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Author(s): Ilnaz I. Fairushin and Anatolii V. Mokshin

In this paper, the theoretical model of weak decaying collective excitations characteristic of many-particle systems with long-range interaction potentials is developed using the example of one-component strongly coupled Yukawa plasmas. The proposed model is based on the self-consistent relaxation t…

[Phys. Rev. E 112, 015210] Published Wed Jul 23, 2025

SummaryThe stress and load path dependencies of elastic properties and their evolution under varying damage states is of critical interest to a multitude of communities, such as geophysicists understanding rock properties for subsurface engineering as well as both civil and geological engineers interested in fundamental damage mechanics of materials. Here, we perform a set of laboratory experiments on a Dakota Mahogany granite to understand the dependence of stress path, orientation, and magnitude on static and dynamic properties as well as dynamic evolution under varying states of damage. Localized strain and ultrasonic velocity, axial and radially aligned with respect to the sample, are recorded along four distinct load paths with varying ratios of mean and differential stress. Differential stress is found to be the predominant factor for variations in static Youngs modulus, while undamaged axial dynamic Youngs modulus is primarily a factor of increasing mean stress. Radial dynamic Young’s modulus demonstrates an overall positive correlation with increasing mean stress and negative correlation with differential stress. A novel relationship is constructed to predict phase velocity and orientation/polarization as a function of stress and load path. The effect of damage within the material is analyzed by subjecting the sample to increasing stresses along a single load path, after which the multipath testing is repeated. Ultrasonic velocity and thus dynamic moduli become less sensitive to increases in differential stress for wave propagation parallel with the maximum principle stress. For P-wave velocity aligned parallel, the contribution of differential stress decreases from nearly that of confining pressure (0.88) to below half at the highest damage state tested. Similar decreases also occur in the contribution of differential stress to the remaining three wave polarizations and orientations. This shows that the degradation of physical properties brought about by microcracking and subsequent decrease in velocity overcomes any increase resulting from consolidation with increasing stress. The results provide a way to anticipate changes in elastic response and subsurface acoustic velocity brought about by increased damage and changing stress state through the use of a new empirical model. Additional methods to establish the distribution of microcracks and their orientations within a damaged material through differences in velocity from loading to unloading are presented which provide useful tools for non-destructively assessing damage state.

Author(s): Masaru Nakanotani, Luis Lazcano Torres, Gary P. Zank, and Edward Thomas, Jr.

The Jeans instability in a magnetized dusty plasma is considered a fundamental process in space, where magnetic fields are common. We investigate the Jeans instability in a magnetized dusty plasma using 1D and 2D particle-in-cell simulations, in which dust grains are treated as particles and the Poi…

[Phys. Rev. E 112, 015208] Published Mon Jul 21, 2025

Author(s): T. A. Shelkovenko, I. N. Tilikin, A. R. Mingaleev, V. M. Romanova, and S. A. Pikuz

The small-sized, low-voltage, and low-inductive KING generator (190–230 kA, 40 kV, 200–240 ns) was specially designed to work with X-pinches; however, it was unstable in its original design. In the present work, it is experimentally shown that an increase in the inductance of the output node of the …

[Phys. Rev. E 112, 015207] Published Thu Jul 17, 2025

Abstract

To meet the high-precision and high-integrity positioning demands of safety–critical applications, monitoring the quality of precise satellite products in global navigation satellite system (GNSS) precise point positioning (PPP) is crucial. This work employs ionosphere-free (IF) PPP with ambiguity resolution (PPP-AR) phase residuals to construct test statistics for monitoring the quality of precise satellite corrections. By utilizing precise satellite orbit and clock products from CODE, WUM, and GRG, the PPP-AR phase residuals were first analyzed with sample moments, Allan variance and power spectral density (PSD). The key findings are as follows: (1) The skewness and kurtosis results indicate that ambiguity-fixed phase residuals deviate from an ideal zero-mean Gaussian distribution and exhibit a super-Gaussian distribution. (2) Allan variance and PSD analysis reveal that flicker noise dominates the phase residuals. (3) The noise amplitudes are similar for all satellites, but certain differences are observed among different GNSS systems and satellite types. (4) The noise level of phase residuals is influenced by the receiver types, antenna types, and precise products from different analysis centers. Leveraging the error characteristics, the two-step Gaussian overbounding (OB) method was employed to estimate the corresponding OB parameters of the phase residuals. The overbounding results demonstrate that, under similar conditions, phase residuals can be bounded by the calculated bound within the acceptable integrity risk after removing the detected outliers. Anomaly monitoring experiments further show that phase residuals can effectively capture anomalies in precise satellite corrections, with the set threshold successfully detecting such anomalies.

The measurement of virtual height of the sporadic E layer (h'Es) is very sensitive to the type of ionosonde used and the calibration processes. The ionosondes used by the national institute of communication an...

Abstract

Solar photospheric abundances and CI-chondrite compositions are reviewed and updated to obtain representative solar system abundances of the elements and their isotopes. The new photospheric abundances obtained here lead to higher solar metallicity. Full 3D NLTE photospheric analyses are only available for 11 elements. A quality index for analyses is introduced. For several elements, uncertainties remain large. Protosolar mass fractions are H (X = 0.7060), He (Y = 0.2753), and for metals Li to U (Z = 0.0187). The protosolar (C+N)/H agrees within 13% with the ratio for the solar core from the Borexino experiment. Elemental abundances in CI-chondrites were screened by analytical methods, sample sizes, and evaluated using concentration frequency distributions. Aqueously mobile elements (e.g., alkalis, alkaline earths, etc.) often deviate from normal distributions indicating mobilization and/or sequestration into carbonates, phosphates, and sulfates. Revised CI-chondrite abundances of non-volatile elements are similar to earlier estimates. The moderately volatile elements F and Sb are higher than before, as are C, Br and I, whereas the CI-abundances of Hg and N are now significantly lower. The solar system nuclide distribution curves of s-process elements agree within 4% with s-process predictions of Galactic chemical evolution models. P-process nuclide distributions are assessed. No obvious correlation of CI-chondritic to solar elemental abundance ratios with condensation temperatures is observed, nor is there one for ratios of CI-chondrites/solar wind abundances.

The January 2022 Tonga volcanic eruption generated atmospheric pressure waves that propagated over the ocean’s surface and triggered a meteotsunami. This meteotsunami caused significant amplitudes exceeding 10...

Abstract

Accurately modeling and prediction the nonlinear motion of GNSS (Global Navigation Satellite System) coordinate time series holds significant theoretical and practical value for the study of geodynamics. A novel integrated network, named Ensemble Learning method based on Signal Source Driver (ELSSD), is proposed, which leverages the strengths of Long Short-Term Memory (LSTM) and Deep Self-Attention Neural Network (DSANN), while integrating GNSS loading data as an additional data source. Additionally, a multi-track synchronous sliding window data processing strategy is designed to address the challenge of multi-source data fusion input. The effectiveness of this algorithm is validated using GNSS coordinate time series from 186 global stations over a period of 10 years. Experimental results initially illustrate that, when accounting for displacement caused by environmental loading effects, there is a marked improvement in the modeling and prediction accuracy compared with GNSS input-only. Furthermore, the application of three ensemble network strategies-Bagging, Boosting, and Stacking-have further been demonstrated to enhance modeling and prediction accuracy. Compared with LSTM and DSANN networks, the proposed ELSSD algorithm achieves an average RMSE (Root Mean Square Error) of 3.6 mm for both modeling and prediction, with modeling accuracy improvements of 4.8% and 6.2%, while prediction accuracy improvements of 5.4% and 5.9%, respectively. With respect to the traditional Least Square method, there is an improvement of 22.1% and 27.9% in modeling and prediction accuracy, respectively. Regarding noise characteristics, there is a significant reduction in colored noise amplitude, with decreases of 36.7% and 36.0% observed in modeling and prediction, respectively. Simultaneously, the velocity uncertainty experiences an average reduction of 27.1% and 27.5%. The average velocity differences are measured at 0.06 mm/year and 0.24 mm/year, respectively. Hence, our findings suggest that the ELSSD algorithm emerges as an effective methodology for handling multi-source data input in GNSS coordinate time series, presenting promising practical applications in the field.

The Mw 7.5 Noto Peninsula earthquake, which occurred on January 1, 2024, was considerably hazardous to the peninsula and surrounding regions owing to a strong motion, large-scale crustal deformation, and subse...

Nature Geoscience, Published online: 21 February 2025; doi:10.1038/s41561-025-01653-z

Quartz-rich clasts in Martian meteorite NWA 7533 indicate the presence of granitic rocks on early Mars that formed via hydrothermal activity and impact melting, according to petrologic and in situ geochemical analyses.

Abstract

The predictive learning of total electron content (TEC) spatiotemporal sequences aims to generate future TEC maps by learning from historical data, where both the spatial appearances and temporal variations are crucial for accurate predictions. However, the state-of-the-art TEC map prediction models typically employ sequential stacking of ConvLSTM, ConvGRU, and their variants. These models focus more on modeling temporal variations, and the spatial features extracted from the historical sequence are highly abstracted, resulting in the fine-grained spatial appearances not being adequately memorized or transmitted, leading to fuzzy prediction results during storm time. In this paper, we used PredRNN to propose a storm-time ionospheric TEC spatiotemporal prediction model with multichannel features, named Multichannel PredRNN, which can simultaneously remember the temporal patterns and spatial appearances in input sequence. The temporal memory as well as the spatial memory are updated repeatedly over time, ensuring that both temporal memory and spatiotemporal memory are fully utilized in prediction. According to Dst index, 60 magnetic storm events from 2011 to 2019 were selected as the dataset. We first discussed the impact of feature combinations on predictive performance. The results show that using multichannel feature (TEC + Dst&F10.7), the Multichannel PredRNN and the comparison models ConvGRU and ConvLSTM have the best prediction performance. Then we used the optimal feature combination for prediction. We compared Multichannel PredRNN with IRI-2016, COPG, ConvLSTM and ConvGRU under various conditions, including the entire test magnetic events, periods of quiet and storm, different phases of geomagnetic storms, and the most severe geomagnetic storms. Finally, we compared the performance of different output steps. The experimental results indicate that in all cases, Multichannel PredRNN with dual memory state and zigzag flow is superior to four compared models.

Abstract

The gravimetry measurements from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission provide an essential way to monitor changes in ocean bottom pressure ( \(p_b\) ), which is a critical variable in understanding ocean circulation. However, the coarse spatial resolution of the GRACE(-FO) fields blurs important spatial details, such as \(p_b\) gradients. In this study, we employ a self-supervised deep learning algorithm to downscale global monthly \(p_b\) anomalies derived from GRACE(-FO) observations to an equal-angle 0.25  \( ^{\circ }\) grid in the absence of high-resolution ground truth. The optimization process is realized by constraining the outputs to follow the large-scale mass conservation contained in the gravity field estimates while learning the spatial details from two ocean reanalysis products. The downscaled product agrees with GRACE(-FO) solutions over large ocean basins at the millimeter level in terms of equivalent water height and shows signs of outperforming them when evaluating short spatial scale variability. In particular, the downscaled \(p_b\) product has more realistic signal content near the coast and exhibits better agreement with tide gauge measurements at around 80% of 465 globally distributed stations. Our method presents a novel way of combining the advantages of satellite measurements and ocean models at the product level, with potential downstream applications for studies of the large-scale ocean circulation, coastal sea level variability, and changes in global geodetic parameters.

Abstract

Data-driven technologies have shown promising potential for improving GNSS positioning, which can analyze observation data to learn the complex hidden characteristics of system models, without rigorous prior assumptions. However, in complex urban areas, the input observation data contain task-irrelevant noisy GNSS measurements arising from stochastic noise, such as signal reflections from tall buildings. Moreover, the problem of data distribution shift between the training and testing phases exists for dynamically changing environments. These problems limit the robustness and generalizability of the data-driven GNSS positioning methods in urban areas. In this paper, a novel deep reinforcement learning (DRL) method is proposed to improve the robustness and generalizability of the data-driven GNSS positioning. Specifically, to address the data distribution shift in dynamically changing environments, the robust Bellman operator (RBO) is employed into the DRL optimization to model the deviations in the data distribution and to enhance generalizability. To improve robustness against task-irrelevant noisy GNSS measurements, the long-term reward sequence prediction (LRSP) is adopted to learn robust representations by extracting task-relevant information from GNSS observations. Therefore, we develop a DRL method with robust augmented reward sequence prediction to correct the rough position solved by model-based methods. Moreover, a novel real-world GNSS positioning dataset is built, containing different scenes in urban areas. Our experiments were conducted on the public dataset Google smartphone decimeter challenge 2022 (GSDC2022) and the built dataset Guangzhou GNSS version 2 (GZGNSS-V2), which demonstrated that the proposed method can outperform model-based and state-of-the-art data-driven methods in terms of generalizability across different environments.

Nature Geoscience, Published online: 18 February 2025; doi:10.1038/s41561-025-01648-w

Global sea-level rise during Meltwater Pulse 1A followed sequential ice loss from the Laurentide, Eurasian and then West Antarctic ice sheets, according to a fingerprinting approach.

Abstract

Many components of the Earth system feature self-reinforcing feedback processes that can potentially scale up a small initial change to a fundamental state change of the underlying system in a sometimes abrupt or irreversible manner beyond a critical threshold. Such tipping points can be found across a wide range of spatial and temporal scales and are expressed in very different observable variables. For example, early-warning signals of approaching critical transitions may manifest in localised spatial pattern formation of vegetation within years as observed for the Amazon rainforest. In contrast, the susceptibility of ice sheets to tipping dynamics can unfold at basin to sub-continental scales, over centuries to even millennia. Accordingly, to improve the understanding of the underlying processes, to capture present-day system states and to monitor early-warning signals, tipping point science relies on diverse data products. To that end, Earth observation has proven indispensable as it provides a broad range of data products with varying spatio-temporal scales and resolutions. Here we review the observable characteristics of selected potential climate tipping systems associated with the multiple stages of a tipping process: This includes i) gaining system and process understanding, ii) detecting early-warning signals for resilience loss when approaching potential tipping points and iii) monitoring progressing tipping dynamics across scales in space and time. By assessing how well the observational requirements are met by the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS), we identify gaps in the portfolio and what is needed to better characterise potential candidate tipping elements. Gaps have been identified for the Amazon forest system (vegetation water content), permafrost (ground subsidence), Atlantic Meridional Overturning Circulation, AMOC (section mass, heat and fresh water transports and freshwater input from ice sheet edges) and ice sheets (e.g. surface melt). For many of the ECVs, issues in specifications have been identified. Of main concern are spatial resolution and missing variables, calling for an update of the ECVS or a separate, dedicated catalogue of tipping variables.

Abstract

The Gravity field and steady-state Ocean Circulation Explorer (GOCE) mission, launched by the European Space Agency, provided high-quality gravitational gradient data with near-global coverage, excluding polar regions. These data have been instrumental in regional gravity field modelling through various methods. One approach involves a mathematical model based on Fredholm’s integral equation of the first kind, which relates surface gravity anomalies to satellite gradient data. Solving this equation requires discretising a surface integral and applying further regularisation techniques to stabilise the numerical solution of a resulting system of linear equations. This study examines four methods for modifying the system of linear equations derived by discretising the Fredholm integral equation. The methods include direct inversion, remove-compute-restore, truncation reduction of the integral formula, and inversion of a modified integral for estimating surface gravity anomalies from satellite gradient data over a test area in Central Europe. Since the system of linear equations is ill-conditioned, the Tikhonov regularisation is applied to stabilise its numerical solution. To assess the precision and reliability of the estimated gravity anomalies, the study introduces mathematical models for estimation of biased and de-biased noise variance–covariance matrices of estimated surface gravity anomalies. The results indicate that the signal-to-noise ratio of reduced satellite gradient data in the remove-compute-restore method is smaller compared to other methods in the study, necessitating stronger stabilisation of the model to recover surface gravity anomalies. This, in turn, leads to a more optimistic uncertainty propagation than the other considered methods.