Papers

We present a statistical evidence of a temporal correlation between ionospheric storm-enhanced density (SED) and plasmaspheric plumes during magnetic storms. We identified SED plumes in 75 storms between 2010-2024 with sufficient total electron content (TEC) coverage. We also identified plasmaspheric plumes during these storms using a plasmapause asymmetry (PPA) index derived from the machine-learning-based DEN3D model. We found that all SED plumes coincided with plasmaspheric plumes. The start and end times of both phenomena exhibit near-zero lag within a half-hour delay, confirming their synchronized evolution. For the first time, superposed epoch analyses reveal that plume onsets align with solar wind driving and geomagnetic activity, including peaks in the electric field, coupling function, auroral electrojet indices, and the asymmetric ring current index, all of which facilitate plume formation. This work provides timely new insights into magnetosphere-ionosphere coupling processes and demonstrates the utility of machine-learning-based diagnostics for space physics/weather.

The cryosphere regulates global cycles of water, energy, and carbon, affecting societies and ecosystems. By assimilating observations into models, we can infer the past trajectory of state variables, generating a cryospheric reanalysis. Existing cryospheric reanalyses typically treat each water year independently, with no pooling of information across years. With sparse and noisy observations, such 'climate-assumed' reanalyses perform poorly, reverting to an assumed yet uncalibrated background climatology. Conversely, Bayesian calibration with static parameters completely pools information across water years, inferring climatological parameters without capturing inter-annual variability. Here, we propose a new hierarchical Bayesian approach using partial pooling, which we coin 'climate-informed' cryospheric reanalysis. It jointly infers state trajectories, annual parameters, and local climatological hyperparameters using a nested hybrid particle smoothing workflow. We test the approach through three experiments by assimilating in situ snow water equivalent, satellite-based fractional snow-covered area, and glacier-wide mass balance data into a temperature index model at eight study areas, from the Swiss Alps to the High Arctic. Hierarchical reanalysis with partial pooling outperforms annually independent cryospheric reanalysis (no pooling) and static parameter calibration (complete pooling) for all experiments during calibration and for all but one during validation. Overall, it yields a 51% improvement in continuous ranked probability score relative to the prior, markedly exceeding that of annual reanalysis (38%) and static calibration (19%). Compared to a particle Markov chain Monte Carlo benchmark, our workflow reduces computational cost by several orders of magnitude through recycling and expectation maximization, with comparable or improved skill.

Compound coastal flooding (CCF) arises from interacting coastal, precipitation, and river processes, yet modeling workflows often separate simulation, evaluation, and impact analysis. We present AutoCF, an automated ecosystem integrating data harmonization, model construction, observational evaluation, exposure analysis, complete factorial driver attribution, and cross-platform execution. The automated Hurricane Harvey simulation achieves a median root mean square error of 0.147 m and correlation of 0.951 across eight observational gauges, and a correlation of 0.942 with 55 high-water marks. Maximum water-level fields from CPU and GPU implementations agree within 0.01 m for 98.8% of cells. Attribution analysis shows that during Harvey, precipitation dominated building and population exposure, whereas coastal forcing becomes increasingly important for deep and persistent inundation. The introduced Driver Impact Shift metric further quantifies whether each driver contributed disproportionately to societal consequences relative to its flooded-area contribution. Overall, AutoCF provides a reproducible pathway from CCF model construction to evaluation and driver-specific impact interpretation.

Artificial intelligence is increasingly deployed in critical industrial domains, including healthcare, energy grids, subsurface exploration, where failures can have severe consequences for human safety, system stability, and economic outcomes. Yet AI is still evaluated primarily through benchmark accuracy, a model-centric metric that fails to capture the structural complexity and risks of real-world deployment. We propose a framework that views industrial AI reliability as a problem of structural alignment across four interacting worlds: physical, representational, machine, and human cognitive. These worlds are connected through two interfaces: digitalization, linking physical reality to computational representations, and goal encoding, translating human cognition to the machine objectives. Together, they define the space of admissible solutions. We characterize the solution space through four attributes: existence, non-uniqueness, robustness, and interpretability and show how mismatches arise at interfaces and propagate across worlds to produce reliability failures. Applications to healthcare, energy grids, and subsurface exploration illustrate that although dominant failure modes differ across domains, for example, interpretability in healthcare, robustness in energy grids, and non-uniqueness in subsurface exploration, all originate from a shared structural mechanism. By shifting the focus from model-centric evaluation to system-level alignment, this framework offers a principled foundation for assessing and governing reliability in industrial AI systems.

The Surface Water and Ocean Topography (SWOT) mission offers unprecedented freshwater monitoring capabilities through its innovative wide-swath measurement system, which generates several data products, including the high-resolution pixel cloud (PIXC) product. However, the native PIXC water classification remains prone to systematic misclassification in urban environments, where strong radar returns from non-water surfaces are the predominant error sources. We present a deep learning approach that enhances land-water classification directly on SWOT PIXC data based on a dynamic graph convolutional neural network that simultaneously exploits spatial proximity and feature similarity to derive binary land-water class labels. The model is trained on a full year of PIXC data for the Dallas-Fort Worth metropolitan area using pixel-level ground-truth class labels derived from the DSWx-HLS product, which provides water-class labels based on Landsat and Sentinel-2 at a 30\,m spatial resolution. Against the DSWx-derived reference, the proposed method increases the mean scene-level F1 score against the DSWx-derived reference from 0.52 to 0.86 on the temporally independent test set and from 0.43 to 0.74 on the spatiotemporal test set, relative to the native PIXC classification. These results demonstrate that dynamic graph neural networks are well-suited to the irregular, point-cloud-like structure of PIXC data and offer a scalable path toward more reliable urban flood monitoring and freshwater mapping at the high spatial resolution provided by the SWOT PIXC product.

Denoising is a long-standing and widely-concerned topic in seismic data processing, for it can significantly increase the signal-to-noise ratio of seismic data. Numerous deep learning (DL) methods have shown promising denoising performance, but most of them are task-specific and focus on a certain type of seismic background noise. The real condition that seismic datasets are often contaminated by various types of noises motivates us to explore a well-generalized and versatile DL model for seismic data denoising. Recently, in the fields of computer vision and natural language processing, foundation models (FMs) pre-trained on vast datasets demonstrate outstanding adaptability and generality across diverse downstream tasks. This paradigm offers a promising path to address the challenges faced by task-specific DL denoising models, such as poor generalization, retraining from scratch for different noise, and the lack of labeled data. We first provide a brief review of traditional seismic denoising methods, followed by a comprehensive review of existing DL-based denoising methods categorized by noise type. Furthermore, we conduct a case study on a dedicated seismic denoising foundation model termed SeisDeFM. This is the first study in geophysical research to develop and validate a seismic denoising foundation model on pre-stack gathers with diverse noise conditions. Experimental results demonstrate that, compared with task-specific DL baselines, SeisDeFM achieves superior denoising performance and cross-noise generalization by the advantages of sufficient pre-training and appropriate downstream adaptation, and it effectively preserves weak reflection events while suppressing complex noise in pre-stack seismic data.

Plume-laden convection plays a fundamental role in thermal transport in geophysical and industrial flows. While the dynamics of isolated laminar plumes have been extensively studied, their collective interactions and evolution in a multi-plume system remains poorly quantified due to the lack of four-dimensional (4D, in space and time) flow measurements for viscous convection, common in the turbulence community. Here we present a time-resolved experimental study of clustered plume dynamics in a high-Prandtl-number Rayleigh-Bénard system with temperature-dependent viscosity from 4D velocity measurements. Building on our companion methodology (Bao and Lithgow-Bertelloni, 2025), we characterize the spatiotemporal distribution, morphological evolution, and interaction modes of tens of coexisting plumes. We identify a wide spectrum of dynamical behaviors–merging, splitting, branching, pulsing, and head detachment–that arise from coupled interactions among plumes, multiscale flow, and the evolving thermal boundary layers. We synthesize prior behaviors into a unified, time-resolved framework of plume evolutionary pathways. Characteristic length and timescales, including spacing and initiation times, are compared with previous theoretical and experimental studies. We find thinner plumes with greater minimum spacing. The fraction of isolated plumes (e.g., Hawaii-like) varies dramatically (0–60%) as the flow evolves. Our results provide new insight into plume self-organization and spatiotemporal variability under Earth-like conditions, crucial for understanding geochemical evolution and mapping geochemical anomalies from hotspot lavas to deep sources. These results quantitatively complement previous experimental work and numerical studies on interacting plumes, serving as a benchmark for comparison with geodynamical simulations of mantle convection.

Laboratory investigation of convective flow in viscous fluids is crucial for various engineering and geophysical applications, including vigorous convection in planetary mantles, which involves multiple rising plumes. Many experimental techniques have been developed to visualize the flow and plumes, but most previous measurements remain largely qualitative. Here we present a method to measure and analyze four-dimensional (4D) plume-bearing viscous fluid flow, extending volumetric velocimetry approaches to the high Prandtl-number laminar regime relevant to mantle convection. First, a customized three dimensional (3D) Scanning Stereoscopic Particle Image Velocimetry (SSPIV) system is applied to a Rayleigh-Bénard experiment suitable to study the dynamics of Earth's interior. We report the first quantitative 4D velocity measurements of multiple interacting laminar plumes at high Prandtl numbers. The raw velocity data are postprocessed with a Lagrangian Coherent Structure (LCS) and cluster analysis pipeline, tailored for clusters of viscous plumes, which allows quantitative, transport-based material boundary tracking of the plumes in space and time. We show example results to demonstrate the power of our method while fully exploring the dynamics in a companion paper Bao and Lithgow-Bertelloni (2025). Our analysis quantifies the rich dynamical behavior of multiple interacting plumes. We suggest our method of measurement and analysis can help better quantify the morphology, interaction, and evolutionary pathways of plumes feeding Earth's volcanic hotspots and other planetary interiors.

Surface-wave tomography provides important constraints on the crust and upper mantle, but directly inverting millions of measurements for three-dimensional structure remains computationally demanding. We develop an efficient Gauss–Newton framework for one-step surface-wave tomography that jointly inverts multiperiod interstation traveltimes for shear-wave velocity, accounting for both lateral wave propagation and depth sensitivity. Implicit differentiation of the converged discrete Eikonal equations provides individual traveltime sensitivity kernels, which are constructed efficiently by reusing the Eikonal dependency structure across receivers. A factorized Jacobian retains these kernels separately from shared model and dispersion mappings, reducing storage and repeated-product costs within each linearized solve. Synthetic experiments show that the framework recovers three-dimensional velocity anomalies with fewer model updates and lower model errors than the tested comparison workflows at similar data fits. Computational benchmarks demonstrate substantial savings in operator storage and the cost of linearized model updates compared with using explicitly assembled Jacobians. We apply the framework to 6.34 million Rayleigh-wave phase traveltimes across the contiguous United States. The recovered model captures major crustal and uppermost mantle velocity variations, including the broad contrast between the tectonically active western United States and the continental interior, consistent with previous surface-wave studies. These results demonstrate a practical Gauss–Newton approach to imaging the crust and upper mantle using large surface-wave data sets.

Sites hosting strong-motion stations in the Central and Eastern United States are commonly characterized using proxy-based parameters, limiting the ability to identify the physical causes of large ground-motion residuals. This study investigates the amplification mechanisms at two New York strong-motion stations that recorded some of the largest positive 1-Hz pseudospectral-acceleration residuals during the 2024 Mw 4.8 Tewksbury, New Jersey, earthquake. Station N62A is located at Caumsett State Historic Park in the Atlantic Coastal Plain, whereas station PAL is located at the Lamont-Doherty Earth Observatory atop the Palisades cliffs. Detailed geophysical site characterization at both sites combined active-source and ambient-noise surface-wave testing with HVSR measurements. At Lamont-Doherty, additional ambient-noise arrays were deployed across the Palisades ridge to evaluate topographic amplification. At Caumsett, surface-wave testing resolved a thick sedimentary column overlying a major impedance contrast, with a spatially representative fundamental site frequency of 0.93 Hz. Amplification in the same frequency range was also independently evident in observed amplifications computed from site-to-site (S2S) residuals. The fundamental-resonance band also encompasses the 1-Hz frequency at which the large Tewksbury residual was observed, supporting deep sediment resonance as the dominant mechanism. At Lamont-Doherty, surface-wave testing indicated hard-rock conditions with a thin, laterally variable sediment cover. The ambient-noise arrays showed repeatable, directional amplification near 2 Hz at PAL, and the earthquake-based S2S amplifications show a peak between approximately 0.6 and 2 Hz, a pattern reproduced by neither the transfer functions nor the ergodic linear amplification models. These observations support topographic amplification as the primary mechanism at PAL.

Frequency continuation does not improve every neural representation, and better velocity recovery need not imply better prediction of unseen waveforms. We compare a velocity grid with total-variation regularization, an adapted sinusoidal coordinate network (SIREN), and a frozen image generator with a full-grid residual in two-dimensional acoustic full-waveform inversion. All methods use the same frozen SWEEP discretization, acquisition, observations, background, and budget of 1,500 shot–model gradient evaluations. Three exposed synthetic targets represent inclined layering, curved layering, and a high-contrast curved interface drawn from a fault-labelled family. With 2-to-10 Hz continuation, the generative-residual model attains velocity RMSEs of 218.7, 108.0, and 341.9 m/s, compared with 232.5, 100.3, and 412.9 m/s for Grid+TV. Thus the latter wins on curved layering, while the former improves model RMSE by 5.9% and 17.2% on the other targets. Grid+TV nevertheless predicts held-out 10 Hz shots better on all three. SIREN is worse than the common background in every cell; continuation worsens two targets but improves one. A five-weight TV sensitivity study preserves the CurveFault model-error ranking. An equal-budget variational extension fails both recovery and interval coverage. These results establish conditional rankings, not universal superiority or an isolated spectral-bias mechanism. Full-grid residuals also prevent attributing gains uniquely to the generator. We advocate joint reporting of model recovery, unseen-shot prediction, optimization cost, and negative outcomes when auditing neural FWI.

Scalar travel-time relations can identify propagation geometry without velocity inputs, ray labels or equation supervision during learning. Physical interpretation is applied after learning through the high-frequency isotropic eikonal relation (Aki and Richards, 2002). Here we show that local value differences constrain derivatives through sampling geometry and smoothness. Controlled neural fields recover propagation directions with $5.34\pm0.07^{\circ}$ median angular error, and local P- and S-wave velocities in a prospectively locked, test-sealed realization. Classical representations also recover geometry. Scalar-matched perturbations separate value accuracy from derivative reliability; regularized inversion can recover structure when direct gradients fail. A field trained on 2.90 million Chinese arrivals retains continental crust–mantle boundary structure: correlation with a withheld reference is $\rho=0.910$ raw and 0.592 after quadratic detrending (spatial-shift $p=0.020$). Direct catalogue values yield $\rho=0.909$, and thickness contrasts are compressed. Manual-only California arrivals support broad velocity organization with limited lateral fidelity. Synthetic quantum fields containing phase information support probability-flow and trajectory readouts without supervising those quantities. These results establish conditional identification across representations and physical settings: informative scalar relations can constrain geometry, while known physical relations supply its interpretation. They do not imply that accurate scalar fitting alone guarantees reliable physical derivatives.

Structural learning and physical-property inference can draw on different sources of knowledge. We learn morphology from 33,140 natural, satellite, and texture images, without geological training models, then infer Earth properties from physical observations. Identical frozen weights map 128 coordinates into each $256\times256$ compressional-velocity, shear-velocity, or density field. Fixed adapters assign physical meaning; observation-specific likelihoods determine new posteriors without retraining. Across 256 unseen Earth models, image coordinates reduce projection error fourfold relative to a dimension-matched cosine basis. Matched inversions favor image coordinates for the tested curved-velocity targets and a smooth radial basis for the tested faulted targets. A simplified 2.5-dimensional application with 915 measured observations along one 120.9-km southern-California profile constrains a velocity–density contrast. Grouped arrival-time and gravity errors fall by 43% and 44% relative to the pretrained image prior, although the matched smooth radial basis predicts better. Direct layered-Rayleigh updating reduces blind phase-velocity error by 28% at five sites, whereas P-and-gravity-only sharing worsens it, distinguishing reusable morphology from beneficial cross-property coupling. These results establish a practical separation: images supply structural alternatives, and heterogeneous physical observations determine their experiment-specific posterior probabilities.

Muon flux variations in muography time series can provide information on density-related changes in geological targets. A common approach is to define regions of interest before constructing flux time series, but this choice may dilute coherent signals or mask localized variations. We present a data-driven analysis of two muon data acquisitions at the Sos Enattos Mine in Sardinia, Italy. The method aggregates lines of sight into overlapping spatial blocks, computes standardized flux deviations, and applies principal component analysis through singular value decomposition. The leading components reveal spatially coherent structures and distinct temporal evolutions. In each run, the first mode explains more than 20% of the variance, while the first eight modes explain approximately 80%. Regions selected from the spatial modes show anticorrelated and delayed flux variations. These results show that the proposed approach can identify candidate regions of coherent muon-flux variability without imposing them a priori.

Exploration seismic processing underpins subsurface imaging and resource exploration, but learning-based methods remain difficult to compare across studies. Our survey of 368 papers finds widespread reliance on private or difficult-to-reproduce datasets, with only 25 providing public code. This obscures whether reported gains arise from model design or experimental settings. We introduce the Seismic Processing Benchmark (SPBench), covering six tasks: random noise attenuation, trace interpolation, ground-roll suppression, multiple suppression, deblending, and first-arrival picking. We reproduce 24 supervised methods on 10 datasets under 43 standardized settings and release datasets, implementations, configurations, evaluation scripts, and results. To complement global scores and per-trace pick errors, we introduce signal-component-resolved evaluation (SCoRE) for reconstruction and a reference-free ridge-curvature score (RC_norm) for first-arrival picking. Our analyses show that synthetic rankings do not reliably predict field rankings, with task-dependent agreement when models train within each setting. As degradation strengthens, rankings reorder more under coherent ground roll than under random-like interference. The ridge score agrees with MAE-based model rankings in the evaluated settings, with a mean Kendall correlation of 0.881 across three field surveys, while SCoRE reveals frequency- and energy-dependent differences hidden by global scores. SPBench provides a reproducible basis for comparing learning-based seismic processing methods and characterizes how their relative advantages vary across data settings, degradation strengths, and evaluation criteria.

We present a survey of electron distributions measured by THEMIS within Earth's equatorial magnetosphere at $L$ values $\le$ 14. Particular attention is focused on the spatial distribution and occurrence of magnetic field-aligned and perpendicular anisotropies in electron distribution functions and how these depend on solar wind dynamic pressure and magnetospheric activity. Using 161,300 3-s resolution electron measurements from THEMIS A and D, we find that field-aligned anisotropies are most prominent at low energies (30–200 eV) and occur over nearly all $L$ and MLT, with the largest values concentrated in the dayside/postnoon plasmasphere, while perpendicular anisotropies dominate at higher energies (1–30 keV) and are largest and most probable in a narrower band from $L \sim$ 5–10 with a pronounced dayside/prenoon bias. A localized region near dawn at $4\lesssim L\lesssim 8$–9 is found to be typically devoid of field-aligned electrons over the entire 30–200~eV band. Both anisotropy types shift toward lower $L$ shell and become more probable under elevated solar wind dynamic pressure and Kp, and their spatial and energy dependence is largely complementary: perpendicular anisotropies coincide spatially with regions of quasi-parallel chorus generation, while field-aligned anisotropies coincide with oblique chorus and time domain structure (TDS) generation regions. In addition to being a useful diagnostic for electron population types, our results provide a useful basis for comparison with global distributions of wave modes which arise from these anisotropies, and offer an anisotropy-based observational framework for interpreting their dependence on magnetospheric activity.

Mechanical characterization of subsurface rock formations typically requires standardized cylindrical core specimens, which are often unavailable from fractured or unconventional reservoir sequences. This study uses a Discrete Element Method (DEM) framework calibrated to Sulphur Mountain Formation siltstone to investigate how fragment geometry governs mechanical response in irregular rock particles, which are direct analogues of drill cuttings. Nineteen specimens (18 procedurally generated irregular geometries and one reference Brazilian disk), normalized to a 10 mm bounding sphere radius, were tested under quasi-static (Brazilian-type) and dynamic (Short Impact Load Cell) loading modes. Four mechanical outputs of failure force (Ff), apparent strength ({\sigma}f), apparent stiffness (Ka), and stiffness (E) were regressed against seven retained shape descriptors. Results show that force-based quantities are strongly controlled by the surface-area-to-volume (SA/V) ratio under static loading (Adj. R2 = 0.69), while area-normalized quantities ({\sigma}f and E) are geometrically insensitive. Dynamic loading amplifies geometric sensitivity for failure force and introduces an independent role for surface concavity depth. These findings establish a preliminary quantitative framework for interpreting mechanical measurements from irregular rock fragments when a standardized core is unavailable.

We present a distributed framework for large-scale Bayesian inverse problems governed by the eikonal equation, with a specific focus on seismic traveltime tomography. Traditional deterministic approaches often fail to provide the uncertainty quantification (UQ) necessary for ill-posed problems, while conventional Bayesian sampling methods such as Markov chain Monte Carlo (MCMC) suffer from the curse of dimensionality and slow convergence in high-dimensional model spaces. The proposed framework addresses these challenges through a three-tier computational strategy. First, we utilize the Fast Marching Method (FMM) to solve the eikonal equation, ensuring high numerical accuracy. Second, we reformulate the global tomographic objective into a decentralized consensus form, allowing the inversion to be decomposed into independent subproblems solved in parallel via the Alternating Direction Method of Multipliers (ADMM). This architecture eliminates the need for the explicit construction of large-scale sensitivity matrices, significantly reducing the memory footprint for 3D surveys. Finally, we integrate Stein Variational Gradient Descent (SVGD) within the ADMM workers to perform approximate posterior sampling. By evolving a set of model particles along a functional gradient direction that balances data-fitting forces with a repulsive kernel-based diversity force, we obtain an ensemble from which posterior summaries are computed. We derive a data-space Gauss-Newton update using the Woodbury matrix identity to further accelerate the particle evolution in large-scale 3D problems. Numerical experiments on complex 2D and 3D models demonstrate that the algorithm achieves stable convergence, produces high-fidelity velocity reconstructions, and provides posterior uncertainty maps.

Full-waveform inversion (FWI) requires accurate initial velocity models to avoid cycle-skipping, but constructing such models remains challenging in practice. Building on the depth-progressive diffusion framework introduced in Part~I, which relied on idealized reflectivity constraints, this work adapts the methodology to realistic exploration scenarios. We replace perfect structural information with migration-derived attributes extracted from seismic images, and introduce smooth background velocity models from tomography as additional conditioning inputs. The framework jointly leverages background/migration velocity, migrated structural information, and sparse well measurements to synthesize high-resolution velocity models through depth-progressive generation. Validation on synthetic examples demonstrates superior accuracy compared to conventional interpolation and alternative deep learning methods, with generated models successfully initializing FWI and mitigating cycle-skipping even in complex geological structures. Field data confirms practical applicability: despite training on synthetic data, the method generalizes effectively to field conditions, producing velocity models with synthetic data response that nearly match observed seismic data. As a result, this framework establishes a practical pathway to deploy generative diffusion models for velocity model building under realistic constraints.

Seismic velocity model building (VMB) is fundamental for understanding subsurface structures. Traditional methods demand high-quality starting models and, also, remain limited in resolution in coverage and computationally intensive. Recent generative diffusion model-based approaches capture statistical priors to support traditional inversion methods, but these approaches do not account for the top to bottom progression of information (layer stripping) involved in surface recorded data, where deep velocity information depends on the shallow. To address this issue, we propose a depth-progressive diffusion framework that constructs velocity models incrementally from shallow to deep by propagating prior information. Our method trains on paired shallow-deep velocity patches with variable overlap and explicit depth encoding, integrating multiple geophysical constraints including well logs and seismic images (representing structural information). During inference, we synthesize overlapping depth slices using a progressive algorithm and merge them with Gaussian-weighted blending to eliminate boundary artifacts. This approach leverages both learned geological distributions and observed shallow priors while providing uncertainty quantification. Extensive numerical experiments on in-distribution tests and an out-of-distribution test demonstrate excellent VMB accuracy with a strong correlation between predicted uncertainty and actual errors. As a proof of concept, this part I employs idealized structural constraints derived from vertical reflectivity to validate the methodological framework. The companion paper (Part II) extends the approach to realistic structural constraints relying on migrated images with field data applications.

To improve understanding of how precipitation frequency and intensity change with warming, we combine the coupled land–atmosphere water balance with a stochastic hydrological model that retains explicit dependence on precipitation frequency and event depth. We find a linear relationship between co-variations of rainfall frequency and intensity, where the slope is a hydrological sensitivity determined by the dryness and storage indices and the intercept is a forcing term associated with changes in moisture convergence, potential evapotranspiration, and effective water-storage capacity. While a given relative change in either rainfall frequency or intensity produces an identical relative change in the mean rate, rainfall intensity additionally influences the runoff coefficient by altering terrestrial water storage relative to rainfall depth. This means frequency and intensity have different impacts on the water cycle, allowing the frequency–intensity slope to depart from -1. Analysis of long-term MOPEX records shows that the forcing term is dominated by moisture convergence and that the frequency–intensity relation becomes steeper from wet to dry regimes, consistent with the slope predicted by the theory. The framework provides a physical interpretation of the observed negative covariation between precipitation frequency and intensity and clarifies the roles of hydrological partitioning and atmospheric moisture supply in their responses to warming.

Temporal rainfall distributions are widely used in design storm construction; however, many countries with limited sub-daily observations, such as Brazil, still rely on frameworks developed under different hydroclimatic conditions. This mismatch may introduce bias in hydrological design and water resources estimation. Here, we develop the first national-scale empirical Huff curves for Brazil using sub-daily rainfall observations. We compiled data from 3,164 stations and, after quality control, retained 290,164 storm events from 1,045 stations spanning 2010 to 2025. Empirical cumulative rainfall mass curves were constructed and fitted using seventh-degree polynomials at station, biome, state, and municipality scales. Our findings show a dominance of first-quartile (Q1; front-loaded) storm patterns, occurring at 94.4% of stations nationally, increasing to 99.2% in the Amazon and Cerrado biomes and decreasing to 88.7% in the Atlantic Forest. The national Q1 median curve closely matches the Huff (1967) reference (MAE = 0.045; Dmax = 0.097), with narrow bootstrap uncertainty. Q1 dominance is robust to inter-event time definition, with 84.7% of stations showing consistent classification across 2 to 12 h thresholds. A Soil Conservation Service Curve Number experiment across 579 headwater catchments shows that Brazilian curves increase design peak discharge by a median of 8% and up to 11% in the Cerrado relative to the Illinois reference, indicating potential underestimation when using non-local distributions. Biome-, state-, and municipality-scale parameters are provided as open data and through an interactive platform, offering locally calibrated design-storm alternatives for Brazil.

Seismic feature extraction and acoustic impedance inversion are important for subsurface characterisation, but sparse well-log data limit the generalisation of data-driven inversion models across different areas. Self-supervised masked pretraining offers a way to exploit large volumes of unlabelled seismic data; however, conventional approaches often overlook the directional characteristics of 3D seismic data. We propose a direction-aware masked autoencoder (DA-MAE) that distinguishes lateral reflector structure from vertical waveform characteristics in 3D post-stack data. The framework incorporates this distinction into token representation, masking geometry, and reconstruction constraints on reflector continuity and waveform fidelity. We evaluate DA-MAE through masked reconstruction on an independent field survey and cross-area acoustic impedance inversion using two additional field areas. Reconstruction experiments show that performance is more sensitive to lateral token resolution than to moderate changes in vertical patch length, and that increasing encoder capacity does not fully compensate for coarse tokenization. Moreover, the preferred token scales and masking strategies vary between reconstruction and inversion, suggesting that reconstruction fidelity alone is not a reliable indicator of transferability. In cross-area inversion, the pretrained representations remain effective in the target area and improve impedance prediction, demonstrating their transferability across different seismic surveys. These results provide practical guidance for seismic-specific masked pretraining and its application to acoustic impedance inversion.

Accurate prediction of ionospheric Total Electron Content (TEC) during geomagnetically disturbed conditions remains challenging, particularly when empirical models perform poorly during storms and neural network (NN) approaches rely explicitly on geomagnetic indices such as Kp/Ap or Dst/SYM-H. In this study, we develop an NN-based model to predict TEC variations over the Indian longitude sector without incorporating geomagnetic indices as inputs. The model is trained on the full-year (2024) Global Ionospheric Map (GIM) TEC dataset at a 15-minute cadence, together with solar wind parameters: Interplanetary Magnetic Field (IMF) $B_z$, velocity ($V_{sw}$), and density ($N_p$), along with the F10.7 cm solar radio flux. Seasonal and diurnal variability are represented using sinusoidal transformations of Day of Year (DOY) and hour of day (HOD). An optimized architecture with 96 hidden neurons yields strong training performance ($R^2$ $\sim$ 0.96, RMSE $\sim$ 5.4, and MAE $\sim$ 3.8 TECU). Independent predictions for the year 2025 demonstrate robust generalization under both quiet and geomagnetically active conditions. Quiet-time residuals are generally confined within $\pm$10 TECU, while during moderate and strong storm-time conditions, the model maintains comparable error bounds across most of the Indian region, despite not being trained on geomagnetic indices. The results indicate that solar wind parameters, combined with the F10.7 solar flux and cyclical temporal encoding, contain sufficient information to reproduce geomagnetic storm-time TEC variability. This study therefore demonstrates the applicability of the proposed approach for regional TEC prediction and ionospheric nowcasting, with potential applications in GNSS positioning, satellite navigation, radio communication systems, and the future development of operational ionospheric forecasting tools.

This study investigates storm-to-storm variability and hemispheric differences in magnetosphere-ionosphere (MI) coupling during the extreme (G5) geomagnetic storm of May 10-11 and the severe (G4) storm of October 10-11, 2024. Global field-aligned current (FAC) patterns derived from the Active Magnetosphere and Planetary Dynamics Response Experiment (AMPERE), together with conjugate observations from ground-based magnetometers within the SuperMAG network and Global Positioning System (GPS)-derived total electron content (TEC), are analyzed to examine high-latitude electrodynamic and ionospheric responses in both hemispheres. The May event exhibits broad and relatively organized Region 1/Region 2 FAC systems encircling the polar caps across multiple local time sectors, accompanied by intervals of correspondence in conjugate magnetic perturbations and structured TEC enhancements with temporal offsets between hemispheres. In contrast, the October event shows more localized, asymmetric, and uneven FAC morphology with pronounced hemispheric and dawn-dusk asymmetries, together with greater divergence in conjugate magnetic responses and spatially heterogeneous TEC variability. These differences are consistent with enhanced mesoscale variability and asymmetric current closure under storm-time conditions. Overall, the results highlight that even under similarly strong solar wind driving, the coupled MI system can exhibit substantially different spatial organization and interhemispheric coupling, reflecting the combined influence of FAC morphology, ionospheric conductance, and local electrodynamic conditions.

Assessing the impacts of future climate scenarios including tipping events of major Earth system components such as the Amazon rainforest (ARF) or the Atlantic meridional overturning circulation (AMOC), requires accurate and high-resolution simulations. Here, we present ClimTip-GML, the first globally bias-corrected and downscaled climate dataset for impact assessment of large-scale tipping scenarios, comprising eight key variables at 0.25{\deg} spatial resolution from three general circulation models (GCMs): CESM1-CAM5, HadGEM3-GC31-MM, and MPI-ESM1-2-HR. The dataset includes 100-year-long climate simulations with preindustrial and historical conditions, as well as scenarios at a +2{\deg}C warming level with and without tipping transitions of the AMOC or ARF. We apply generative machine learning (GML) techniques trained on reanalysis data to bias-correct and downscale the GCMs in a manner that is physically consistent across space, time, and all eight variables. Comprehensive validation shows substantially reduced biases, improved small-scale spatial variability, multivariate correlations, and consistent long-term climate responses to the external forcing and tipping events. The results hence permit substantially improved impact assessments of tipping transitions of the ARF and AMOC, directly informing mitigation and adaptation policies.

Super-Earth exoplanets are among the most abundant planets known, yet their bulk densities leave the interior state degenerate. The static structure models used to interpret them, and the interior retrievals built on them, typically describe the cold, solidified end state of an evolution that begins hot and molten. During this magma ocean stage the interior, the outgassed atmosphere, and the surface co-evolve and set the long-term climate and geophysics of super-Earths. We develop and validate a fully coupled model for the structural and thermal evolution of super-Earth exoplanets within the PROTEUS framework, including new and upgraded models of the interior structure, mantle energetics, and volatile outgassing. In volatile-poor super-Earths of 1 to 10 Earth masses, the silicate interior contracts by about 10 % of its molten radius through cooling and crystallisation, nearly independent of planet mass and driven by the thinning silicate shell alone. The solidified radius is set by planetary mass and core fraction, insensitive to the host star, irradiation, and initial thermal state. In contrast, volatile-rich super-Earths at and above about 5 Earth masses may not solidify: their thick outgassed atmospheres throttle the surface heat loss until the interior settles into a deep magma ocean, keeping the planet inflated and limiting the contraction to about half its volatile-poor value. Mantle contraction alone thus shapes the low-mass exoplanet transit population, motivating joint interpretation of atmospheric and geophysical signatures in upcoming exoplanet surveys.

Few fragments of Earth's earliest crust have survived, and so this formative period of our planet's history is shrouded in mystery. To leverage the limited data available, it is important to understand all the processes that could have shaped our young planet. Here, I explore a previously neglected aspect of early-Earth dynamics: rotation. Earth after the Moon-forming impact was rapidly rotating with its rotation rate slowing significantly during the first tens of millions of years after the impact as the Moon tidally receded. Using planetary structure and tidal evolution calculations, I show that the initially distorted structure of Earth would likely have led to significant latitudinal variations in primary crustal thickness. Further, the subsequent changes in Earth's shape in response to its slowing rotation rate could have driven extensive tectonic activity and modulated the response of the crust to other forces. There was extension of the crust in polar regions and convergence in the equatorial regions at rates potentially comparable to those forming the Himalayas today in high-angular momentum scenarios. Depending on the rheology of the earliest crust, such motions could have led to burial of hydrated and weathered surface material, extensive volcanism, and degassing of Earth's interior. Phase changes due to increases in internal pressure could have led to delamination of parts of the crust, producing early mantle heterogeneities. Hydrated secondary melting of the crust and the production of evolved magmas could explain the emergence of a diversity of rock types within tens of millions of years of Earth's formation.

The determination of physical height differences using highly stable atomic clocks has emerged as a novel approach in relativistic geodesy, exploiting the gravitational redshift as a direct observable of geopotential differences. In this study, we investigate the feasibility of satellite-based clock comparisons using the Atomic Clock Ensemble in Space (ACES) onboard the International Space Station, which enables time transfer via microwave (MWL) and optical (ELT) links. Since operational optical data are not yet available, a comprehensive full-scale simulation of realistic ACES observation scenarios is performed, including detailed noise models of clocks and links. A slope-based estimation method is applied to time series of clock comparisons in order to extract the relativistic redshift signal and derive height differences between the ground stations. The performance of the approach is evaluated for quasi-common view, non-common view, and split non-common view configurations, where the latter divides the observation period into shorter intervals. The results show that optical links enable faster convergence and can achieve height accuracies at the decimeter level within a few days and at the centimeter level over longer periods, while microwave links are more strongly affected by noise and bias contributions. Non-common view processing significantly increases observation availability with only minor loss in accuracy, and the split approach provides robust solutions for larger networks. These findings demonstrate the strong potential of satellite-based clock comparisons as a remote-sensing technique for determining physical height differences on a continental scale.

While underground reservoirs emerge as essential elements to face global warming, these systems represent complex multi-physical and multiscale problems. The considered injection of fluids during hydrogen storage, carbon dioxide sequestration, or geothermal energy recovery involves a modification of the chemical equilibrium of the fluid in the porous reservoir. Chemical reactions can induce microstructural changes of the rock matrix, leading to a reduction of elastic properties of the material, and to potential settlement or stress redistribution. Consequently, it becomes pivotal to establish predictive behavior laws to describe the effect of chemical damage on elastic properties. Facing the difficulties to estimate experimentally the impact of chemical damage on mechanical properties, a Digital Rock Physics approach is proposed in this contribution. This numerical homogenization scheme is used to compare two distinct types of microstructure models: the first one consists in a Discrete Element Model, while the second one employs a continuous description. This continuous formulation is based on a Phase-Field description to predict the evolution of the microstructure subjected to chemical alterations and on the Fast Fourier Transform to estimate the macroscopic properties of the material. Finally, these frameworks establish different softening laws that can be used as constitutive ingredients for a cemented material during its weathering.