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Abstract

Integrated Water Vapor (IWV) is crucial in environmental research, offering insights into atmospheric dynamics. Direct IWV measurement is challenging, necessitating alternative estimation technologies. Existing methods including Global Navigation Satellite System (GNSS), radiosondes, water vapor radiometers (WVR), satellite remote sensing, and numerical weather models (NWM), have specific limitations. GNSS and WVR provide high precision and temporal resolution (e.g., 5 min) but are limited to specific locations. Radiosondes, while accurate, have sparse spatial distribution and low temporal resolution (e.g., twice daily). Satellite remote sensing offers broad spatial resolution but lower temporal resolution (hours to days) and reduced accuracy under cloudy conditions and due to satellite tracks. NWMs provide global hourly products but their accuracy depends on meteorological data and model precision.

This study introduces a regional IWV predictive model using Machine Learning to address these challenges. Utilizing IWV data from GNSS stations, the study develops a predictive model based on least squares support vector machine, which autonomously determines optimal parameters to enhance performance. The model enables accurate IWV estimation at any location within a region, using inputs such as latitude, longitude, altitude, and temperature, achieving an average root mean square error of 0.95 mm. The model’s performance varies across seasons and terrains, showing adaptability to diverse conditions. The model’s reliability is validated by comparing its predictions with the conventional ERA5 IWV method, showing a 61% improvement rate. This refined IWV estimation model is applied for regional climate analysis, demonstrating its practical utility in environmental research, specifically for the Upper Rhine Graben Region.

Nature Geoscience, Published online: 30 January 2025; doi:10.1038/s41561-024-01628-6

Waves breaking on sandy beaches globally contribute a similar amount of dissolved silicon to oceans as that from rivers, according to a global analysis informed by experiments performed on a simulated quartz sand beach.

SummarySeismic tomography is a principal method for studying mantle structure, but imaging of Earth’s wavespeed anomalies is conditioned by seismic wave sampling. Global models use misfit criteria that may strive for balance between portions of the data set but can leave important regional domains underserved. We evaluate two full-waveform global tomography wavespeed models, GLAD-M25 and SEMUCB-WM1, in the mantle below the Pacific Ocean. The region of the South Pacific Superswell contains multiple hotspots which may be fed by plumes anchored in the Large Low Shear-Velocity Province at the base of the mantle. The uneven distribution of seismic receivers worldwide leaves several candidate plumes beneath various hotspots poorly resolved. We assess the regional quality of GLAD-M25 relative to its global performance using a partition of the seismic waveform data used in its construction. We evaluate synthetic waveforms computed using the spectral-element method to determine how well they fit the data according to a variety of criteria measured across multiple seismic phases and frequency bands. The distributions of travel-time anomalies that remain in GLAD-M25 are wider for trans-Pacific paths than globally, suggesting comparatively insufficiently resolved seismic velocity structure in the region of interest. Hence, Pacific-centered regional inversions, based on (augmented) subsets of the global data set have the potential to enhance the resolution of velocity structure. We compare GLAD-M25 and SEMUCB-WM1 by cross-validation with a new, independent, data set. Our results reveal that short- and long-wavelength structure is captured differently by the two models. Our findings lead us to recommend focusing future model iteration on and around the Pacific Superswell and adding data that sample new corridors, especially using ocean sensors, to better constrain seismic velocity structure in this area of significant geodynamic complexity.

SummaryWe present a new 3D crustal P-wave velocity (VP) model for the greater Alpine region (GAR). We use and merge three different high-quality datasets for local earthquake tomography covering 24 years, starting from January 1st, 1996, up to December 31st, 2019. We processed and repicked the waveforms from the events reported by the European-Mediterranean Seismological Centre with M > 3.0 inside the greater Alpine region for the period between May 2007 and December 2015 using a recently developed automated arrival time-picking procedure (ADAPT framework). This allows bridging the data gap between previously published (pre-2007) datasets and the recently published AlpArray research seismicity catalogue and thus provides a high-quality, highly consistent set of P-wave arrival times covering 24 years. With this data set we derived a new minimum 1D VP model and associated station delays covering the entire GAR. Subsequently, we performed a series of local-earthquake-tomography (LET) inversions obtaining a 3D VP model with a horizontal node spacing of 20×20 km and between 7 to 15 km variable vertical spacing in the well-resolved area of investigation, thus improving the spatial and uniformly high-resolution coverage compared to previous LET studies in the area. For well-known major crustal structures, such as, e.g. the geophysical Ivrea body, deep foreland basins and main orogenic crustal roots, our tomographic results correlate well with features documented by various previous seismic studies in the region. This correlation increases our confidence in the model's accuracy throughout the well-resolved area. Additionally, our model reveals previously poorly known, or unknown crustal features and it documents details in the Moho topography throughout the region. Eventually, we present a LET-Moho map (VP isoline of 7.25 km/s) for the GAR with spatially nearly uniform resolution and document its comparison with previously published Moho maps. The new regional 3D VP crustal model also correlates well with a previously published VS crustal model obtained by ambient noise tomography. These comparisons document the new LET results of combined 3D VP crustal velocities and Moho topography being intrinsically consistent and reliable within the region of high resolution. Hence, in addition to further improving our understanding of crustal structure geometries in the GAR, our results also provide pivotal information for a future reference seismic 3D crustal model of the region.

SummaryNumerical simulations of infinite Prandtl number convection in Cartesian domains have shown that a combination of internal and basal heating allows for behaviour not observed in either end-member cases of pure basal or pure internal heating. In particular, these mixed heating systems exhibit a decrease in the upper boundary layer velocity as internal heating increases. This leads to an inverse relationship between surface heat flow and boundary layer velocity. The inverse relationship has been attributed to boundary layer interactions, leading to deviations from classic boundary layer theory. Herein, we extend that work by presenting results from numerical experiments for mixed-heated convection in an isoviscous fluid in a fully 3D spherical domain. We show that an increase in internal heating causes a decrease in surface velocity, consistent with previous Cartesian results. We confirm that boundary layer interactions decrease with increased internal heating, which correlates with decreasing surface velocities. A scaling theory, previously applied to Cartesian geometry, is modified for spherical geometries and tested against the results of the numerical solutions. The modified scalings lead to good fits for temperature and heat flux trends. The scalings predict that velocities can decrease with increased internal heating from low to moderate internal heating rates and become constant at higher heating rates, consistent with numerical results. The quantitative match between velocity scalings and numerical results is not as good as observed for heat flow and temperature trends. We attribute this to surface velocities being more strongly affected by observed changes in convective wavelengths and planform transitions from sheet-like to plume-like downwellings as the rate of internal heating and/or basal heating increases.

SummarySeismic tomography is used to image subsurface structures at various scales, accomplished by solving a nonlinear and nonunique inverse problem. It is therefore important to quantify velocity model uncertainties for accurate earthquake locations and geological interpretations. Monte Carlo sampling techniques are usually used for this purpose, but those methods are computationally intensive, especially for large datasets or high-dimensional parameter spaces. In comparison, Bayesian variational inference provides a more efficient alternative by delivering probabilistic solutions through optimization. The method has been proven to be efficient in 2D tomographic problems. In this study, we apply variational inference to solve 3D double-difference (DD) seismic tomographic system using both absolute and differential travel time data. Synthetic tests demonstrate that the new method can produce more accurate velocity models than the original DD tomography method by avoiding regularization constraints, and at the same time provides more reliable uncertainty estimates. Compared to traditional checkerboard resolution tests, the resulting uncertainty estimates provide a better measure for the reliability of the solution. We further apply the new method to data recorded by a local dense seismic array around the San Andreas Fault Observatory at Depth (SAFOD) site along the San Andreas Fault (SAF) at Parkfield. Similar to previous studies, the obtained velocity models show significant velocity contrasts across the fault. More importantly, the new method produces velocity uncertainties of less than 0.34 km/s for ${{\rm{V}}}_p$ and 0.23 km/s for ${{\rm{V}}}_s$. We therefore conclude that variational inference provides an effective tool for solving 3D seismic tomographic problems and quantifying model uncertainties.

The precise position and geometry of a fault and the recognition of contemporary active strands are pivotal elements for formulating regulations for earthquake fault zones and fault setbacks. The western front...

An international research team led by the University of Göttingen has investigated the influence of the forces exerted by the Zagros Mountains in the Kurdistan region of Iraq on how much the surface of the Earth has bent over the last 20 million years. Their research has revealed that in the present day, deep below the Earth's surface, the Neotethys oceanic plate—the ocean floor that used to be between the Arabian and Eurasian continents—is breaking off horizontally, with a tear progressively lengthening from southeast Turkey to northwest Iran.

The Yellow River, which stretches from the Tibetan Plateau to the Bohai Sea in China, is so called because of the color lent by massive amounts of suspended sediments along its 5,400-kilometer length. Its waters, sediments, and nutrients support more than 100 million people and many endemic plant and animal species. China's "Mother River" also transports metals such as iron, cobalt, arsenic, and platinum, a process with important implications for the health and evolution of downstream ecosystems.

Weather, climate and hydrometeorology forecasts require accurate surface–atmosphere coupled modeling. This requires the use of proper coupling heights in computing surface turbulent fluxes, or the exchanges of heat, moisture and momentum between the surface of the Earth and the near-surface thin layer of air called the surface layer.

Mangrove forests along the Amazon coast release significant amounts of trace elements such as neodymium and hafnium. These elements and their isotopic compositions can serve to understand the inputs of micronutrients which are vital for marine life.

Author(s): Pauline A. Simon, Fouad Sahraoui, Sébastien Galtier, Dimitri Laveder, Thierry Passot, and Pierre-Louis Sulem

The impact of ion pressure anisotropy on the energy cascade rate of Hall-MHD turbulence with biadiabatic ions and isothermal electrons is evaluated in three-dimensional direct numerical simulations, using the exact (or third-order) law derived by Simon and Sahraoui in 2022. It is shown that pressure…

[Phys. Rev. E 111, 015210] Published Wed Jan 29, 2025

Abstract—The editing problem in clustering (deletion and addition of edges and/or vertices in an initial graph for building a cluster structure), including various options of the problem (edge cluster editing, vertex cluster editing, etc.) is studied. A literature survey on the problems (problem types and solving approaches) is presented. The cluster edge editing problem is in focus. Several mathematical optimization models for the problem are described: (i) the basic edge editing problem with minimization of the general number of added edges and the number of deleted edges, (ii) the option of the above-mentioned problem with weights of all vertex pairs (including the bi-criteria problem case), and (iii) a multi-criteria problem with vector weights of the vertex pairs. In addition, some other editing problems are briefly described: (a) the edge deletion editing problem, (b) edge editing problem with several vertex types, and (c) vertex deletion editing problem. The problems discussed are illustrated by numerical examples. Some future research directions are pointed out.

Abstract—An estimation problem distributionally robust with respect to random noise is considered for a signal with the bounded second-order derivative on a finite number of observations. The objective functional is the probability that the L2-norm of the estimation error will exceed a pre-specified threshold. Its worst-case value on the set of all signals with the bounded second-order derivative and arbitrary distributions of the noise vector with fixed mean and covariance is to be minimized over the finite-dimensional class of spline estimators. The optimization problem is solved using the methods of convex programming by representing the objective functional in terms of the mean square bound following Markov’s inequality and the tight bound in the form of the multivariate Selberg inequality. A numerical experiment is carried out to compare the obtained solutions to the problem of restoration of the trajectory of a target with bounded acceleration.

Abstract—Problems of image smoothing and segmentation are among the most important directions of processing and analysis of video information, which have much in common in their formulation, final goals, and solution methods. The similarities and differences of the problems are shown on the base of the image model using the analysis and comparison of known smoothing and segmentation algorithms.

SummaryWe present a new seismic shear wave velocity model of the upper mantle of the Antarctic Plate region, AP2024. It includes the lithosphere and underlying mantle down to 660 km depth beneath both the continental and oceanic portions of the plate. To augment the limited seismic station coverage of Antarctica, we assemble very large regional and global data sets, comprising all publicly available broadband seismic data. The model is built using 785 thousand seismograms from over 27 thousand events and 8.7 thousand stations. It is constrained by both body and Rayleigh surface waves, ensuring the dense data sampling of the entire upper mantle depth range. The tomographic inversion is global but focused on the Antarctic Plate, with the data sampling maximised in the Southern Hemisphere, with elaborate automated and manual outlier analysis and removal performed on the regional data, and with the regularisation tuned for the region. The upper mantle of the Antarctic continent exhibits a bimodal nature. The sharp boundary along the Transantarctic Mountains separates the cratonic eastern from tectonic western Antarctica and shows a shear-velocity contrast of up to 17% at ∼100 km depth. The bimodal pattern is also seen in the oceanic part of the plate, with the older oceanic lithosphere beneath the Indian sector of the Southern Ocean showing higher shear velocities. The continental lithosphere in East Antarctica shows high velocity anomalies similar to those beneath stable cratons elsewhere around the world. It is laterally heterogeneous and exhibits significant thinning in the near-coastal parts of Dronning Maud Land and Wilkes Land. A low velocity channel is observed along the southern front of the West Antarctic Rift System and is probably related to Cenozoic rifting. High seismic velocity anomalies are detected beneath the Antarctic Peninsula and are likely to indicate fragments of the recently subducted Phoenix Plate Slab. Low velocity anomalies beneath Marie Byrd Land extend into the deep upper mantle and are consistent with a deep mantle upwelling feeding West Antarctica intraplate magmatism.

SummaryThis study investigates the effectiveness of inversion methods using tsunami waveforms to analyse volcanic tsunami sources, which are a type of non-seismic tsunami source. We focused on the 2018 Anak Krakatau tsunami triggered by a volcanic eruption. This study developed a static initial sea surface displacement model based on tsunami waveform inversion with data recorded at tide gauge stations using a Gaussian-shaped sea surface displacement for the unit source. A key characteristic of our model is that all initial velocity components of the tsunami were zero. We tested 12 scenarios for accuracy to determine the most plausible sea surface displacement. The optimal displacement model reasonably reproduced the observed tsunami waveforms. The calculated water volume at the initial sea surface displacement was reasonably consistent with the total collapse volume of the Anak Krakatau eruption by magnitude. These findings suggest that our approach to developing a static source model can effectively apply to non-seismic tsunami events. Although this approach offers simplified tsunami source modeling for tsunami estimation during volcanic eruptions with complex source dynamics, further validation is required for its application to other non-seismic tsunami events.

Abstract

A comprehensive approach to the analysis of electroencephalographic (EEG) signals obtained from human brain and artificially synthesized using machine learning methods is presented. The main focus is on data preprocessing, including signal normalization and filtering, as well as application of various feature extraction methods, in particular, Fast Fourier Transform and Mel-Frequency Cepstral Coefficients. A comparative analysis of classification accuracy using logistic regression, random forest, gradient boosting, and recurrent neural network LSTM is performed. Special attention is given to the effect of filtering parameters on classification accuracy. The results show that filtering and proper tuning of model parameters significantly improve the accuracy of EEG signal classification, ensuring separation of real and synthetic EEG pools. The results and discussion may serve as a basis for further research in the field of biomedical signal analysis and processing.

Abstract

The real-time ionospheric data streams are continuously being provided by a number of International GNSS service analysis centers such as Centre National d’Etudes Spatiales (CNES), Chinese Academy of Sciences (CAS), Universitat Politècnica de Catalunya (UPC), and Wuhan University, however, the performance evaluation of these Real-Time Global Ionosphere Map (RT-GIM) products is essential. We assess the quality and consistency of these RT-GIM products from the declining phase of solar cycle 24 (year 2017) to the maximum of solar cycle 25 (year 2024) by comparing with Final GIMs provided by Center for Orbit Determination in Europe (CODE) and Jason-3 altimetry satellite. The results suggest that during the low solar activity periods (2017–2022), all the RT-GIMs perform almost similar. However, the performance of the CNES and CAS RT-GIMs strongly deteriorates as the solar cycle proceeds towards the maximum (2022–2024) with annual RMS values remains between 9 and 7.5 TECU. The external validation vs Jason-3 during this maximum period suggested that the accuracy of the UPC RTGIMs is nearly identical to the final CODE GIMs at typically 4–10 TECU in standard deviation over oceans, while performance degradations are recorded for rest of the RTGIMs exhibiting high standard deviations. Results suggest that the high RMS errors in GIMs from CNES and CAS might be related to the geomagnetic inclination misalignments followed by the map projections as both maps form single peak along geomagnetic equator during high solar activities. In addition, under the presence of a severe G4-class geomagnetic storm, CNES RT-GIMs undergoes severe accuracy degradation across all continents recording a − 20 to − 40 TECU bias offset. Meanwhile, UPC RT-GIM remain the most consistent and stable performer (both, globally and over oceans) that provides accurate global ionospheric information which is promising for their applications in real-time precise GNSS positioning.

Abstract

The Surface Dust Analyser (SUDA) is a mass spectrometer onboard the Europa Clipper mission for investigating the surface composition of the Galilean moon Europa. Atmosphereless planetary moons such as the Galilean satellites are wrapped into a ballistic dust exosphere populated by tiny samples from the moon’s surface produced by impacts of fast micrometeoroids. SUDA will measure the composition of such surface ejecta during close flybys of Europa to obtain key chemical signatures for revealing the satellite’s composition such as organic molecules and salts, history, and geological evolution. Because of their ballistic orbits, detected ejecta can be traced back to the surface with a spatial resolution roughly equal to the instantaneous altitude of the spacecraft. SUDA is a Time-Of-Flight (TOF), reflectron-type impact mass spectrometer, optimized for a high mass resolution which only weakly depends on the impact location. The instrument will measure the mass, speed, charge, elemental, molecular, and isotopic composition of impacting grains. The instrument’s small size of \(268 ~\mathrm {mm} \times 250 ~\mathrm {mm} \times 171\) \(~\mathrm {mm}\) , radiation-hard design, and rather large sensitive area of 220 cm2 matches well the challenging demands of the Clipper mission.