phase multipath errors of two commonly used GNSS antennas at the International GNSS Service (IGS) tracking stations can reach 8 mm, the maximum, with the mean and SD in a few millimeters at the 95% confidence level. The findings of this work should be applicable to other choke-ring antennas with similar architecture.

Abstract

The ambiguity resolution (AR) technology effectively accelerates convergence and improves precise point positioning (PPP) accuracy. Many observations involved in the calculation can enhance the accuracy of parameter estimation. Still, it can also introduce unmodeled errors, making it difficult to fix ambiguities, especially in multiple global navigation satellite system (GNSS). This paper presents a novel PPP Partial-AR (PAR) method to enhance Precise Point Positioning (PPP) performance by selecting an ambiguity subset based on the variation of ambiguity check factors, including the ratio, ambiguity dilution of precision (ADOP), and Bootstrapping success rate. The proposed method is validated using post-processing and real-time static and kinematic datasets across five GNSS integration modes involving the global positioning system (GPS) and BeiDou navigation satellite system (BDS), demonstrating that PPP Partial-AR (PAR) outperforms the method that fixes all ambiguities, known as PPP Full-AR (FAR). The static and kinematic post-processing experiment shows that PPP-PAR, compared with PPP-FAR, increases the ambiguity epoch fixing rate from 84.6 and 79.5% to 94.2 and 91.9%, decreases the time to first fix (TTFF) from 21.4 and 31.1 min to 17.7 and 25.4 min, and reduces the root mean square error (RMSE) from 12.7/11.3/32.2 and 21.7/19.4/51.8 mm to 11.5/10.3/30.9 and 19.1/18.1/48.9 mm in the north-east-up directions, respectively. The static and kinematic real-time experiment shows that PPP-PAR, compared with PPP-FAR, increases the ambiguity epoch fixing rate from 80.1 and 71.7% to 91.8 and 86.6%, decreases the TTFF from 29.5 and 35.5 min to 25.2 and 28.7 min, and reduces the RMS from 22.4/19.4/45.9 and 33.8/26.9/65.2 mm to 18.6/17.1/42.0 and 29.2/23.6/60.5 mm in the north-east-up directions, respectively. Moreover, the real-time experiments with actual kinematic data show that the proposed method significantly improves the ambiguity epoch fixing rate from 43.3% for PPP-FAR to 50.7% for PPP-PAR, and increases the positioning accuracy with an RMS value of 0.28/0.21/0.57 m for the PPP float solution, 0.23/0.19/0.55 m for the PPP-FAR solution towards 0.21/0.18/0.53 m for the PPP-PAR solution.