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Abstract

Fractal analysis of VLF electric field data obtained by using vertical antenna located at Chaumuhan, Mathura station (Lat., 27.5° N, Long., 72.68° E) has been carried out using Higuchi method for investigating the impact of moderate shallow earthquakes (M = 4.9–5.6, depth 4.44–16.7 Km) that occurred during February 1, 2016 to October 31, 2016 (excluding April 2016) on the fractal dimension of VLF data. The results of the analysis show that daily values of fractal dimension vary much above and below the monthly mean during the period of observations, 1–30 days before and 1–30 days after the onset of the quakes considered in the present study. The ranges of reductions and enhancements in fractal dimension from the monthly mean are 0.05–0.33 and 0.054–0.43 respectively while the percentage ranges of reductions and enhancements in its daily variation are 3.0–23.21 and 2.81–19.88% respectively. The observed variations in fractal dimension have also been studied in the light of other expected sources like, magnetic storms, lightning activity, local building noises, and instrumental errors which may affect the fractal dimension of the VLF data. It is noticed that the observed variations of fractal dimensions do not correspond to these spurious sources considered. Further, model describing the genesis of VLF emissions in preparatory zones of the impending seismic events and their mechanism of transmission to the observing station have also been discussed.

Abstract

Satellite navigation systems are used for positioning purposes, however to calculate an accurate position, it is crucial to take into account all possible sources of error. The Ionosphere is the primary cause of the positional error. There is a lot of research into first-order ionospheric error estimation and removal. Due to the growing demand for positioning precision across a wide range of applications, significant research has been done over the last two decades to ascertain the impact of second-order ionospheric error (SOIE). However, very less research has been identified that examines the relationship between SOIE and the receiver’s geographic location and total electron content (TEC). Achieving the desired millimeter/centimeter level positional accuracy in these regions requires the study of a realistic diurnal and seasonal variability of SOIE because the behavior of ionospheric TEC in equatorial and low-latitude regions (Indian region in this case) is highly dynamic. Additionally, NavIC (Navigation with Indian Constellation), an Indian satellite navigation system, uses carrier frequencies, namely L5 and new frequency S1, as opposed to GPS L1 and L2, which presents a fresh chance to investigate the effects of SOIE on these frequencies. This research may serve as a benchmark for systems like NavIC that are using L5 and new S-band frequencies for satellite signal transmission, space weather monitoring, and ionosphere abnormalities research. To comprehend various elements of its seasonal properties, this research estimates and analyses SOIE. Data from the SOIE were examined for 12 months, from May 2018 to February 2019, to analyze the diurnal and seasonal fluctuation. It has been noted that seasonal and diurnal fluctuations have a substantial impact on the SOIE. In comparison to the winter months, the SOIE levels are higher in the summer and equinoctial months. Although the SOIE peak levels are similar during the equinoctial and summer months, a higher midnight value and a slowly declining rate have been noted. At L5 frequency, there is a significant seasonal fluctuation in SOIE (–1.1 to –2.84 cm), whereas at S1 shows just a little seasonal variation (–0.1 to –0.3 cm) throughout the year. Additionally, geostationary orbit (GEO) satellites are discovered to be more suitable for the analysis of SOIE than satellites in geosynchronous orbit (GSO), and they might also be employed for ionospheric studies.

The vast Antarctic Ice Sheet holds more than half of Earth's freshwater. In several places around the continent, the ice extends over the ocean, where it forms large floating shelves. Observations suggest many of these ice shelves are thinning as they melt from below, with implications for ocean dynamics, global sea level, and Earth's climate.

Minerals underground may be part of the solution to global climate change. The most famous greenhouse gas, carbon dioxide (CO2), can react with some minerals found deep underground to form stable carbonates—permanently storing the CO2. This storage mechanism has helped naturally regulate CO2 throughout Earth's history.

Marine and coastal geoscience play a critical role in understanding ancient and modern geological history, offshore and coastal hazards, and climate change. Deep-water environments prevent scientists from directly visiting field sites, so unique methods must be employed for researching the ocean floor.

To transition to a green economy, we require more critical metals such as copper, rare earth elements and cobalt than are currently available. Therefore, we need to find new resources formed in different ways in areas that have not yet been explored.

A new study has unraveled the hidden mechanics of how earthquakes ignite, shedding light on the mysterious transition from quiet, creeping motion to the violent ruptures that shake the Earth.

A trio of Earth and atmospheric scientists at the University of Houston, working with a geoscientist from the University of Arizona, has found evidence that a geographic hotspot laid the groundwork for the formation of the Great Lakes.

The summer of 2023 saw a surprising increase in global temperatures, even within the context of the ongoing greenhouse gas-driven warming trend. Many scientists were flummoxed. Their simulations didn't show this kind of spike.

Abstract

Terrestrial scale is one of the key datum parameters in the realization of the International Terrestrial Reference Frame (ITRF), which is defined by Very Long Baseline Interferometry (VLBI) and Satellite Laser Ranging (SLR) in the latest ITRF2020. Currently, the scale of GNSS is aligned to ITRF by estimating phase center offsets (PCOs) of GNSS satellites in a global adjustment with minimum constraints to ITRF. With the proposal of space tie concept in recent years, the co-location of different techniques on the same Low Earth Orbit (LEO) spacecraft provides a possible alternative to achieve this scale datum transfer between different techniques. In this study, we investigate the potential of terrestrial scale transfer between GNSS and SLR using the co-location onboard LEO satellites. The integrated precise orbit determination of GNSS and LEO satellites is performed based on one year of onboard GPS data and SLR observations from GRACE-FO and Swarm satellites as well as a global GNSS network. Two GNSS-only solutions and four GNSS + SLR combined solutions are generated. The results indicate that the scale determined by LEO gravitational constraint in the GNSS-only solution presents an average offset of -0.35 ppb w.r.t. ITRF2020. The space-ties onboard LEO satellites fail to transfer SLR scale information to GNSS network. With the inclusion of SLR observations to LEO satellites, the scale factor of the combined solution is only changed by less than 0.05 ppb with respect to the GNSS-only solution. The small changes of a few millimeters in GPS PCO of the orbital radial direction for the combined solution also demonstrate that the GPS z-PCOs cannot inherit any SLR scale information through LEO co-locations. Meanwhile, we find that the range biases of GRACE-FO and Swarm satellites achieve a good consistency for the majority of SLR stations, since these satellites carry the same type of laser retroreflector arrays and can achieve comparable orbit accuracy. The result indicates that estimating a common range bias parameter is sufficient for GRACE-FO and Swarm when using the SLR observations from these satellites.

Nature Geoscience, Published online: 08 January 2025; doi:10.1038/s41561-024-01617-9

Multiple factors, including slab geometry and upper-plate stress state, determine the variation in slip behaviour along most megathrusts, according to a synthesis of observations of the Alaska, Hikurangi and Nankai subduction zones.

Since the establishment of Indonesia's new capital, Nusantara (IKN), hydroclimate extremes have emerged as a significant environmental concern. One of the most notable events was the devastating flash flood on March 15–16, 2022, which was triggered by 4–6 hours of prolonged heavy rainfall, causing severe damage and substantial economic loss.

Г. Лойхс

Author(s): Jarett LeVan and Scott D. Baalrud

The intrinsic bulk viscosity of the one-component plasma (OCP) is computed and analyzed using equilibrium molecular dynamics simulations and the Green-Kubo formalism. It is found that bulk viscosity exhibits a maximum at Γ≈1, corresponding to the condition that the average kinetic energy of particle…

[Phys. Rev. E 111, 015202] Published Tue Jan 07, 2025

Abstract

Vehicular attitude can be estimated using micro-electro-mechanical systems (MEMS) based magnetic, angular rate, and gravity (MARG) sensors or global navigation satellite systems (GNSS). In challenging environments external accelerations, magnetic distortions, and failure of GNSS will result in significant attitude estimation errors. We proposed a hybrid attitude estimation algorithm based on the low-cost dual-antenna GNSS/MEMS MARG sensor integration, in which the two GNSS antennas are connected to two separate low-cost receivers. Heading and pitch angles are obtained from the moving baseline spanned by the two antennas. An error state Kalman filter is built for data fusion, the filter shares the identical kinematic model but switches the measurement model according to the valid aiding sources. Six possible measurement update schemes are conditioned on the availability of GNSS-derived angles and the disturbances detected in the MARG sensor data. The accuracy degradation of attitude estimation caused by disturbances is alleviated by adjusting the measurement covariance matrix adaptively. A land vehicle-based dynamic experiment was performed to assess the proposed algorithm. Compared to the MARG sensor alone method, the root mean square errors of the proposed GNSS/MARG sensor integrated method were reduced by 38.9%, 65.8%, and 45.6% in the roll, pitch, and yaw angles, respectively.

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.

Author(s): M. J. May, G. E. Kemp, J. D. Colvin, R. Benjamin, D. Liedahl, T. Fears, S. Kucheyev, P. L. Poole, K. Widmann, and B. E. Blue

Shooting a laser pulse at a porous silver target generates more intense x rays than previous targets, which will help studies of matter in extreme conditions.

[Phys. Rev. E 111, 015201] Published Fri Jan 03, 2025