Rock and ice are ubiquitous geologic materials. While apparently solid, they also exhibit fluid behavior under stress - a property termed viscoelasticity. Viscoelastic convection of Earth's mantle drives tectonic plate motion with consequences for earthquakes and sea-level rise, while viscoelastic deformation of ice controls glacier flow and the flexure of icy moons. For crystalline materials, "flow laws" describing bulk rheology can be derived from understanding microstructural dynamics such as crystal-defect migration. Common geologic materials like ice and olivine have grain sizes and crystal orientations that evolve with strain; this complexity precludes a first principles approach. Here we use a Bayesian inference method to learn the connection between microstructure and flow in ice and olivine, from fits to experimental data of these materials undergoing steady-state deformation and forced oscillations. We demonstrate that this method can constrain a nonlinear viscoelastic model for each material, that is capable of capturing both steady and transient dynamics and can also predict dynamics for data it was not trained on. Our results may improve geodynamic models that rely on parameterized constitutive equations, while our approach will be useful for experimental design and hypothesis testing.
Compound flood risks from spatially and temporally clustered extremes challenge traditional risk models and insurance portfolios that often neglect correlated risks across regions. Spatiotemporally clustered floods exhibit fat-tail behavior, modulated by low-frequency hydroclimatic variability and large-scale moisture transport. Nonstationary stochastic simulators and regional compound event models aim to capture such tail risk, but have not yet unified spatial and temporal extremes under low-frequency hydroclimatic variability. We introduce a novel attention-based framework for multisite flood generation conditional on a multivariate hydroclimatic signal with explainable attribution to global sub-decadal to multi-decadal climate variability. Our simulator combines wavelet signal processing, transformer-based multivariate time series forecasting, and modified Neyman-Scott joint clustering to simulate climate-informed spatially compounding and temporally cascading floods. Applied to a Mississippi River Basin case study, the model generates distributed portfolios of plausibly clustered flood risks across space and time, providing a basis for simulating spatiotemporally correlated losses characteristic of flood-induced damage.
The sensitivity of Global Navigation Satellite Systems (GNSS) receivers to ionospheric disturbances and their constant growth are nowadays resulting in an increased concern of GNSS-users about the impacts of ionospheric disturbances at mid-latitudes. The geomagnetic storm of June 2015 is an example of a rare phenomenon of a spill-over of equatorial plasma bubbles well North from their habitual. We study the occurrence of small- and medium-scale irregularities in the North Atlantic Eastern-Mediterranean mid- and low-latitudinal zone by analysing the behaviour of the amplitude scintillation index S4 and of the rate of total electron content index (ROTI) during such a storm. In addition, large scale perturbations of the ionospheric electron density were studied using ground and space-born instruments, thus characterizing a complex perturbation behaviour over the region mentioned above. The multi-source data allows us to characterize the impact of irregularities of different scales to better understand the ionospheric dynamics and stress the importance of a proper monitoring of the ionosphere in the studied region.
The statistical basis for conventional full-waveform inversion (FWI) approaches is commonly associated with Gaussian statistics. However, errors are rarely Gaussian in non-linear problems like FWI. In this work, we investigate the portability of a new objective function for FWI applications based on the graph-space optimal transport and $κ$-generalized Gaussian probability distribution. In particular, we demonstrate that the proposed objective function is robust in mitigating two critical problems in FWI, which are associated with cycle skipping issues and non-Gaussian errors. The results reveal that our proposal can mitigate the negative influence of cycle-skipping ambiguity and non-Gaussian noises and reduce the computational runtime for computing the transport plan associated with the optimal transport theory.
Building subsurface velocity models is essential to our goals in utilizing seismic data for Earth discovery and exploration, as well as monitoring. With the dawn of machine learning, these velocity models (or, more precisely, their distribution) can be stored accurately and efficiently in a generative model. These stored velocity model distributions can be utilized to regularize or quantify uncertainties in inverse problems, like full waveform inversion. However, most generators, like normalizing flows or diffusion models, treat the image (velocity model) uniformly, disregarding spatial dependencies and resolution changes with respect to the observation locations. To address this weakness, we introduce VelocityGPT, a novel implementation that utilizes Transformer decoders trained autoregressively to generate a velocity model from shallow subsurface to deep. Owing to the fact that seismic data are often recorded on the Earth's surface, a top-down generator can utilize the inverted information in the shallow as guidance (prior) to generating the deep. To facilitate the implementation, we use an additional network to compress the velocity model. We also inject prior information, like well or structure (represented by a migration image) to generate the velocity model. Using synthetic data, we demonstrate the effectiveness of VelocityGPT as a promising approach in generative model applications for seismic velocity model building.
Ensemble data assimilation techniques form an indispensable part of numerical weather prediction. As the ensemble size grows and model resolution increases, the amount of required storage becomes a major issue. Data compression schemes may come to the rescue not only for operational weather prediction, but also for weather history archives. In this paper, we present the design and implementation of an easy-to-use framework for evaluating the impact of lossy data compression in large scale ensemble data assimilation. The framework leverages robust statistical qualifiers to determine which compression parameters can be safely applied to the climate variables. Furthermore, our proposal can be used to apply the best parameters during operation, while monitoring data integrity. We perform an exemplary study on the Lorenz96 model to identify viable compression parameters and achieve a 1/3 saving in storage space and an effective speedup of 6% per assimilation cycle, while monitoring the state integrity.
Hydrogeodesy can benefit greatly from the use of Global Positioning System (GPS) displacements to analyse local changes in the hydrosphere, which the commonly used Gravity Recovery and Climate Experiment (GRACE) mission is unable to provide due to coarse spatial resolution. Hydrosphere changes recorded by GPS are unfortunately hidden among the other signals to which the system is also sensitive so that the sensitivity of GPS to changes in the hydrosphere on temporal-scales from pluri-annual to decadal is questionable. We focus on hydrosphere signatures present on these long-term temporal-scales as observed by GPS through the vertical displacement time series (DTS) of 122 permanent stations over Europe and compare them to the DTS derived from GRACE for GPS locations. Our methodology is based on the weighted Savitzky-Golay (S-G) filter, an underestimated filter in the field of geodetic time series analysis. We show that the correspondence between GPS and GRACE on long-term temporal-scales is generally strong, but decreases for coastal regions and regions where the coarse gridding of GRACE does not capture local hydrosphere effects. Further, the negative correlation with El Niño Southern Oscillations (ENSO) is confirmed for Europe.
The Matuyama-Brunhes reversal of Earth's magnetic dipole field took place 0.78 Ma ago, and detailed temporally resolved paleomagnetic data are available for this period. A geomagnetic reversal is expected to impact the cosmic ray flux, which in turn might impact atmospheric ionization rates. In this study a model that yields atmospheric ionization for the entire globe based on an input magnetic field is presented. Taking the time dependent paleomagnetic data as input, a 3D time series of the atmospheric ionization rates during the reversal is produced. We show, that as the dipole field weakens, the atmospheric ionization increases at low latitudes. The increase is ca. 25% at the surface and up to a factor of 5 in the upper atmosphere. Globally, ionization rates increase around 13% at the surface and up to a factor of 2 in the upper atmosphere, whereas polar regions are largely unaffected. Finally, the change in ionization due to the solar 11-year cycle is greatly affected by the reversal. The relative change in atmospheric ionization between solar-minimum and solar-maximum varies between 2 and two orders of magnitude. All atmospheric ionization data is made available for download.
This paper introduces new definitions of common geodetic measures on a planetary surface (namely surface area, path length, and mean value or other statistical parameters of a surface function) that are not based on a datum such as a reference ellipsoid. Instead, the so-called datumless geodetic measures are based on physically meaningful formulations that rely only on the actual planetary surface and gravity. The datumless measures provide universally standardized measurements on any terrestrial object, including non-ellipsoidal asteroids and comets. Conveniently, on fairly round planets such as Earth and Mars, the datumless measures yield very similar values as corresponding geodetic measures on a reference ellipsoid. Like their ellipsoidal counterparts, the datumless measures quantify area and length in the familiar "bird's-eye view" or "horizontal, normal-to-gravity" sense. Far from being purely theoretical, the datumless measures can be approximated in GIS software using a digital elevation model and a gravity model such as a geoid.
Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a non-convex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCN). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structure to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. These numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time, and yield more accurate subsurface velocity models in comparison with conventional FWI.
David Cortés-Ortuño, Karl Fabian, Lennart V. de Groot
Micromagnetic tomography aims at reconstructing large numbers of individual magnetizations of magnetic particles from combining high-resolution magnetic scanning techniques with micro X-ray computed tomography (microCT). Previous work demonstrated that dipole moments can be robustly inferred, and mathematical analysis showed that the potential field of each particle is uniquely determined. Here, we describe a mathematical procedure to recover higher orders of the magnetic potential of the individual magnetic particles in terms of their spherical harmonic expansions (SHE). We test this approach on data from scanning superconducting quantum interference device microscopy and microCT of a reference sample. For particles with high signal-to-noise ratio of the magnetic scan we demonstrate that SHE up to order $n=3$ can be robustly recovered. This additional level of detail restricts the possible internal magnetization structures of the particles and provides valuable rock magnetic information with respect to their stability and reliability as paleomagnetic remanence carriers. Micromagnetic tomography therefore enables a new approach for detailed rock magnetic studies on large ensembles of individual particles.
Dario Sabbagh, Carlo Scotto, Alessandro Ippolito
et al.
The effects of the solar eclipse of March 20, 2015 on different ionospheric layers were studied, using vertical ionospheric soundings from the ionosondes of Rome (41.8N, 12.5E), Gibilmanna (37.9N, 14.0E), and San Vito dei Normanni (40.6N, 18.0E). The responses of the critical frequencies were investigated during the solar eclipse, and the formulations used for their estimation were corrected taking into account the decreased solar irradiance. This effect was modeled as a Solar Obscuration Factor (SOF) and comparisons with experimental values were performed. A further study on the occurrence of the sporadic E layer during the eclipse is presented. When ionogram analysis is limited to 3 days before and 3 days after the eclipse, the appearance of the sporadic E layer seems to be related to the eclipse. However, when a longer range of days before and after the eclipse event are taken into account this phenomenon does not appear so clear. The behavior of a regional adaptive and assimilative 3D ionospheric model was also tested, assimilating plasma frequency profiles fp(h). Studying the behavior of the model in such unusual conditions enabled the introduction of corrections to the foE, foF1, and foF2 formulations, improving the performances of the model itself.
A 54-km diameter Noachian-aged crater in the southern highlands of Mars contains unusually well-preserved inverted fluvial channel networks and lacustrine deposits, all of which formed completely inside the crater. This closed-source drainage basin (CSDB) crater is distinct from previously documented fluvially breached or groundwater-fed crater basin lakes on Mars. We compare our observations to previously established models of crater degradation, fluvial incision, and topographic inversion on Mars to assess the most likely origins of the water that formed the fluvial and lacustrine features. We favor top-down melting of a cold-based glacier as the source of water in the CSDB crater, which would represent the first examples of proglacial fluvial channels and lakes found on Noachian Mars.
Ocean swell plays an important role in the transport of energy across the ocean, yet its evolution is still not well understood. In the late 1960s, the nonlinear Schr{ö}dinger (NLS) equation was derived as a model for the propagation of ocean swell over large distances. More recently, a number of dissipative generalizations of the NLS equation based on a simple dissipation assumption have been proposed. These models have been shown to accurately model wave evolution in the laboratory setting, but their validity in modeling ocean swell has not previously been examined. We study the efficacy of the NLS equation and four of its generalizations in modeling the evolution of swell in the ocean. The dissipative generalizations perform significantly better than conservative models and are overall reasonable models for swell amplitudes, indicating dissipation is an important physical effect in ocean swell evolution. The nonlinear models did not out-perform their linearizations, indicating linear models may be sufficient in modeling ocean swell evolution.
The Carrington storm in 1859 is considered to be the major geomagnetic disturbance related to solar activity. In a recent paper, Cid et al. (2015) discovered a geomagnetic disturbance case with a profile extraordinarily similar to the disturbance of the Carrington event at Colaba, but at a mid-latitude observatory, leading to a re- interpretation of the 1859 event. Based on those results, this paper performs a deep search for other 'Carrington-like' events and analyses interplanetary observations leading to the ground disturbances which emerged from the systematic analysis. The results of this study based on two Carrington-like events (1) reinforce the awareness about the possibility of missing hazardous space weather events as the large H-spike recorded at Colaba by using global geomagnetic indices, (2) argue against the role of the ring current as the major current involved in Carrington-like events, leaving field- aligned currents (FACs) as the main current involved, and (3) propose abrupt southward reversals of IMF along with high solar wind pressure as the interplanetary trigger of a Carrington-like event.