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Satellite magnetic field observations have the potential to provide valuable information on dynamics, heat content and salinity throughout the ocean. Here we present the expected spatio-temporal characteristics of the ocean-induced magnetic field at satellite altitude on periods of months to decades. We compare these to the characteristics of other sources of Earth's magnetic field, and discuss whether it is feasible for the ocean-induced magnetic field to be retrieved and routinely monitored from space. We focus on large length scales (spherical harmonic degrees up to 30) and periods from one month up to five years. To characterize the expected ocean signal we make use of advanced numerical simulations taking high resolution oceanographic inputs and solve the magnetic induction equation in 3D including galvanic coupling and self induction effects. We find the time-varying ocean-induced signal dominates over the primary source of the internal field, the core dynamo, at high spherical harmonic degree with the cross-over taking place at degree 15 to 20 depending on the considered period. The ionospheric and magnetospheric fields (including their Earth induced counterparts) have most power on periods shorter than one month and are expected to be mostly zonal in magnetic coordinates at satellite altitude. Based on these findings we discuss future prospects for isolating and monitoring long period ocean induced magnetic field variations using data collected by present and upcoming magnetic survey satellites.

In a recent paper (Gonzalez et al., 2023), we investigated the motion of grains within a granular bed sheared by a viscous fluid, and showed how segregation and hardening occur in the fluid- (bedload) and solid-like (creep) regions. In this paper, we inquire further into the mechanisms leading to grain segregation in a bidisperse bed, and how the forces are distributed. For that, we carried out numerical simulations at the grain scale by using CFD-DEM (computational fluid dynamics-discrete element method), with which we were able to track the positions, velocities, forces, and solid contacts underwent by each grain. We show that during the upward motion of large grains the direct action of fluid forces is significant in the middle and upper parts of the bedload layer, while only contact forces are significant in the creep layer and lower part of the bedload layer. We also show that in all cases the particles experience a moment about a -45 degrees contact point (with respect to the horizontal plane) when migrating upward, whether entrained by other contacts or directly by the fluid. In addition, we show the variations in the average solid-solid contacts, and how forces caused either by solid-solid contacts or directly by the fluid are distributed within the bed. Our results provide the relationship between force propagation and reorganization of grains in sheared beds, explaining mechanisms found, for example, in river beds and landslides.

Accurate modeling of terrestrial carbon and water exchange requires robust ecological parameters that capture vegetation responses and adaptations to the local environment. The current generation of land models use Plant Functional Types (PFTs) to discretize vegetation functional diversity, but these coarse categorizations often overlook fine-scale variations shaped by local climate, soil, and forest age factors. The lack of governing equations for plant adaptation demands a paradigm shift in how we integrate diverse Earth observations to uncover ecological functional dependence on changing environments. To address this challenge, we developed DifferLand, a differentiable, hybrid physics and machine learning model that infers the spatial distributions of ecological parameters and their relationships with environmental factors constrained by satellite and in-situ observations. Our model unifies top-down and bottom-up observational constraints with process-based knowledge to generate a global analysis of ecological functions and their adaptation to environmental gradients. We found PFTs account for less than half of the explainable spatial parameter variations controlling carbon fluxes and vegetation states. The remaining parameter variability is largely driven by local climate and forest demography factors, and the learned environment-parameter relationships lead to enhanced spatial generalization at unseen locations. DifferLand identified growing season length, leaf economics, and agricultural intensity as the three orthogonal spatial gradients underlying parameter variations. Our novel framework can lead to new insights on global carbon cycling by learning directly from data and expanding our understanding of local responses of ecosystems to environmental drivers.

Knowing the distribution between fresh and saline groundwater is imperative for sustainable and integrated management of water resources in coastal areas. The airborne electromagnetic (AEM) method is increasingly used for hydrogeological mapping over large areas via bulk electrical resistivity. However, accurately and reliably mapping the fresh/saltwater interface (FSI) requires accurate knowledge about the transition zone. The objective is to quantify the uncertainty in using AEM data to inform on the depth of the FSI. The study mimics a dual-moment time-domain SkyTEM sounding recorded in the Belgian coastal plain based on borehole data. It quantifies uncertainty using a differential evolution adaptive Metropolis algorithm to sample the posterior distribution. The results indicate the importance of reliable altitude, pitch and roll logging. Gathering prior knowledge about the transition zone, for example, through borehole logs, significantly improves the estimation of the FSI. The Resolve frequency-domain system, especially in context with very shallow to shallow FSIs, is more suitable for salinity mapping than the time-domain SkyTEM used in the field survey. The depth of the FSI may be defined via various threshold values. The uncertainty of three different thresholds is studied. The FSI based on the middle of the transition zone is the most reliable, while the FSI based on the 1500 mg/L total dissolved solids threshold is the least robust.

Understanding material failure is critical for designing stronger and lighter structures by identifying weaknesses that could be mitigated. Existing full-physics numerical simulation techniques involve trade-offs between speed, accuracy, and the ability to handle complex features like varying boundary conditions, grid types, resolution, and physical models. We present the first foundation model specifically designed for predicting material failure, leveraging large-scale datasets and a high parameter count (up to 3B) to significantly improve the accuracy of failure predictions. In addition, a large language model provides rich context embeddings, enabling our model to make predictions across a diverse range of conditions. Unlike traditional machine learning models, which are often tailored to specific systems or limited to narrow simulation conditions, our foundation model is designed to generalize across different materials and simulators. This flexibility enables the model to handle a range of material properties and conditions, providing accurate predictions without the need for retraining or adjustments for each specific case. Our model is capable of accommodating diverse input formats, such as images and varying simulation conditions, and producing a range of outputs, from simulation results to effective properties. It supports both Cartesian and unstructured grids, with design choices that allow for seamless updates and extensions as new data and requirements emerge. Our results show that increasing the scale of the model leads to significant performance gains (loss scales as $N^{-1.6}$, compared to language models which often scale as $N^{-0.5}$)., Comment: Accepted at NeurIPS 2024 "Foundation Models for Science: Progress, Opportunities, and Challenges" Workshop

Following the discovery of several ocean worlds in the solar system, and the selection of Uranus as the highest priority objective by the Planetary Science and Astrobiology Decadal Survey 2023-2032, the five largest moons of Uranus (Miranda, Ariel, Umbriel, Titania and Oberon) have been receiving renewed attention as they may also harbor a subsurface ocean. We assess how rotation measurements could help confirm the internal differentiation of the bodies and detect internal oceans if any. Because of the time-varying gravitational torque of Uranus on the flattened shape of its synchronous satellites, the latter librate with respect to their mean rotation and precess with a non zero obliquity. For a range of interior models with a rocky core surrounded by a hydrosphere, either solid or divided into an outer ice shell with a liquid ocean underneath, we compute their diurnal libration amplitude and obliquity., Comment: Paper accepted for publication in Icarus

Controversy exists regarding whether grain morphology reduces or enhances the drag of a single grain in creeping flows; further complication occurs when orientation dependence for aspherical grains comes into play. To quantify influences of shape irregularity and orientation, this study numerically investigates the drag in creeping flows for fractally rough grains depicted by Spherical Harmonics. As the grain surface becomes more angular with increasing relative roughness and fractal dimension, a stronger drag reduction is observed; this observed morphology-dependent reduction indicates Stokes formula is insufficient. The rotational dependence helps to explain the paradox wherein drag enhancement is always encountered insettling-grain experiments popular in geophysics. For the drag of a given rough grain at various orientations, extracted distributions of the overall drag, and its two components, i.e., skin friction and pressure drag, universally adhere to the Weibull patterns. Moreover, we identify a universal power law between drag coefficients and a newly proposed area-related number, sufficiently taking into consideration both grain roughness by surface area and orientational dependence by projected area perpendicular to the flow direction. This law is primarily controlled by relative roughness, with less sensitivity to fractal dimension. The determined expression enables precise estimation of fluid-particle interactions in upscaling simulations.

This article introduces a newly hypothesized atmospheric phenomenon called toroh, inspired by the Brazilian regional phenomenon known as tromba de agua. The existence of the toroh, also suggested to be termed toroh, is characterized by intense and localized precipitation that causes water erosion, leaving straight scars on mountain slopes. The term originates from the Tupi-Guarani language meaning rainfall like a water jet. Guarani uses sounds to imitate natural phenomena, capturing distinct noises: one when water cuts through the air and another when it hits the ground. The toroh differs from cloudbursts and rainstorms, referring to other extreme precipitation events, as it is stronger and more localized. A conceptual model has been proposed, suggesting that a toroh occurs when a supercell passes over a rift, which intensifies the liquid micro-explosion and leads to the formation of a river within the atmosphere. Previously, meteorological observers in Southern Brazil used the term Tromba de agua for the same phenomenon. However, this term also referred to waterspouts in official Brazilian meteorology, leading to confusion. Additionally, the article emphasizes the importance of indigenous contributions i n naming and understanding natural phenomena, which are often overlooked by Western science.

We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation ($r^2=0.6$) to high-quality airborne estimates of sun-induced fluorescence (SIF). SIF estimates can provide explanatory information for many tasks related to agricultural management and physiological studies. While SIF products from airborne platforms are accurate and spatially well resolved, the data acquisition of such products remains science-oriented and limited to temporally constrained campaigns. Spaceborne SIF products on the other hand are available globally with often sufficient revisit times. However, the spatial resolution of spaceborne SIF products is too small for agricultural applications. In view of ESA's upcoming FLEX mission we develop a method for SIF retrieval in the O$_2$-A band of hyperspectral DESIS imagery to provide first insights for spaceborne SIF retrieval at high spatial resolution. To this end, we train a simulation-based self-supervised network with a novel perturbation based regularizer and test performance improvements under additional supervised regularization of atmospheric variable prediction. In a validation study with corresponding HyPlant derived SIF estimates at 740 nm we find that our model reaches a mean absolute difference of 0.78 mW / nm / sr / m$^2$., Comment: submitted to ECCV CVPPA 2024, 14 pages, 8 figures

Improving hydrocarbon production with hydraulic fracturing from unconventional reservoirs requires investigating transport phenomena at the single fracture level. In this study, we simulated geomechanical deformation, fluid flow, and reactive transport to understand the effect of hydraulic fracturing treatment on permeability evolution in shale rough-walled fractures. Using concepts of fractional Brownian motion and surface roughness characterizations with laser profilometer, we first generated three rough-walled microfractures consistent with three laboratory experiments (i.e., E4, E5 and E6). After that, the generated microfractures were subjected to a confining pressure in accord with experimental conditions, and geomechanical deformation was simulated. We used the OpenFOAM software package to simulate the fluid flow and permeability. By comparing the simulated permeability values with the experimentally measured ones we found relative errors equal to 28, 15 and 200% respectively for the experiments E4, E5 and E6. After calibration, however, the relative error dropped below 4%. We next simulated the reactive transport using the GeoChemFOAM solver and investigated permeability evolution in the deformed microfractures. We found that after 10 hrs of reactive transport simulations, permeability increased by 47%, on average, in all cases studied here., Comment: 9 figures, 3 tables

Ongoing efforts aim to achieve a globally uniform and consistent International Height Reference System (IHRS), as a global standard for accurately determining physical (height-)coordinates worldwide. Near the Earth's surface, two stationary standard clocks separated by 1 cm in height have a redshift of about 10^-18 according to Einstein's theory of general relativity. Thus, clock comparison allows for accurate height determination in high-performance clock networks. In such networks, frequency differences observed between clock sites and corresponding gravity potential differences can be derived. The heights can be represented as geopotential numbers and measured potential differences between clock locations in a dedicated clock network can be used to estimate the transformation parameters between regional/national height reference frames and resolve distortions in individual height systems. Our study employs chronometric levelling in closed-loop simulations across two different regions, Europe and Brazil. A set of realistic offsets and tilts in the local height data is assumed by considering, e.g., systematic tilts in latitude and longitude direction, errors related to the distance from the tide gauges, the elevation of levelling points, and the presence of noisy levelling lines. External effects such as tidal effects (solid earth tide, ocean load tide, pole tide), propagation errors due to fibre and space link uncertainties, random noise, and outliers are included in the simulation of the unification process. The best configuration is determined by analyzing the standard deviations of the estimated error parameters, which vary based on the spatial distribution of the clocks. An optimal setup includes placing clocks at corners, tide gauges, and the highest points of the local height systems. The added value of chronometric levelling is demonstrated for the realization of an IHRS.

In this work, we develop a new compatible finite element formulation of the thermal shallow water equations that conserves energy and mathematical entropies given by buoyancy-related quadratic tracer variances. Our approach relies on restating the governing equations to enable discontinuous approximations of thermodynamic variables and a variational continuous time integration. A key novelty is the inclusion of centred and upwinded fluxes. The proposed semi-discrete system conserves discrete entropy for centred fluxes, monotonically damps entropy for upwinded fluxes, and conserves energy. The fully discrete scheme reflects entropy conservation at the continuous level. The ability of a new linearised Jacobian, which accounts for both centred and upwinded fluxes, to capture large variations in buoyancy and simulate thermally unstable flows for long periods of time is demonstrated for two different transient case studies. The first involves a thermogeostrophic instability where including upwinded fluxes is shown to suppress spurious oscillations while successfully conserving energy and monotonically damping entropy. The second is a double vortex where a constrained fully discrete formulation is shown to achieve exact entropy conservation in time.

Seismic data inevitably suffers from random noise and missing traces in field acquisition. This limits the utilization of seismic data for subsequent imaging or inversion applications. Recently, dictionary learning has gained remarkable success in seismic data denoising and interpolation. Variants of the patch-based learning technique, such as the K-SVD algorithm, have been shown to improve denoising and interpolation performance compared to the analytic transform-based methods. However, patch-based learning algorithms work on overlapping patches of data and do not take the full data into account during reconstruction. By contrast, the data patches (CSC) model treats signals globally and, therefore, has shown superior performance over patch-based methods in several image processing applications. In consequence, we test the use of CSC model for seismic data denoising and interpolation. In particular, we use the local block coordinate descent (LoBCoD) algorithm to reconstruct missing traces and clean seismic data from noisy input. The denoising and interpolation performance of the LoBCoD algorithm has been compared with that of K-SVD and orthogonal matching pursuit (OMP) algorithms using synthetic and field data examples. We use three quality measures to test the denoising accuracy: the peak signal-to-noise ratio (PSNR), the relative L2-norm of the error (RLNE), and the structural similarity index (SSIM). We find that LoBCoD performs better than K-SVD and OMP for all test cases in improving PSNR and SSIM, and in reducing RLNE. These observations suggest a huge potential of the CSC model in seismic data denoising and interpolation applications., Comment: 56 pages, 19 figures, 7 tables

Planetesimal formation models often invoke the gravitational collapse of pebble clouds to overcome various barriers to grain growth and propose processes to concentrate particles sufficiently to trigger this collapse. On the other hand, the geochemical approach for planet formation constrains the conditions for planetesimal formation and evolution by providing temperatures that should be reached to explain the final composition of planetesimals, the building blocks of planets. To elucidate the thermal evolution during gravitational collapse, we used numerical simulations of a self-gravitating cloud of particles and gas coupled with gas drag. Our goal is to determine how the gravitational energy relaxed during the contraction is distributed among the different energy components of the system, and how this constrains a thermal and dynamical planetesimal's history. We identify the conditions necessary to achieve a temperature increase of several hundred kelvins, and as much as 1600 K. Our results emphasise the key role of the gas during the collapse., Comment: 12 pages, 12 figures, accepted for publication in A&A

Nearly fifty years ago, Roberts (1978) postulated that Earth's magnetic field, which is generated by turbulent motions of liquid metal in its outer core, likely results from a subcritical dynamo instability characterised by a dominant balance between Coriolis, pressure and Lorentz forces. Here we numerically explore the generation of subcritical geomagnetic fields using techniques from optimal control and dynamical systems theory to uncover the nonlinear dynamical landscape underlying dynamo action. Through nonlinear optimisation, via direct-adjoint looping, we identify the minimal seed - the smallest magnetic field that attracts to a nonlinear dynamo solution. Additionally, using the Newton-hookstep algorithm, we converge stable and unstable travelling wave solutions to the governing equations. By combining these two techniques, complex nonlinear pathways between attracting states are revealed, providing insight into a potential subcritical origin of the geodynamo. This paper showcases these methods on the widely studied benchmark of Christensen et al. (2001), laying the foundations for future studies in more extreme and realistic parameter regimes. We show that the minimal seed reaches a nonlinear dynamo solution by first attracting to an unstable travelling wave solution, which acts as an edge state separating a hydrodynamic solution from a magnetohydrodynamic one. Furthermore, by carefully examining the choice of cost functional, we establish a robust optimisation procedure that can systematically locate dynamo solutions on short time horizons with no prior knowledge of its structure., Comment: 21 pages, 8 figures

The nucleosynthetic isotope signatures of meteorites and the bulk silicate Earth (BSE) indicate that Earth consists of a mixture of "carbonaceous" (CC) and "non-carbonaceous" (NC) materials. We show that the fration of CC material recorded in the isotopic composition of the BSE varies for different elements, and depends on the element's tendency to partition into metal and its volatility. The observed behaviour indicates that the majority of material accreted to the Earth was NC-dominated, but that CC-dominated material enriched in moderately-volatile elements by a factor of ~10 was delivered during the last ~2-10% of Earth's acccretion. The late delivery of CC material to Earth contrasts with dynamical evidence for the early implantation of CC objects into the inner solar system during the growth and migration of the giant planets. This, together with the NC-dominated nature of both Earth's late veneer and bulk Mars, suggests that material scattered inwards had the bulk of its mass concentrated in a few, large CC embryos rather than in smaller planetesimals. We propose that Earth accreted a few of these CC embryos while Mars did not, and that at least one of the CC embryos impacted Earth relatively late (when accretion was 90-90% complete). This scenario is consistent with the subsequent Moon-formign impact of a large NC body, as long as this impact did not re-homogenize the entire Earth's mantle.

Accurate hypocenter determination in the Nankai Trough subduction zone is essential for hazard assessment and advancing our understanding of seismic activity in the region. A handy hypocenter determination tool incorporating a realistic 3D velocity structure, accessible to the scientific community, is beneficial. In this study, we developed HypoNet Nankai, a rapid hypocenter determination tool based on a physics-informed neural network (PINN) emulator (surrogate model) for travel time calculations. This tool leverages a PINN trained to predict P-wave travel times between arbitrary underground sources and surface receivers with a realistic 3D P-wave velocity structure model of the Nankai Trough subduction zone that incorporates marine seismic survey data. The PINN embeds physical laws, namely, the Eikonal equation, directly into the loss function of training and circumvents the need for labeled training data. To address the training challenges posed by small-scale features in the velocity model, we employed a simple domain decomposition approach and Fourier feature embedding. Once trained, the PINN immediately infers the P-wave travel time, enabling rapid hypocenter determination. The data size required to store NNs for travel time calculations is significantly smaller than those of conventional travel-time tables. HypoNet Nankai provides high flexibility for addition of new observation points. We verified HypoNet Nankai by comparing its performance with a widely used grid-based numerical method for forward travel time calculations and synthetic hypocenter determination. In both tests, HypoNet Nankai provided results consistent with those for the conventional method. HypoNet Nankai offers a rapid, accurate, and easy-to-use hypocenter determination method for the Nankai Trough subduction zone, with greater data efficiency and extendibility compared to conventional approaches.

Suction stress is a fundamental component for applying the effective stress principle in unsaturated geotechnical engineering problems. The present paper aims to understand how the suction stress profiles get influenced if the $m$ and $n$ parameter of the van-Genuchten SWCC model is completely independent. Through the analysis, it is well noted that for constant air entry value, if the $m$ and $n$ relationships are varied, not only the number and the spread of the characteristic regimes get influenced, but the nature and the trend of the suction stress profiles associated with the regimes also alter significantly. A suggestion of selecting the m and n relationships is proposed for obtaining conservative solutions for geotechnical stability problems. The relevance of considering the unconstrained m parameter is established by computing the tensile crack depth of the clayey slope under steady-state infiltration conditions., Comment: 48 pages, 18 figures, 2 tables

The pendulum is one of the oldest gravimeters, featuring frequency-based readout limited by geometric nonlinearity. While modern gravimeters focus on displacement-based spring-mass or free-fall designs, the advent of nanofabrication techniques invites a revisiting of the pendulum, motivated by the prospect of low-loss, compact, isochronous operation, leveraging precise dimensional control. Here we exploit advances in strain-engineered nanomechanics -- specifically, strained Si$_3$N$_4$ nanoribbon suspensions -- to realize a $0.1$ mg, $32$ Hz torsion pendulum with an ultralow damping rate of $16\,\mu$Hz and a parametric gravity sensitivity of $5$ Hz/$g_0$ ($g_0 = 9.8\;\text{m}/\text{s}^2)$. The low thermal acceleration of the pendulum, $2\times 10^{-9}g_0/\sqrt{\text{Hz}}$, gives access to a parametric gravity resolution of $10^{-8}g_0$ for drive amplitudes of $10\;\text{mrad}$ and integration times within the free decay time, of interest for both commercial applications and fundamental experiments. We present progress toward this goal, demonstrating free and self-sustained oscillators with frequency stabilities as little as $2.5\,\mu$Hz at 200 s, corresponding to a gravity resolution of $5\times 10^{-7}g_0$. We also show how the Duffing nonlinearity of the suspension can be used to cancel the pendulum nonlinearity, paving the way toward a fully isochronous, high-$Q$ micromechanical clock., Comment: 9 pages, 7 figures

A comprehensive geoscientific downscaling model strategy is presented outlining an approach that has evolved over the last 20 years, together with an explanation for its development, its technical aspects, and evaluation scheme. This effort has resulted in an open-source and free R-based tool, 'esd', for the benefit of sharing and improving the reproducibility of the downscaling results. Furthermore, a set of new metrics was developed as an integral part of the downscaling approach which assesses model performance with an emphasis on regional information for society (RifS). These metrics involve novel ways of comparing model results with observational data and have been developed for downscaling large multi-model global climate model ensembles. This paper presents for the first time an overview of the comprehensive framework adopted by the Norwegian Meteorological Institute for downscaling aimed at supporting climate change adaptation. A literature search suggests that this comprehensive downscaling strategy and evaluation scheme are not widely used within the downscaling community. In addition, this strategy involves a new convention for storing large datasets of ensemble results that provides fast access to information and drastically saves data volume., Comment: Was submitted as a discussion paper to GMD (DOI:10.5194/gmd-2021-176) and revised according to criticism, but the reviwers misread and disliked this paper so it was unfarily rejected in the end

Terrestrial experiments that use electrons in Earth as a spin-polarized source have been demonstrated to provide strong bounds on exotic long-range spin-spin and spin-velocity interactions. These bounds constrain the coupling strength of many proposed ultralight bosonic dark-matter candidates. Recently, it was pointed out that a monopole-dipole coupling between the Sun and the spin-polarized electrons of Earth would result in a modification of the precession of the perihelion of Earth. Using an estimate for the net spin-polarization of Earth and experimental bounds on Earth's perihelion precession, interesting constraints were placed on the magnitude of this monopole-dipole coupling. Here we investigate the spin associated with Earth's electrons. We find that there are about $6 \times 10^{41}$ spin-polarized electrons in the mantle and crust of Earth oriented anti-parallel to their local magnetic field. However, when integrated over any spherically-symmetric Earth model, we find that the vector sum of these spins is zero. In order to establish a lower bound on the magnitude of the net spin along Earth's rotation axis we have investigated three of the largest breakdowns of Earth's spherical symmetry: the large low shear-velocity provinces of the mantle, the crustal composition, and the oblate spheroid of Earth. From these investigations we conclude that there are at least $5 \times 10^{38}$ spin-polarized electrons aligned anti-parallel to Earth's rotation axis. This analysis suggests that the bounds on the monopole-dipole coupling that were extracted from Earth's perihelion precession need to be relaxed by a factor of about 2000., Comment: 10 pages, 1 figure, 1 table

Machine learning has great potential to increase crop production and resilience to climate change. Accurate maps of where crops are grown are a key input to a number of downstream policy and research applications. In this proposal, we present preliminary work showing that it is possible to accurately classify crops from time series derived from Sentinel 1 and 2 satellite imagery in Mexico using a pixel-based binary crop/non-crop time series transformer model. We also find preliminary evidence that meta-learning approaches supplemented with data from similar agro-ecological zones may improve model performance. Due to these promising results, we propose further development of this method with the goal of accurate multi-class crop classification in Jalisco, Mexico via meta-learning with a dataset comprising similar agro-ecological zones.

High-resolution seismic reflections are essential for imaging and monitoring applications. In seismic land surveys using sources and receivers at the surface, surface waves often dominate, masking the reflections. In this study, we demonstrate the efficacy of a two-step procedure to suppress surface waves in an active-source reflection seismic dataset. First, we apply seismic interferometry (SI) by cross-correlation, turning receivers into virtual sources to estimate the dominant surface waves. Then, we perform adaptive subtraction to minimise the difference between the surface waves in the original data and the result of SI. We propose a new approach where the initial suppression results are used for further iterations, followed by adaptive subtraction. This technique aims to enhance the efficacy of data-driven surface-wave suppression through an iterative process. We use a 2D seismic reflection dataset from Scheemda, situated in the Groningen province of the Netherlands, to illustrate the technique's efficiency. A comparison between the data after recursive interferometric surface-wave suppression and the original data across time and frequency-wavenumber domains shows significant suppression of the surface waves, enhancing visualization of the reflections for following subsurface imaging and monitoring studies.

On September 16th, 2023, an anomalous 10.88 mHz seismic signal was observed globally, persisting for 9 days. One month later an identical signal appeared, lasting for another week. Several studies have theorized that these signals were produced by seiches which formed after two landslide generated mega-tsunamis in an East-Greenland fjord. This theory is supported by seismic inversions, and analytical and numerical modeling, but no direct observations have been made -- until now. Using data from the new Surface Water Ocean Topography mission, we present the first observations of this phenomenon. By ruling out other oceanographic processes, we validate the seiche theory of previous authors and independently estimate its initial amplitude at 7.9 m using Bayesian machine learning and seismic data. This study demonstrates the value of satellite altimetry for studying extreme events, while also highlighting the need for specialized methods to address the altimetric data's limitations, namely temporal sparsity. These data and approaches will help in understanding future unseen extremes driven by climate change., Comment: 19 pages, 9 figures

Earthquakes cause catastrophic damage to buildings and loss of human life. Civil engineers across the globe design earthquake-resistant buildings to minimize this damage. Conventionally, the structures are designed to resist the translational motions caused by an earthquake. However, with the increasing evidence of rotational ground motions in addition to the translational ground motions due to earthquakes, there is a crucial need to identify if these additional components have an impact on the existing structural design strategies. In this regard, the present study makes a novel attempt to obtain the dynamic properties of a large-scale prototype prestressed reinforced concrete bridge structure using six component (6C) ground motions. The structure is instrumented with conventional translational seismic sensors, rotational sensors and newly developed six-component sensors under operating and externally excited conditions. The recorded data is used to carry out Operational Modal Analysis and Experimental Modal Analysis of the bridge. Modal analysis using the rotational measurements shows that the expected location of maximum rotations on the bridge differs from the maximum translations. Therefore, further understanding the behavior of rotational motions is necessary for developing earthquake-resistant structural design strategies

Formula connecting dips of unmigrated 2D and prestack time migrated constant offset gathers was recently proposed (Singh, 2024). A handy formula for 2D prestack migration aperture was also derived. We extend the same work to the 3D case, that is, we derive prestack time migration aperture, and formula connecting dips as a function of dip of a planar reflector and azimuth w.r.t. the dip direction., Comment: 11 pages, 14 figures

Recent studies showed that seemingly random Slow Slip Events (SSEs) can display chaotic patterns within the largest source of seismic hazards in New Zealand, the Hikurangi subduction zone. Some irregular SSE occurrences are therefore not arbitrary but behave with short-term predictability. However, the forecasting challenge persists as observations remain too short and noisy to constrain purely data-driven solutions, calling for a physics-based modelling approach. Here we propose a physical model of two coupled oscillators, each capturing the behaviour of a single slow-slip patch, for the deep Kaimanawa and the shallow East Coast SSEs respectively. The simplified model successfully reproduces the type of chaotic behaviour observed at the Global Navigational Satellite System station in Gisborne, yielding SSEs of appropriately varying amplitude and duration. Those results reveal that the multi-physics response of the shear zone strongly controls the underlying system, even before accounting for any geometrical complexity or distribution of material properties.

We inquire into the morphodynamics of barchans under seasonal flows. For that, we carried out grain-scale numerical computations of a subaqueous barchan exposed to two-directional flows, and we varied the angle and frequency of oscillations. We show that when the frequency is lower than the inverse of the characteristic time for barchan formation, the dune adapts to the new flow direction and recovers the barchan shape while losing less grains than under one-directional flow. For higher frequencies, the dune has not enough time for adaptation and becomes more round while losing more grains. For both cases, we show, for the first time, the typical dynamics of grains (trajectories and forces). In particular, the round barchans are similar to the so-called occluded dunes observed on Mars, where seasons have very high frequencies compared to the dune timescale, different from Earth. Our results represent a possible explanation for that shape., Comment: Geomorphology (2024). This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

This article describes condensation of the elements and use of condensation temperatures (Tcond) to interpret the volatility trend of the Earth. Major points are: (1) a listing of updated 50% Tcond for all natural elements and Pu at 1e-2 to 1e-8 bar pressure for solar composition matter. (2) The Tcond are mainly controlled by the Gibbs energy of condensation reactions and also by the Gibbs energy of ideal mixing if elements (compounds) condense in a solution. The Gibbs energy change of non-ideal solution (activity coefficients not equal to 1) is a secondary effect. (3) The theoretically correct relationship between Tcond and fraction condensed is derived from mass balance and chemical thermodynamic considerations. (4) The maximum amount of element condensed per 1/T, is at the inflection point in the logistic (sigmoid) curve for an element, which is also at (or close to) the 50% Tcond. (5) Plots of normalized elemental abundances versus 50% Tcond (volatility trends) are qualitative indicators of elemental fractionations due to volatility. (6) Volatility trend plots for average elemental abundances in CM, CO, CV, CR, H, L, LL, EH, EL chondrites show different trends for moderately and highly volatile elements, which may be linear, curved, a step function, or plateau. Comparison of three abundance sets for CM and CV chondrites shows trends depend on which elements are plotted, which data sources are used, and which temperature range is considered. (7) Proposed mechanisms for volatile element depletion in carbonaceous chondrites and the Earth are reviewed. (8) Possible implications of volatile element abundances in the bulk silicate Earth are discussed., Comment: 50 pages, 1 table, 15 figures. Preprint submitted to Space Science Reviews October 2024

This study addresses the boundary artifacts in machine-learned (ML) parameterizations for ocean subgrid mesoscale momentum forcing, as identified in the online ML implementation from a previous study (Zhang et al., 2023). We focus on the boundary condition (BC) treatment within the existing convolutional neural network (CNN) models and aim to mitigate the "out-of-sample" errors observed near complex coastal regions without developing new, complex network architectures. Our approach leverages two established strategies for placing BCs in CNN models, namely zero and replicate padding. Offline evaluations revealed that these padding strategies significantly reduce root mean squared error (RMSE) in coastal regions by limiting the dependence on random initialization of weights and restricting the range of out-of-sample predictions. Further online evaluations suggest that replicate padding consistently reduces boundary artifacts across various retrained CNN models. In contrast, zero padding sometimes intensifies artifacts in certain retrained models despite both strategies performing similarly in offline evaluations. This study underscores the need for BC treatments in CNN models trained on open water data when predicting near-coastal subgrid forces in ML parameterizations. The application of replicate padding, in particular, offers a robust strategy to minimize the propagation of extreme values that can contaminate computational models or cause simulations to fail. Our findings provide insights for enhancing the accuracy and stability of ML parameterizations in the online implementation of ocean circulation models with coastlines.

Seismic exploration is currently the most important method for understanding subsurface structures. However, due to surface conditions, seismic receivers may not be uniformly distributed along the measurement line, making the entire exploration work difficult to carry out. Previous deep learning methods for reconstructing seismic data often relied on additional datasets for training. While some existing methods do not require extra data, they lack constraints on the reconstruction data, leading to unstable reconstruction performance. In this paper, we proposed a zero-shot self-consistency learning strategy and employed an extremely lightweight network for seismic data reconstruction. Our method does not require additional datasets and utilizes the correlations among different parts of the data to design a self-consistency learning loss function, driving a network with only 90,609 learnable parameters. We applied this method to experiments on the USGS National Petroleum Reserve-Alaska public dataset and the results indicate that our proposed approach achieved good reconstruction results. Additionally, our method also demonstrates a certain degree of noise suppression, which is highly beneficial for large and complex seismic exploration tasks., Comment: 12 pages, 8 figures

Self-similarity indicates that large and small earthquakes share the same physics, where all variables scale with rupture length $L$. Here I show that rupture tip acceleration during the start of dynamic rupture (break-out phase) is also self-similar, scaling with $L_c$ in space and $L_c/C_{lim}$ in time (where $L_c$ is the breakout patch length and $C_{lim}$ the limiting rupture velocity in the subsonic regime). Rupture acceleration in the breakout phase is slower for larger initial breakout patches $L_c$. Because small faults cannot host large breakout patches, a large and slower initial breakout may be indicative of a potentially large final earthquake magnitude. Initial moment rate $\dot{M}_o$ also grows slower for larger $L_c$, therefore it may reflect fault dimensions and carry a probabilistic forecast of magnitude as suggested in some Early Warning studies. This result does not violate causality and is fully compatible with the shared fundamental, self-similar physics across all the magnitude spectrum., Comment: 22 pages including supplementary information at bottom of article, 12 figures

This monograph presents advancements in Airborne Gamma-Ray Spectrometry (AGRS), a critical tool for emergency response to radiological incidents such as severe nuclear accidents or nuclear weapon detonations. Current AGRS calibration and data evaluation methods struggle to accurately quantify many radioactive materials expected in radiological emergencies, limiting the risk assessment and, hence, the effectiveness of emergency response actions. To address these limitations, this work introduces a full spectrum numerical modeling approach that features three key innovations: high-fidelity Monte Carlo simulations that combine an advanced scintillation physics model with detailed geometric representations of the aircraft and detector system; a surrogate model that replicates the Monte Carlo simulations with significantly reduced computation time; and a data evaluation methodology that leverages the surrogate model within a Bayesian inversion framework, enabling the quantification of arbitrarily complex gamma-ray fields. The methodology presented here, rigorously validated through laboratory and field measurements, achieves not only a significant improvement in accuracy and sensitivity over traditional methods but also substantially expands the operational capabilities of AGRS systems for both emergency response scenarios and geophysical surveys. These innovations lay the groundwork for establishing a new global standard for AGRS, ultimately supporting better-informed protective actions and reducing health risks during radiological emergencies.

The superionic state represents a fundamental phase of matter where liquid-like mobility emerges within a solid crystalline lattice. This peculiar state has recently been discovered in the Earth's inner core. Despite extensive research on the kinetics of the superionic state and its geophysical impact on the Earth's core, the equilibration of the superionic phase with the liquid solution under core conditions has yet to be discovered. In this study, we develop a method to calculate the ab initio Gibbs free energy and the superionic-liquid phase diagram for the Fe1-xOx system under conditions near the Earth's inner core boundary. Our findings indicate oxygen can form superionic states in hcp and bcc iron. We elucidate the stability fields of these superionic phases, which vary significantly with oxygen stoichiometry. Furthermore, we show that the oxygen concentration in the Earth's inner core is likely higher than previously estimated due to the superionic state. This work offers a quantitative approach to studying the equilibrium between liquid and superionic solutions in Fe-light element systems under the extreme conditions of the Earth's core.

The Hadean, once thought to be uninhabitable and tumultuous, has more recently been recontextualized as a clement time in which oceans, land, and life likely appeared on Earth. This non-exhaustive chapter follows multiple threads from planet formation to the origin of life. We place significant emphasis on the solar system context for the Earth, the timing and nature of crustal formation and the evolution of the surface and atmosphere. Several scenarios for prebiotic chemistry are also discussed including atmospheric photochemistry, wet-dry and freeze-thaw cycles, and hydrothermal vent systems. We attempt to draw connections between the large-scale, planetary processes and various origin of life pathways to illustrate possible overlaps and correlations. In detail, we conclude with and discuss the "impact of impacts" to show how asteroid and comet impacts during the Hadean may have affected many of these processes and scenarios, from generating land to altering the chemical composition and oxidation state of the early Earth's atmosphere and surface., Comment: 18 pages, 5 figures, accepted for publication in "The Archean Earth" (ed.: M. Homan, P. Mason, R. Ernst, T. Lyons, C. Heubeck, D. Papineau, E. Stueeken, R. Mazumder, A. Webb, W. Altermann), Elsevier

This paper presents a novel framework for full-waveform seismic source inversion using simulation-based inference (SBI). Traditional probabilistic approaches often rely on simplifying assumptions about data errors, which we show can lead to inaccurate uncertainty quantification. SBI addresses this limitation by building an empirical probabilistic model of the data errors using machine learning models, known as neural density estimators, which can then be integrated into the Bayesian inference framework. We apply the SBI framework to point-source moment tensor inversions as well as joint moment tensor and time-location inversions. We construct a range of synthetic examples to explore the quality of the SBI solutions, as well as to compare the SBI results with standard Gaussian likelihood-based Bayesian inversions. We then demonstrate that under real seismic noise, common Gaussian likelihood assumptions for treating full-waveform data yield overconfident posterior distributions that underestimate the moment tensor component uncertainties by up to a factor of 3. We contrast this with SBI, which produces well-calibrated posteriors that generally agree with the true seismic source parameters, and offers an order-of-magnitude reduction in the number of simulations required to perform inference compared to standard Monte Carlo techniques. Finally, we apply our methodology to a pair of moderate magnitude earthquakes in the North Atlantic. We utilise seismic waveforms recorded by the recent UPFLOW ocean bottom seismometer array as well as by regional land stations in the Azores, comparing full moment tensor and source-time location posteriors between SBI and a Gaussian likelihood approach. We find that our adaptation of SBI can be directly applied to real earthquake sources to efficiently produce high quality posterior distributions that significantly improve upon Gaussian likelihood approaches., Comment: 22 + 11 pages, 11 + 11 figures

One way in which we can attempt to relate chemical pathways to geochemical environments is by studying the kinetics of a given sequence of reactions and identifying the conditions under which this chemistry is the most productive. Many prebiotic reactions rely on a source of fixed carbon, therefore chemical pathways that suggest prebiotically plausible ways of fixing carbon are of significant interest. One such pathway is the carboxysulfitic reaction network which uses solvated electrons, produced as a result of electron photodetachment from sulfite, to reduce carbon. In this work we explore carboxysulfitic chemistry at three different pH values: 6, 9, and 12. We utilise a new light source, that matches the broadband spectral shape of the young Sun, to irradiate a mixture of bicarbonate and sulfite. We determine the rate equation for the production of formate from these compounds and find the order to be 0.71 $\pm$ 0.12 with respect to bicarbonate and -0.60 $\pm$ 0.10 with respect to sulfite. Following this, we determine rate constants for the production of formate considering two different mechanisms. We find this chemistry to be feasible at all three of the pH values tested, with the magnitude of the rate constants being highly dependent on the assumed mechanism. We suggest that these results may have implications for Mars Sample Return owing to Jezero Crater having had lakes similar to those in which we propose carboxysulfitic chemistry to have been the most productive. Due to Mars' relatively unaltered surface, we propose that Mars Sample Return missions could look for preserved tracers of this chemistry, shedding light on Mars' past conditions and its potential for having hosted life., Comment: 37 pages (+ 59 pages of SI), 5 figures (+ 42 figures in the SI). This is the Accepted Manuscript version of a work that was subsequently published in ACS Earth and Space Chemistry, published by the American Chemical Society after peer review and technical editing by the publisher. The final edited and published version can be accessed at DOI: 10.1021/acsearthspacechem.4c00083

Sea ice plays a crucial role in the climate system, particularly in the Marginal Ice Zone (MIZ), a transitional area consisting of fragmented ice between the open ocean and consolidated pack ice. As the MIZ expands, understanding its dynamics becomes essential for predicting climate change impacts. However, the role of clouds in these processes has been largely overlooked. This paper addresses that gap by developing an idealized coupled atmosphere-ocean-ice model incorporating cloud and precipitation effects, tackling both forward (simulation) and inverse (data assimilation) problems. Sea ice dynamics are modeled using the discrete element method, which simulates floes driven by atmospheric and oceanic forces. The ocean is represented by a two-layer quasi-geostrophic (QG) model, capturing mesoscale eddies and ice-ocean drag. The atmosphere is modeled using a two-layer saturated precipitating QG system, accounting for variable evaporation over sea surfaces and ice. Cloud cover affects radiation, influencing ice melting. The idealized coupled modeling framework allows us to study the interactions between atmosphere, ocean, and sea ice floes. Specifically, it focuses on how clouds and precipitation affect energy balance, melting, and freezing processes. It also serves as a testbed for data assimilation, which allows the recovery of unobserved floe trajectories and ocean fields in cloud-induced uncertainties. Numerical results show that appropriate reduced-order models help improve data assimilation efficiency with partial observations, allowing the skillful inference of missing floe trajectories and lower atmospheric winds. These results imply the potential of integrating idealized models with data assimilation to improve our understanding of Arctic dynamics and predictions.

Current models of chemical evolution during star and planetary formation rely on the presence of dust grains to act as a third body. However, they generally ignore the reactivity of the dust grains themselves. Dust grains present in the protoplanetary phase will evolve as the solar system forms and, after protoplanets have appeared, they will be constantly delivered to their surfaces in the form of large aggregates or meteorites. Chondritic meteorites are mostly unaltered samples of the dust present in the first stages of the Solar System formation, that still arrive nowadays to the surface of Earth and allow us to study the properties of the materials forming the early Solar System. These materials contain, amongst others, transition metals that can potentially act as catalysts, as well as other phases that can potentially react in different astrophysical conditions, such as FeS. In this work, we present the reactivity of chondritic meteorites under \hydrogen-rich atmospheres, particularly towards the reduction of FeS for the formation of H2S and metallic Fe during the early phases of the planetary formation. We present the obtained results on the reaction rates and the percentage of FeS available to react in the materials. Additionally, we include a computational study of the reaction mechanism and the energetics. Finally, we discuss the implications of an early formation of H2S in planetary surfaces., Comment: Approved for publication in MNRAS

Geomagnetically Induced Current (GIC) refers to the electromagnetic response of the Earth and its conductive modern infrastructures to space weather and would pose a significant threat to high-voltage power grids designed for the alternative current operation. To assess the impact of space weather on the power grid, one needs to calculate the GIC on a national or continental scale. In this study, we developed a smart and parallelized GIC modelling algorithm, Graph GIC. This algorithm deploys a graph representing a power grid in a single-line diagram, in which substations/transformers act as nodes and transmission lines as edges. With these denotations, a power grid and its electric parameters are mathematically represented with an adjacency matrix and an admittance matrix. We used sparse matrix and parallelisation techniques to expedite the intensive computation in cases of large-scale power grids. The Graph GIC was validated with a benchmark grid, applied to the GIC calculation of the 500 kV power grid of Guangdong, China, and conducted preliminary analysis on the grid's susceptibility to geomagnetic storms. The Graph GIC algorithm has the advantage of an intuitive and highly scalable graph representation of a power grid at any scale. It achieves high-accuracy calculation and a speedup of about 18 times after parallelisation. This algorithm could be applied to assess the impact of space weather on a power grid up to continental scales and could be incorporated into global space weather modelling frameworks., Comment: 19 pages, 10 figures

Accurate damage prediction is crucial for disaster preparedness and response strategies, particularly given the frequent earthquakes in Turkey. Utilizing datasets on earthquake data, infrastructural quality metrics, and contemporary socioeconomic factors, we tested various machine-learning architectures to forecast death tolls and fatalities per affected population. Our findings indicate that the Random Forest model provides the most reliable predictions. The model highlights earthquake magnitude and building stability as the primary determinants of damage. This research contributes to the reduction of fatalities in future seismic events in Turkey.

The growing number of seismic and elastic attributes poses a challenge, making the full benefit from each attribute in characterizing geological formation very difficult, if not impossible. Various approaches are routinely employed to select the best attributes for specific purposes. Machine learning (ML) algorithms have demonstrated good capabilities in combining appropriate attributes to address reservoir characterization problems. This study aims to use and combine seismic and elastic attributes to detect hydrocarbon-saturated reservoirs, source rock, and seal rocks in the Poseidon field, Offshore Australia. A large number of attributes are extracted from seismic data and from impedance data. Artificial Neural Networks (ANN) are implemented to combine the extracted attributes and convert them into Resistivity volume, and Gamma Ray volume from which Shale probability volume, Sand volume probability volume, Effective Porosity volume, and Gas Chimney Probability Cubes. The cubes are deployed for a detailed analysis of the petroleum system in the area. The produced Shale volume and Resistivity cube helped delineate the seal rock and source rock in the area. Next, the reservoir intervals were identified using Porosity, Shale, and Resistivity volumes. A pre-trained Convolutional Neural Network (CNN) is trained using another carefully selected attribute set to detect subtle faults that hydrocarbons might migrated through from source rock to trap. The integration of all the extracted cubes contributed to find new prospects in the area and assess their geological probability of success. The proposed approach stands out for its multi-physical attribute integration, Machine Learning and Human expertise incorporation, possible applicability to other fields., Comment: 20 pages, 10 figures

Full waveform inversion (FWI) is capable of reconstructing subsurface properties with high resolution from seismic data. However, conventional FWI faces challenges such as cycle-skipping and high computational costs. Recently, deep learning method has emerged as a promising solution for efficient velocity estimation. We develop DiffusionVel, a data-driven technique based on the state-of-the-art generative diffusion models (GDMs) with integration of multiple information including seismic data, background velocity, geological knowledge, and well logs. We use two separate conditional GDMs, namely the seismic-data GDM and the well-log GDM, and an unconditional GDM, i.e., the geology-oriented GDM, to adapt the generated velocity model to the constraints of seismic data, well logs, and prior geological knowledge, respectively. Besides, the background velocity can be incorporated into the generated velocity model with a low-pass filter. The generation of these GDM are then combined together with a weighted summation in the sampling process. We can flexibly control the constraints from each information by adjusting the weighting factors. We make a comprehensive comparison between the proposed DiffusionVel and three previously-developed methods including conventional FWI, InversionNet, and VelocityGAN by using the OpenFWI datasets and the Hess VTI model example. The test results demonstrate that the proposed DiffusionVel method predicts the velocity model reasonably by integrating multiple information effectively., Comment: This work has been submited to Geophysics for peer reviews

Metallic hydrogen is the most common condensed material in the universe, comprising the centre of gas giant planets. However, experimental studies are extremely challenging, and most of our understanding of this material has been led by theory. Chemistry in this environment has not been probed experimentally, so here we examine hydrocarbon chemistry in metallic hydrogen using density functional theory calculations. We find that carbon and oxygen react with metallic hydrogen to produce an entirely new type of hydrocarbon chemistry based on sixfold coordinated carbon with organic-style molecules CH6, C2H8, C3H10 OH3 NH4 and CH4OH. These are charged molecules stabilised by the metallic environment. Their associated electric fields are screened, giving oscillation in the surrounding electron and proton densities. In view of the excess hydrogen we refer to them as hypermethane, hyperethane etc. The relationship to traditional chemistry is that the metallic background acts as an electron donor and stabilizes negatively charged ions. This enables the formation of six covalent bonds per carbon atom, or three per oxygen atom. This demonstrates that organic chemistry may take place in very different environments from those found on earth, and may be common throughout the universe., Comment: 11 pages, 3 figures, submitted to Nature Communications

Pore-scale modeling of rock images based on information in 3D micro-computed tomography data is crucial for studying complex subsurface processes such as CO2 and brine multiphase flow during Geologic Carbon Storage (GCS). While deep learning models can generate 3D rock microstructures that match static rock properties, they have two key limitations: they don't account for the spatial distribution of rock properties that can have an important influence on the flow and transport characteristics (such as permeability and relative permeability) of the rock and they generate structures below the representative elementary volume (REV) scale for those transport properties. Addressing these issues is crucial for building a consistent workflow between pore-scale analysis and field-scale modeling. To address these challenges, we propose a two-stage modeling framework that combines a Vector Quantized Variational Autoencoder (VQVAE) and a transformer model for spatial upscaling and arbitrary-size 3D porous media reconstruction in an autoregressive manner. The VQVAE first compresses and quantizes sub-volume training images into low-dimensional tokens, while we train a transformer to spatially assemble these tokens into larger images following specific spatial order. By employing a multi-token generation strategy, our approach preserves both sub-volume integrity and spatial relationships among these sub-image patches. We demonstrate the effectiveness of our multi-token transformer generation approach and validate it using real data from a test well, showcasing its potential to generate models for the porous media at the well scale using only a spatial porosity model. The interpolated representative porous media that reflect field-scale geological properties accurately model transport properties, including permeability and multiphase flow relative permeability of CO2 and brine., Comment: 24 pages

The analogy between frictional cracks, propagating along interfaces in frictional contact, and ordinary cracks in bulk materials is important in various fields. We consider a stress-controlled frictional crack propagating at a velocity $c_{\rm r}$ along an interface separating two strips, each of height $H$, the frictional counterpart of the classical problem of a displacement-controlled crack in a strip, which played central roles in understanding material failure. We show that steady-state frictional cracks in a strip geometry require a nonmonotonic dependence of the frictional strength on the slip velocity and, in sharp contrast to their classical counterparts, feature a vanishing stress drop. Here, rupture is driven by energy flowing to its edge from behind, generated by an excess power of the external stress, and to be accompanied by an increase in the stored elastic energy, in qualitative contrast to the classical counterpart that is driven by the release of elastic energy stored ahead of the propagating edge. Finally, we derive a complete set of mesoscopic and macroscopic scaling relations for frictional cracks in a strip geometry and demonstrate that the stress singularity near their edges is proportional to $(\Delta{v}/c_{\rm r})\sqrt{H}$, where $\Delta{v}$ is the slip velocity rise accompanying their propagation., Comment: 6 papges, 2 figures + Supplementary material + bibliographical list

An application based on graphical processing units (GPUs) applied to 3-D digital images is described for computing the linear anisotropic elastic properties of heterogeneous materials. The application can also retrieve the property contribution tensors of individual inclusions of any shape. The code can be executed on professional GPUs as well as on a basic laptop or personal computer Nvidia GPUs. The application is extremely fast: a calculation of the effective elastic properties of volumes consisting of about 7 million voxel elements ($191^3$) takes less than 4 seconds of computational time using a single A100 GPU; 3 minutes for 100 million voxel elements ($479^3$) using a single A100 GPU; 14 minutes for 350 million voxel elements ($703^3$) using a single A100 GPU. Several comparisons against analytical solutions are provided. In addition, an evaluation of the anisotropic effective elastic properties of a 3-D digital image of a cracked Carrara marble sample is presented. The software can be downloaded from a permanent repository Zenodo, the link with a doi is given in the manuscript.

Geologic CO$_2$ storage is an important strategy for reducing greenhouse gas emissions to the atmosphere and mitigating climate change. In this process, coupling between mechanical deformation and fluid flow in fault zones is a key determinant of fault instability, induced seismicity, and CO$_2$ leakage. Using a recently developed methodology, PREDICT, we obtain probability distributions of the permeability tensor in faults from the stochastic placement of clay smears that accounts for geologic uncertainty. We build a comprehensive set of fault permeability scenarios from PREDICT and investigate the effects of uncertainties from the fault zone internal structure and composition on forecasts of CO$_2$ permanence and fault stability. To tackle the prohibitively expensive computational cost of the large number of simulations required to quantify uncertainty, we develop a deep-learning-based surrogate model capable of predicting flow migration, pressure buildup, and geomechanical responses in CO$_2$ storage operations. We also compare our probabilistic estimation of CO$_2$ leakage and fault instability with previous studies based on deterministic estimates of fault permeability. The results highlight the importance of including uncertainty and anisotropy in modeling of complex fault structures and improved management of geologic CO$_2$ storage projects.

This paper investigates hydraulic fracture in a media with periodic heterogeneous toughness. Results for the plane-strain (KGD) model are analysed. The energy distribution as the fracture propagates is examined, along with the evolution of the crack geometry. It is shown that the solid layer acts as an elastic battery, discharging to promote rapid propagation through weaker material layers. The limiting behaviour as length $L\to\infty$ is discussed. The velocity of the fluid throughout the crack length is also considered. For fractures in high-toughness material it is shown that fluid backflow can occur, with its profile dependent on the toughness distribution. The implications of these findings are discussed., Comment: 34 pages, 22 figures

The Greenland Ice Sheet may be nearing a tipping point, transitioning to permanent melting. This article analyses two melt rate time series using the Bayesian Langevin estimation (BLE), providing further evidence for destabilizing melt dynamics, along with new insights from the method's nonlinear parameterisation. Comparing the results for Western Central Greenland and the Nuussuaq peninsula suggests that inland melt dynamics may approach less stable coastal dynamics on slow and fast scales. Both datasets show a significant increase in fast-scale amplitudes since the 1970s, possibly driven by large-scale atmospheric fluctuations. Additionally, the BLE's nonlinear drift proves crucial for obtaining these results, as a linear Ornstein-Uhlenbeck process fails to capture these dynamics due to the oversimplification of a strictly positive autocorrelation function., Comment: 8 pages, 2 figures. Supplementary information, data, and code available online