Peer Revenue Publish or profit

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Physics Physics Geophysics

physics.geo-ph Watch Geophysics

Thursday 17 September

Geophysics

Multi-Track Time-Series Burst-Overlap Interferometry for Resolving Horizontal Deformation in Earthquake-Cycle Studies

Xing Li, Zhuang Gao, Han Chen et al. (+3 more)

Interferometric Synthetic Aperture Radar (InSAR) is intrinsically insensitive to the north-south component of crustal motion because of its near-polar geometry, which makes accurate quantification of this deformation a persistent limitation of InSAR. We introduce a Multi-track Time-Series Burst-Overlap Interferometry (MTSB) framework that delivers absolute, ITRF-referenced deformation fields across representative interseismic, coseismic, and postseismic applications by combining optimized phase linking, a unified time-series framework for separating deformation and residual misregistration, and block-wise spectral analysis. Case studies spanning co-, inter-, and postseismic phases demonstrate that MTSB accurately resolves along-track horizontal deformation, with primary sensitivity to north-south motion, while substantially reducing orbit-related artifacts. Independent comparisons with GNSS indicate centimeter-level agreement for postseismic and coseismic displacement estimates and millimeter-per-year agreement for interseismic velocities. The resulting deformation fields improve constraints on regional plate kinematics and fault kinematics, facilitate more reliable Euler-pole estimation, and provide by-product orbital corrections for conventional InSAR processing.
Read the full abstract

All authors Xing Li, Zhuang Gao, Han Chen, Zhangfeng Ma, Yangkang Chen, Alexandros Savvaidis

Geophysics · Cross-listed from astro-ph.EP

Thermal Evolution of Lava Planets Across System Ages: Predictions for Hell of a Survey

Mariana Sastre, Tim Lichtenberg, Lisa Dang et al. (+22 more)

Ultra-short-period (USP) rocky exoplanets can have dayside temperatures high enough to maintain permanent magma oceans, sitting at the intersection of interior geophysics and atmospheric chemistry. Coupled feedbacks between the molten surface and outgassed atmosphere can sustain or enhance a volatile envelope, while stellar interactions can erode it. Understanding which outcome prevails, and its observable imprint, requires a multi-target approach across planets at different stages of thermal evolution. We present predictions for the five targets of JWST Cycle 4 program 8864: TOI-1807 b, TOI-2260 b, TOI-431 b, TOI-6255 b, and TOI-2431 b. Using the PROTEUS coupled interior-atmosphere framework, we construct a simulation grid and classify outcomes into six categories, defined by the final interior melt state and by whether the planet retains a detectable atmosphere thick enough to redistribute heat to the nightside. For targets retaining a non-negligible volatile envelope, our models predict higher partial pressures for most species when the surface is molten, except for S$_{2}$, whose enhancement in the solid regime suggests it may serve as a tracer of interior melt state. Despite some targets showing outcomes across multiple scenarios, most tend toward a bare-rock end-member, with global melt fraction $\leq$ 20% and atmospheric retention sensitive to escape efficiency. Our analysis reveals a minimum escape efficiency threshold below which volatile envelopes survive under energy-limited escape, constraining the conditions required for atmosphere survival on irradiated rocky planets. These predictions will guide interpretation of MIRI-LRS phase curve observations and identify which targets and features best discriminate between competing geophysical states.
Read the full abstract

All authors Mariana Sastre, Tim Lichtenberg, Lisa Dang, Anjali Piette, Haiyang S. Wang, Mercedes López-Morales, Thomas Wilson, Charles-Édouard Boukaré, Mahesh Herath, Nicolas Cowan, Md Abdullah Al Zaman, Madyson G. Barber, Casey Brinkman-Traverse, Nicholas Connors, Ian Crossfield, Lina D'Aoust, Oliver Herbort, Leoni Janssen, Mathilde Kervazo, Owen Lammert, Yamila Miguel, Raymond Pierrehumbert, Allona Vazan, Joost P. Wardenier, Sebastian Zieba

Geophysics

The Brittle-Plastic Transition in Quartz-Albite Mixtures: New Insights From Shear Deformation Experiments at Mid-to-Lower Crustal Depth Conditions

M. Furukawa, B.A. Verberne, S. Sawa et al. (+4 more)

Crustal strength is often characterized using a strength-depth profile where laboratory-derived friction and flow laws are connected at depth (i.e., the so-called Christmas Tree diagram). Large, destructive earthquakes frequently nucleate within the transition zone from a frictional-to-viscous deformation regime, which represents the strongest part of the crust. However, microscale deformation mechanisms controlling bulk frictional-to-viscous transitional behavior remain unclear. To investigate the deformation mechanisms, we conducted shear experiments on room-dry, powdered quartz-albite mixtures under upper- to mid-crustal pressure-temperature conditions using a Griggs-type deformation apparatus. We simulated depth conditions in the range 7-30 km, by varying temperatures and confining pressures (210-900 C and 185-870 MPa, respectively, by assuming 30 C/km and 2,700 kg/m3). To assess the rate dependence and stability of shear deformation, we sequentially stepped shear strain rates between 10^-3/s and 10^-4/s. At shallower depth conditions, friction coefficients follow Byerlee's law, while at greater depth conditions they deviate from it and strain weakening is observed. Post-mortem microstructures indicate changing deformation mechanisms with increasing simulated depths. The samples deformed at shallower depth conditions (<18 km) show a predominance of cataclastic grain comminution. At greater depth conditions (>24 km), nano-grains are observed, as well as polygonal quartz grains at the greatest depth condition (30 km). These results indicate that the controlling deformation mechanisms at the frictional-viscous transition zone are grain boundary sliding and dynamic recrystallization. We conclude that nano-scale deformation mechanisms govern the frictional-viscous transitional deformation in the upper crust, and propose their importance for understanding seismic rupture processes there.
Read the full abstract

All authors M. Furukawa, B.A. Verberne, S. Sawa, H. Nagahama, M. Takahashi, O. Pluemper, J. Muto

Geophysics

Continental-scale probabilistic resistivity imaging of Australia using deep learning: Implications for geology, groundwater, and critical minerals

Sihong Wu, Jiajia Sun and Jiefu Chen

Electrical resistivity is a fundamental physical property that provides powerful constraints on subsurface structure, lithology, fluid distribution, and mineralization. We present a continental-scale electrical resistivity model of Australia's shallow crust with quantified uncertainty, extending to depths of up to ~650 m. The model is derived from probabilistic inversion of more than 26.6 million airborne electromagnetic (AEM) soundings acquired along 352,600 line-kilometers from the AusAEM program. We develop a deep learning-based probabilistic inversion framework using invertible neural networks (INNs). To accommodate differences among AEM surveys, we trained 11 networks using a common set of synthetic resistivity models and the same training strategy, enabling consistent integration of inversion results across Australia. The entire inversion was completed in only 4.37 GPU hours, overcoming long-standing computational barriers to continental-scale probabilistic geophysical imaging. The resulting resistivity model exhibits clear spatial patterns that closely align with Australia's major geological provinces, sedimentary basins, aquifer and mineralizing systems. We further define the depth of investigation using posterior resistivity distributions to support uncertainty-informed geological interpretation. The resistivity model together with quantified uncertainty provides new physical constraints on regolith thickness, basin architecture, paleochannel and mineralized systems across Australia, serving as a foundation for interpreting continental-scale electrical structure and its geological, hydrological, and mineral system implications.
Read the full abstract

Wednesday 16 September

Geophysics

Vertical Attenuation of Thermoremanent Magnetic Anomalies: Implications for Drone-Borne Archaeological and Shallow Geophysical Surveys

Alexandru Hegyi, Kristoffer Aalstad, Arne Anderson Stamnes et al. (+6 more)

Magnetic surveys are widely used in archaeology to reveal buried features by detecting variations in the Earth's magnetic field caused by past human activities. However, as sensor height increases, magnetic anomalies produced by subsurface sources weaken and broaden, posing challenges for both ground-based and aerial (UAV) surveys. This study quantifies that altitude-dependent loss of signal and spatial definition using a fully synthetic model of a uniformly magnetized circular kiln together with upward continuation of measured fluxgate data from the Storbekken 1 iron-production site in central Norway. The synthetic experiment shows that a 2 m diameter, 1 m thick magnetized cylinder buried 0.3 m below ground retains only about 11% of its near-surface (0.2 m) peak amplitude at 2 m sensor height and about 3% at 4 m. In the hybrid experiment, composite furnace anomalies of several hundred nanoteslas collapse to tens of nanoteslas by 2 to 3 m, while distinct lobes merge into broad, low-contrast features. The results show that UAV-borne magnetometry can remain effective for site detection and delineation at elevations where detailed intra-site morphology is already strongly degraded. For small or subtle archaeological targets, sub-metre to low-metre sensor heights provide substantially greater interpretative detail. Recent field evidence on UAV-system noise further indicates that practical detectability will depend not only on source-distance attenuation but also on platform configuration and acquisition strategy. The resulting attenuation curves and morphological observations provide transferable guidance for planning archaeological and shallow-geophysical UAV magnetic surveys.
Read the full abstract

All authors Alexandru Hegyi, Kristoffer Aalstad, Arne Anderson Stamnes, Luc Girod, Sebastian Westermann, Diaa Sheishah, Enas Abdelsamei, Michał Pisz, Norbert Pirk

Geophysics

Period-Inverted Blast-Radius Normalization for a Non-Ablating Hypersonic Reentry Source Using OSIRIS-REx Capsule Infrasound

Elizabeth A. Silber

Cylindrical blast wave theory relates infrasound from hypersonic sources to energy deposition through a characteristic blast radius R_0, yet the normalization constant k in the scaling R_0 = k (E_0/p_0)^(1/2) differs by up to 40% across the formulations examined here. This uncertainty affects period-based source characterization for elevated atmospheric sources relevant to infrasound monitoring. Using infrasound from the OSIRIS-REx Sample Return Capsule reentry on 24 September 2023, observed periods at 39 stations spanning source altitudes of 44-62 km are inverted through a weak-shock propagation model to obtain period-inverted blast radii R_0,obs without prescribing a blast-wave normalization. Combining R_0,obs with aerodynamic drag energy yields a median k = 0.396, with a modified-bootstrap 95% interval of 0.378-0.474 when the reported period-measurement uncertainties are included. This result is consistent with the Sakurai (1965) and Jones et al. (1968)/Plooster (1970) normalizations, while Few (1969) lies outside the reference confidence interval. However, station-level k values exhibit significant along-trajectory structure (Spearman r_s = -0.84, p < 0.001), indicating that k_med is an ensemble-effective normalization rather than a station-invariant constant. The Mach-diameter approximation R_0 ~ M d_m overestimates R_0 by a factor of ~3.4 for this effectively non-ablating source. A period-based relation, R_0 = 23.99 tau - 0.73 (R^2 = 0.991, MAPE = 2.3%), provides a rapid blast-radius estimator whose residuals show no detectable altitude dependence. These results provide inverse calibration constraints and practical period-based source-characterization relations for OSIRIS-REx-like non-ablating hypersonic entries, while highlighting the need for multi-event validation before broader application.
Read the full abstract

Geophysics

A New Way for Determining Earthquake Magnitude Based on Electromagnetic Radiation

Manana Kachakhidze, Nino Kachakhidze-Murphy, Badri Khvitia et al. (+2 more)

The article proposes the possibility of determining the magnitude of a future earthquake with high accuracy, based on the frequency data of VLF/LF electromagnetic emissions, as a diagnostic precursor that existed before an earthquake. Such magnitude will contribute to significantly improving the research of various fundamental issues in seismology in the future.
Read the full abstract

All authors Manana Kachakhidze, Nino Kachakhidze-Murphy, Badri Khvitia, Giorgi Ramishvili, Vaso Kukhianidze

Geophysics

A general lightweight global modeling framework for three-dimensional seismic exploration

Changxin Wei, Jun Ma and Xintong Dong

In seismic exploration, the propagation of seismic waves naturally gives rise to long-range dependencies in seismic data. Capturing such global correlations can significantly improve the accuracies of seismic signal processing, inversion, and interpretation. Global modeling (GM) methods have therefore emerged as an effective paradigm for seismic exploration, offering a powerful means of exploiting the intrinsic global relationships within seismic data. However, the mainstream GM approaches, particularly Transformers, incur prohibitively high computational costs for their inherent quadratic complexity, which restricts the scalability of such methods to higher-dimensional data. To achieve efficient GM for three-dimensional (3D) seismic exploration, we propose a general framework, named lightweight Mamba-based global modeling (LMGM), to establish long-range dependencies at a relatively low computational cost. Specifically, LMGM fully leverages the linear complexity of Mamba. A three-directional scanning Mamba block is designed to exploit spatial correlations along the three dimensions of 3D seismic data. Meanwhile, a dual-domain-aware block is further developed to integrate time- and frequency-domain features for enhanced feature representation. In addition, a two-stage plug-and-play nonlinear normalization strategy is proposed to enhance the adaptability of LMGM to diverse seismic records while preserving signal characteristics. We employ 3D seismic data interpolation as a representative task to comprehensively evaluate LMGM. The results demonstrate that LMGM achieves superior processing performance while substantially reducing computational cost compared with U-Net-based and Transformer-based methods. Furthermore, experiments on random noise removal demonstrate the applicability of LMGM beyond interpolation, supporting its potential as a general GM framework.
Read the full abstract

Geophysics · Cross-listed from physics.comp-ph

Van der Waals interactions in supercritical water under Earth's mantle conditions

Jiajia Huang, Rui Hou and Ding Pan

The properties of water under high pressure and high temperature (HP-HT) are critical in multiple geochemical processes in deep Earth. Ab initio molecular dynamics (AIMD) is a promising approach to study water under extreme conditions without any empirical parameters. However, the accuracy of AIMD simulations is determined by the exchange-correlation (XC) functional including the dispersion correction used in density functional theory calculations. While van der Waals (vdW) interactions are well known to be critically important for water under ambient conditions, the influence of the dispersion correction on the properties of HP-HT water as found in Earth's mantle remains largely unexplored. To address this, we carried out AIMD simulations for supercritical water at 1, 5, and 10 GPa, and 1000 K. We compared PBE, PBE-D3, RPBE-D3, and SCAN functionals, where D3 means Grimme's D3 dispersion correction. We compared the structural, diffusion, and vibrational properties of water as computed with these XC functionals. Overall, the discrepancies between the functionals are reduced under extreme P-T conditions relative to ambient conditions. PBE and PBE-D3 exhibit higher proton-transfer rates than RPBE-D3 and SCAN, suggesting that while vdW interactions do not significantly affect the water structure under extreme conditions, the oxygen-hydrogen bond strength does influence proton transfer. Our results provide molecular-level insight into water in Earth's mantle and offer valuable guidance for selecting appropriate XC functionals in AIMD simulations of aqueous solutions under extreme conditions.
Read the full abstract

Geophysics

Gravitational energy released when Earth quakes: Heat supply for island-arc volcanism

Benjamin F. Chao, Sheng-An Shih, Frederic Deschamps et al. (+1 more)

We address a long-standing paradox in plate tectonics concerning the thermodynamics of its very origin: What supplies the thermal energy for the intense island-arc volcanism at the subduction zones, the supposedly cool end of the mantle convection? We surmise a first-principle physics model tracking the gravitational energy (Eg)-heat conversion within the mantle convection. The scenario points to a great amount of Eg release of ~10 TW induced by the thrust-faulting earthquakes that occur predominantly in the upper mantle at the subduction zones. Generally ~1000 times greater than the seismic energy release or nearly a quarter of the total terrestrial heat flow, this Eg release, upon conversion into heat in the irreversible convection process, supplies amply for the island-arc volcanism. Meanwhile similar but opposite and less vigorous Eg gains at ~2 TW occur at the diverging plate boundaries via the normal-faulting earthquakes. This model resolves the said paradox, and more importantly provides a new paradigm pointing to an active role of seismicity in the plate tectonic energetics and evolution of the convecting mantle.
Read the full abstract

All authors Benjamin F. Chao, Sheng-An Shih, Frederic Deschamps, Eh Tan

Tuesday 15 September

Geophysics · Cross-listed from math.NA

Structure-Driven Inversion: A New Paradigm for Solving Inverse Problems

Shengchang Chen

Inverse problems are predominantly solved within the optimization-driven paradigm, which formulates the problem as objective-function minimization and approaches the solution by iterative search. Though universal, it suffers from limitations in efficiency, interpretability, and multi-parameter decoupling. This paper proposes a new paradigm—Structure-Driven Inversion (SDI). Instead of iterative search, SDI identifies and exploits the intrinsic structure of the problem to construct a solution method. It has two types of structure—mathematical structure and physical structure—and two corresponding driving inversion types: Mathematical Structure-Driven Inversion (MSDI) and Physical Structure-Driven Inversion (PSDI). The current representative method of MSDI is Mathematical Structure-Driven Pseudo-Inverse Inversion (MSDPII), which constructs a pseudo-inverse in the spectral domain via unitary diagonalization. The current representative methods of PSDI are Physical Structure-Driven Waveform Inversion Imaging (PSDWII), which performs inversion through virtual-source projection, and Physical Structure-Driven Back-Propagation (PSDBP), which implements three structural projections for deep neural networks via physical-system analogy. SDI is not a rejection but a complement and extension of Optimization-Driven Inversion (ODI). To the best of the author's knowledge, no existing study has systematically presented structure-driven inversion as an independent paradigm; this paper aims to fill that gap.
Read the full abstract

Monday 14 September

Geophysics

Adaptive and accuracy-aware multiple data assimilation in a three step framework

Kyle Ivey, Matthias Morzfeld, Chaoyi Wang et al. (+4 more)

The ensemble smoother with multiple data assimilation (ES-MDA) is an algorithmic framework for the ensemble-based solution of inverse problems in reservoir engineering (and beyond). ES-MDA gradually transitions a prior ensemble to a posterior ensemble. The details of how this transition, or "multiple data assimilation," is implemented defines the accuracy and computational cost of ES-MDA. We show that many popular, adaptive variants of ES-MDA can be understood within a simple three-step framework: inflation proposal, pre-analysis revision, and post-analysis revision. The three steps interact to resolve a trade-off between accuracy (many assimilations with small updates) and efficiency (few assimilations with large updates), inherent to ES-MDA. We then present a new adaptive and accuracy-aware method, ES-MDA-A2, that combines large updates with a "catch-up" mechanism that decreases the update size allowing for additional assimilations if the accuracy is low. ES-MDA-A2 requires only two inputs: a targeted accuracy and a maximum number of data assimilations. We test existing and new ES-MDA variants in systematic numerical experiments with a toy model, two electromagnetic inversions with field data, and a subsurface flow reservoir simulation. We find that ES-MDA-A2 resolves the accuracy-efficiency trade-off differently from existing methods, leading to accurate inversions at a reasonable computational cost in all experiments.
Read the full abstract

All authors Kyle Ivey, Matthias Morzfeld, Chaoyi Wang, Christina Morency, Christopher S. Sherman, Robert Mellors, Joshua A. White

Geophysics · Cross-listed from physics.ao-ph

A Self-Diagnosing Structural Error-Aware Parameter Estimation Method for Earth System Models

Qingyuan Yang, Addisu G Semie, Brian Medeiros et al. (+3 more)

We propose a fully automated, structural error-aware, interpretable climate model parameter estimation method that leverages Perturbed Parameter Ensembles (PPEs). It is based on history matching and aligns with an increasingly-used iterative simulation-emulation-calibration methodology. The method is motivated by the negative impacts of structural error and emulator and observational uncertainties on climate model parameter estimation efforts, as well as the problems associated with sparsely-sampled PPEs. To address these challenges, the method explicitly builds simpler emulators that avoid overfitting, detect structural error, avoids compensating for structural error through inflated mismatch tolerances, and sequentially excludes structurally inconsistent variables for parameter estimation. The method decomposes the high-dimensional calibration problem into linked low-dimensional subproblems, and integrates their constraints to reconstruct the jointly plausible region of the full parameter space. The method is applied to a 100-member PPE with 34 perturbed parameters generated by a version of CAM6 with machine learning-based warm rain microphysics parameterization. Through iterative application, the method greatly reduces the ensemble spread and improves the matching between simulated and observed zonal climatologies. The method also finds ensemble members that outperform the default CAM6 configuration in root mean square error across multiple diagnostics. Controlled experiments demonstrate that overly-conservative emulator uncertainty could lead to neglect of informative observations, and tolerance of the structural error, in the context of this method, biases the estimated parameters toward compensating for structural error. Our work also emphasizes the value of interpretability for diagnosing structural error and informing parameter estimation in PPE-based calibration.
Read the full abstract

All authors Qingyuan Yang, Addisu G Semie, Brian Medeiros, Gregory S Elsaesser, Da Fan, Wayne Chuang

Geophysics · Cross-listed from eess.IV

A Sentinel-2 benchmark dataset for deep-learning active-fire segmentation across 25 California wildfires

Shreyan Mitra, Mohammadreza Narimani and Parastoo Farajpoor

This article describes an open image dataset for developing and evaluating active-fire segmentation methods in satellite imagery. The dataset contains 2,148 image-mask pairs from 25 California wildfires, with acquisitions spanning July 2020 to August 2026. Each image is a 512x512-pixel, three-channel composite derived from Sentinel-2 Level-2A bands B12, B11 and B8A at 20 m spatial sampling. A fixed linear rendering is applied throughout the dataset. Corresponding masks distinguish background, SWIR-rule active fire and invalid observations. The masks were generated from shortwave-infrared brightness and near-infrared contrast, followed by constrained neighborhood growth. The release includes chip-level metadata and an incident-disjoint partition containing 18 training, three validation and four test fires. Among the image pairs, 841 contain active-fire labels; these labels occupy 0.0766% of all grid cells. A mask-blind analyst review covers 233 test chips and provides a separate assessment of the rule-generated labels at chip and connected-component levels. Reference training and evaluation code accompanies the data, including a ResNet-34 U-Net implementation with validation-based checkpoint and threshold selection. The archived images, masks, metadata and review annotations support research on rare-class segmentation, learning from algorithmic labels and transfer across fire incidents. The versioned dataset is deposited on Zenodo, with preparation and reuse software maintained in a public GitHub repository.
Read the full abstract

Geophysics · Cross-listed from eess.IV

Multisource Remote Sensing and Geospatial Analysis of Vineyard Wildfire Impacts and Resilience: The 2019 Kincade Fire

Parastoo Farajpoor, Mahla Ardebili Pour, Mohammad Bagher Ghiasi et al. (+1 more)

Working agricultural landscapes are often treated as background to wildfire disasters, even though they are managed fuel mosaics, productive assets, and parts of regional infrastructure systems. We examine vineyard wildfire resilience during the electrically initiated 2019 Kincade Fire in Sonoma County, California, using an open, event-anchored geospatial framework spanning 4,581 vineyard fields (8,813.2 ha), wildland vegetation, surveyed structures, roads, overhead smoke, and post-fire greenness. Sentinel-2, OpenET, gridMET, soils, terrain, NOAA smoke polygons, an ignition-date OpenStreetMap network, and three-dimensional data inventories were analyzed at native decision scales. Vineyard pixels showed substantially lower descriptive dNBR than wildland pixels inside the perimeter (means 0.130 and 0.337). This contrast did not identify a universal vineyard firebreak effect: a segment-clustered boundary model gave a small negative contrast at 100 m (tau = -0.0166) but changed across bandwidths, failed slope continuity, disappeared in a 100 m donut, and produced a wrong-signed placebo. A 250 m spatial GAM reversed the unconditional pattern: after conditioning on location, terrain, and water use, vineyard fraction was positively associated with dNBR, while residual Moran's I remained 0.519. Beyond spectral impact, all mapped vineyards intersected overhead smoke on at least one day (mean 7.78 potential smoke-days per field), 34.2% of road-network nodes were dead ends, and inside-perimeter vineyards showed a larger greenness deficit through 2021 (recovery ratios 0.815 inside and 0.854 outside). Lower immediate spectral impact therefore did not imply complete resilience. The study provides a reproducible urban-rural informatics template separating descriptive contrasts, conditional associations, exposure indicators, and recovery evidence for decisions in working landscapes.
Read the full abstract

All authors Parastoo Farajpoor, Mahla Ardebili Pour, Mohammad Bagher Ghiasi, Mohammadreza Narimani

Geophysics

Three dimensional non-singular mollified elastic dislocation theory for extended width fault zones and inhomogeneous boundary element models

Brendan Meade

Classical elastic dislocation theory (CEDT) has two challenges when applied to faulting problems: 1) fictitious on-fault stresses not defined by ordinary integration and 2) the geometric unreality of infinitely thin fault zones. We show that both can be resolved by a mollified elastic dislocation theory (MEDT) built on Cortez blob (Cortez, 2001) mollified displacement discontinuity Green's functions, which represent deformation across spatially distributed fault zones of finite scale epsilon and produce singularity-free displacements and stresses everywhere. Analytical integration of the mollified source solution over arbitrary planar triangular elements is done with AI, and the resulting closed-form solutions allow for the calculation of non-singular stresses across geometrically complex fault systems. Further, we demonstrate the decoupling of the fault-zone width scale epsilon from the mesh length scale h, the elasticity analog of a result established for regularized viscous flow Stokeslets (Ferranti and Cortez, 2024). We use these mollified kernels to demonstrate numerically stable collocation boundary element models including spatially extended fault zones, material property variations and non-planar topography.
Read the full abstract

Sunday 13 September

Geophysics · Cross-listed from cond-mat.stat-mech

A New Framework for Interfacial Statistics: Exact n-Point Correlations of Gaussian Level Sets

Aleksei M. Cherkasov, Kirill M. Gerke and Aleksey Khlyupin

We derive exact analytical expressions for higher-order correlations of Gaussian level-set interfaces, establishing a direct link between bulk field statistics and interface geometry. This framework enables efficient reconstruction of disordered media, detailed modeling of {Gaussian foams}—structured, double-thresholded interfaces with tunable morphology—and analysis of {memory-enhanced anisotropy} reflecting directional persistence in surface structure. These results open new possibilities for characterizing complex transport, guiding stochastic reconstructions, designing materials with desired properties, quantifying the information content of correlation functions and modelling directional processes on irregular boundaries.
Read the full abstract

Geophysics · Cross-listed from cs.LG

Should All Noises Be Treated Equally: Impact of Input Noise Variability on Neural Network Robustness

Salma Alsinan, Maksim Makarenko, Sixiu Liu et al. (+2 more)

Geophysical data collected from active field sites are often contaminated by complex and heterogeneous noise, obscuring weak seismic events, and complicating automated interpretation. Although deep learning offers promising solutions for seismic processing, its performance is highly sensitive to the nature of training noise, especially under out-of-distribution (OOD) conditions. This study investigates the influence of noise parameters, such as type, scale, and complexity on the performance, generalization and robustness of neural networks in two geophysical tasks: first break picking and denoising. We simulate seismic-while-drilling data and apply controlled input source noise augmentation using stochastic generators to vary the noise characteristics. Different neural networks are trained on fixed noise types and scales, then evaluated across both seen and unseen noise scenarios. We incrementally increase the complexity of the noise by introducing compound noise mixtures and assess the performance of the model under increasingly challenging OOD conditions. This yields a robustness matrix that captures the generalizability of each model relative to its training configuration. Results indicate that larger noise scales boost generalization, and that effective alignment between noise type, task complexity, and architecture is key for maximizing generalization gains. In addition, training with compound noises mitigate weaknesses associated with single-noise training, acting as an additional implicit regularizer to improving robustness. These findings highlight key factors influencing model resilience in noisy geophysical environments and offer guidance for developing deep learning models that generalize effectively across diverse and unpredictable noise conditions.
Read the full abstract

All authors Salma Alsinan, Maksim Makarenko, Sixiu Liu, Ali Aldawood, Ibrahim Hoteit

Geophysics · Cross-listed from cond-mat.soft

Avalanches can increase stored energy in a granular fault

A. Douin, E. Saurety, V. Levy dit Vehel et al. (+4 more)

Slowly sheared granular materials generally store mechanical energy and dilate between abrupt failures that release energy and compact the material. Here, simultaneous measurements of torque, layer thickness, acoustic emission, and photoelastic force networks reveal all four combinations of energy release or storage with contraction or dilation in a compressed granular fault. Most strikingly, some avalanches both dilate the layer and increase the elastic energy transmitted to the resisting boundary. These events reorganize force chains beyond the shear band and produce a distinct acoustic response. The results show that an avalanche need not relax a driven disordered material; it can instead redistribute stress into a more highly loaded configuration, a mechanism with potential relevance to the physics of both laboratory and natural faults.
Read the full abstract

All authors A. Douin, E. Saurety, V. Levy dit Vehel, L. Combe, L. Vanel, O. Cochet-Escartin, O. Ramos

Friday 11 September

Geophysics · Cross-listed from physics.ao-ph

Bay Assessment Model: A Python-based Salinity Projection Tool for Florida Bay, USA

Erik Stabenau and Joseph Park

Extreme salinity events in Florida Bay are a central concern of the Comprehensive Everglades Restoration Program (CERP). A large-scale seagrass die-off in the late 1980s, driven by hypersaline conditions following reduced freshwater delivery, made reliable salinity prediction essential to restoration planning. The Bay Assessment Model (BAM) is a hydrologic model that simulates salinity across 54 idealized basins representing Florida Bay. It is mass-conservative apart from where required for model stability. Interbasin fluxes are computed from hydraulic gradients across shoals, and each basin receives direct rainfall and evapotranspiration forcing. Along the shoreward boundary BAM is driven by observed water levels from the Everglades Depth Estimation Network (EDEN), or by output from upstream restoration planning models. Along the marine margins, tidal boundary conditions from NOAA subordinate station harmonic constituents are superimposed on a regional mean sea level anomaly. BAM is implemented entirely in Python and is open source. Over the extended 1999-2026 record the domain-wide mean water level bias is -0.024 m with a mean RMSE of 0.092 m, and the domain-wide mean salinity bias is 0.00 ppt with a mean RMSE of 6.21 ppt. Model bias varies systematically with hydrologic regime, increasing in magnitude from drought to wet conditions and reversing sign at one central bay basin, constraining the interpretation of anomaly-based metrics. Applying uniform offsets at the Everglades shoreward boundary yields salinity responses of 2-4 ppt in the northeastern bay, attenuating toward the marine margins; a fuller treatment of restoration and sea level scenarios is reported separately. BAM provides a transparent, efficient, and community-accessible tool for evaluating the salinity consequences of Everglades restoration actions in Florida Bay.
Read the full abstract

Geophysics

ADEPTS: An auto-differentiable framework for time-dependent nonlinear thermo-chemical mantle convection inversion

Zhiying Ming and Jiashun Hu

Time-dependent mantle-dynamics inversion must address the high dimensionality of the initial state, nonlinear rheology, and gradient propagation through long-term thermo-mechanical evolution. We develop ADEPTS, a two-dimensional staggered-grid finite-difference framework for mantle-dynamics inversion based on automatic differentiation. The forward model solves incompressible Stokes flow, temperature advection-diffusion, and compositional advection with temperature- and strain-rate-dependent viscosity and plastic yielding. For the nonlinear Stokes system, we compare two gradient strategies: unrolled differentiation through a fixed number of Picard iterations and implicit differentiation of the converged discrete residual equations. Numerical experiments show that unrolled differentiation remains stable even when the nonlinear solve is not fully converged, whereas implicit differentiation requires sufficiently accurate nonlinear solutions; otherwise gradient consistency and optimization convergence deteriorate. With sufficiently converged solves, implicit differentiation recovers accurate gradients and reconstruction quality comparable to unrolled differentiation. Joint thermo-chemical twin experiments show that ADEPTS can simultaneously recover a high-dimensional initial temperature field and low-dimensional physical parameters, including compositional density, reference viscosity, and stress exponent, while fitting final-time temperature, surface horizontal velocity, and surface normal stress. These results demonstrate the feasibility of differentiable time-dependent mantle-dynamics inversion and clarify the different convergence requirements of unrolled and implicit differentiation.
Read the full abstract

Geophysics · Cross-listed from astro-ph.EP

The core-mantle mode of gravitational oscillation

Mathieu Dumberry

We present the characteristics of a mode of axial oscillation between Earth's mantle, fluid core and solid inner core that has not been previously reported. The mode involves a quasi-rigid rotation of the fluid core outside the tangent cylinder (TC) exchanging its angular momentum with the mantle via a three step process. First, by a magnetic torque with the fluid inside the TC; second by a magnetic torque between the latter and the inner core; and finally by a gravitational torque between the inner core and mantle. Although the gravitational torque is purely between the mantle and inner core, the mode involves an oscillation of the whole of the core, and we refer to it as the core-mantle gravitational (CMG) mode. This form of gravitational oscillation occurs when the magnetic field within the core is sufficiently strong that the propagation time of Alfvén waves is shorter than the mode period, which is the case for Earth. We show how the period, quality factor $Q$ and structure of the CMG mode depends on the strength of the gravitational torque and viscous relaxation time $\tau_i$ of the inner core. For Earth, the CMG mode period should be in the range of 40 to 100 years, but viscous relaxation of the inner core likely implies a small $Q$, below 1 if $\tau_i<10$ years. Our results suggest that the CMG mode may act to amplify resonantly, though only modestly, multi-decadal changes in the length of day driven by zonal accelerations in the fluid core.
Read the full abstract

Geophysics · Cross-listed from physics.comp-ph

Physically Consistent 2D to 3D Pore Space Synthesis: 1. Dynamic Grain Packing Under Strict Morphology Constraints

Dmitry Kulygin, Andrey Ananev and Aleksey Khlyupin

The 3D digital reconstruction of multi-scale, heterogeneous porous media is traditionally limited by the resolution boundaries and artifacts of X-ray computed tomography (XCT). This paper introduces a physically consistent 2D to 3D pore space synthesis framework that supersedes conventional geometric shuffling with dynamic Newtonian gravitational deposition using a high-performance rigid-body mechanics engine. To honor strict physical contact mechanics, statistical 2D targets, and morphological constraints, we develop a two-stage dynamic packing pipeline that combines realistic 3D grain shapes with a virtual sub-particle insertion engine. The framework is validated against synthetic and natural core benchmarks. First, we show that our stereological integral equation systematically converts size distributions; bypassing this inversion step induces a systematic 50% deviation in permeability predictions despite identical total porosities, independently altering flow channel topology. Second, temporary virtual spheres serve as architectural placeholders to stabilize large non-equilibrium dissolution voids, ensuring the reconstructed model satisfies target flow metrics within the prescribed tolerance. Finally, when applied to a fine-grained, weakly consolidated sandstone, the workflow exposes and suppresses systematic XCT artifacts, including phantom internal porosity. Ensembles of two-point correlation functions demonstrate that the hydrodynamically and structurally synthesized digital twins quantitatively exhibit closer structural proximity to the uncorrupted rock geometry than the reference XCT dataset itself. This methodology establishes a scalable foundation for high-fidelity multi-phase rock reconstruction, with potential extensions toward complex amorphous cement phases.
Read the full abstract

Geophysics · Cross-listed from physics.comp-ph

Physics-enriched neural solvers for transient ice-flow simulation

Thomas Gregov, Sebastian Rosier, Brandon Finley et al. (+2 more)

Transient glacier simulations with higher-order ice flow require the repeated solution of a nonlinear problem as the geometry evolves. In the online mode of the Instructed Glacier Model, the velocity field is represented by a neural network whose weights are warm-started from the previous time step and updated with a few optimizer iterations. We show that supplying the network with inexpensive input fields derived from low-order ice-flow balances improves this online solver. Unlike residual-based physics-informed neural networks, which incorporate physics through governing-equation penalties in the loss, our approach leaves the governing energy objective unchanged, adding physical structure through the network inputs. Across three real-world glacier configurations, the enriched solver is markedly more robust to solver settings. On the two alpine cases, it also improves the tuned accuracy–runtime trade-off, reducing surface-velocity errors by factors of two to four at fixed runtime and reaching few-percent relative errors with only $10^4$–$10^5$ trainable parameters, far fewer than comparable raw-input baselines. A 300-year Aletsch simulation then completes in under one minute, and the larger Valais domain in about two minutes, on a single GPU—a budget once reserved for much simpler shallow-ice models. Gains are smaller for the fast marine-terminating glacier, where nonlocal stress coupling favors larger or spectral networks. More broadly, the results suggest that enriching a neural solver's inputs with reduced-order physics can make repeated higher-order solves much cheaper, with no training data and no offline training.
Read the full abstract

All authors Thomas Gregov, Sebastian Rosier, Brandon Finley, Andreas Vieli, Guillaume Jouvet

Thursday 10 September

Geophysics

Preemptive Ensemble Forecast Sensitivity to Observations

Fumitoshi Kawasaki and Shunji Kotsuki

Estimating the impact of observations newly added to an existing numerical weather prediction (NWP) system requires reintegrating the ensemble forecast from the analysis that assimilates the additional observations, which is laborious and computationally expensive. We therefore propose preemptive EFSO (PEFSO; preemptive ensemble forecast sensitivity to observations), which estimates observation impact without model reintegration by invoking a stronger tangent-linear approximation than that used in EFSO. The estimated impact, however, is merely a scalar quantity at a specified verification time, so it is also valuable to obtain the ensemble forecast in which the remaining additional observations are assimilated after denying the detrimental ones. We refer to this procedure as ADD-SEL, but carrying it out requires reintegration. We therefore propose two methods for updating the ensemble forecast without reintegration as approximations to ADD-SEL, collectively termed ADD-SEL-PRE: one recomputes the ensemble transform matrix (i.e., the Kalman gain), and the other modifies only the innovations at lower cost. Experiments with the Lorenz-96 model examine whether both can be approximated without reintegration. PEFSO estimates observation impact comparable to that obtained by EFSO within the range where the tangent-linear approximation remains valid, even under more practical conditions: an ensemble size of 10, the analysis as the reference state, and only the additional observations available. When additional observations are assimilated after denying detrimental ones, both ADD-SEL-PRE methods update the ensemble forecast, giving forecast errors comparable to those from ADD-SEL, particularly for lead times up to two days, where the tangent-linear approximation is expected to hold.
Read the full abstract

Geophysics

Ensemble Forecast Updates without Model Re-integration Based on Ultra-rapid Data Assimilation: Idealized Experiments with a Heavy Rainfall Case

Fumitoshi Kawasaki, Kenta Kurosawa, Atsushi Okazaki et al. (+1 more)

Observations at high frequency have become dramatically more abundant with recent technological advances. Ultra-rapid data assimilation (URDA) has been proposed to exploit them, frequently updating ensemble forecasts at lower cost without re-integrating the forecast model. In this study, we examine the applicability of URDA to a realistic numerical weather prediction (NWP). Specifically, we conducted idealized experiments for the heavy rainfall event of August 2021, using the regional atmospheric model Scalable Computing for Advanced Library and Environment (SCALE-RM). In the experiments, pseudo-observations emulating the Automated Meteorological Data Acquisition System (AMeDAS) were assumed to become available every 10 min. The results show that the threat score for hourly precipitation is generally improved in the forecasts updated by URDA relative to the pre-existing baseline forecasts, through the ensemble-based error covariance. Furthermore, for sea level pressure, temperature, relative humidity, and winds, the root mean square error of the URDA-updated forecasts is reduced relative to that of the baseline forecasts as the forecasts are successively updated by assimilating additional observations. These results indicate the potential of URDA to operate effectively with a realistic NWP model.
Read the full abstract

All authors Fumitoshi Kawasaki, Kenta Kurosawa, Atsushi Okazaki, Shunji Kotsuki

Geophysics · Cross-listed from physics.comp-ph

Stress-Testing Dynamical and Generative Downscaling Using Subseasonal Extreme Precipitation Forecasts

Mauricio Lima, Marika Koukoula, Romain Pilon et al. (+4 more)

Coarse spatial resolution limits the ability of subseasonal prediction models to resolve extreme precipitation. Downscaling with either dynamical or deep generative models can overcome this issue, but the comparative performance of these models for extremes across different atmospheric regimes remains poorly understood. In this work, we evaluate the Weather Research and Forecasting (WRF) model against a diffusion-based generative model by downscaling two physically distinct, extreme precipitation events up to lead times of 3 weeks. For a fair comparison with WRF, which can downscale boundary conditions from different driving models without model-specific training, the diffusion model is trained in an unpaired fashion. Both approaches improve upon the raw European Centre for Medium-Range Weather Forecasts forecasts, in comparison to fused rain gauge-radar observations in Switzerland (CombiPrecip), but exhibit regime-dependent strengths. WRF achieves the highest probabilistic skill for a multicell, non-stationary event. Conversely, the diffusion model is more consistent across different performance metrics for the two events, outperforming WRF in a more stationary supercell event. These results demonstrate that explicit dynamical modeling can add value for specific precipitation events for subseasonal lead times, and that generative downscaling adds value more broadly in different situations.
Read the full abstract

All authors Mauricio Lima, Marika Koukoula, Romain Pilon, Monika Feldmann, Erwan Koch, Daniela I.V. Domeisen, Tom Beucler

Geophysics · Cross-listed from astro-ph.EP

Lava Tube Exploration with LunarLeaper

Anna Mittelholz, Simon C. Stähler, Valentin T. Bickel et al. (+9 more)

Lunar pits, some of which are interpreted as collapse features into underlying lava tubes, expose otherwise inaccessible stratigraphy and may provide entry points to subsurface voids that preserve records of lunar volcanism and offer potential sites for future human exploration. We synthesize the current state of knowledge on lunar pits and lava tubes, covering their morphological characteristics, classification, proposed formation mechanisms, mechanical stability, and detection from orbit. We then review the open science questions that pit and pit-wall investigation is uniquely placed to address, spanning the volcanic stratigraphy of the lunar maria, the structure and lateral variability of the regolith, and the dimensions and accessibility of subsurface conduits. To evaluate how these questions can be tackled in situ, we assess the feasibility and expected performance of geophysical and remote-sensing investigations for subsurface voids and surface exposures, mainly focusing on gravity measurements, ground-penetrating radar, high-resolution imaging, and spectroscopy. Building on this, we present LunarLeaper, a small legged robot mission concept combining a gravimeter, ground-penetrating radar, high-resolution imager, spectrometer, and leg-based geomechanical experiments to deliver the first in situ investigation of a mare pit. The concept targets the Marius Hills Pit and its associated rille, with a mobility architecture optimized for the rugged terrain encountered at pit edges and funnel slopes.
Read the full abstract

All authors Anna Mittelholz, Simon C. Stähler, Valentin T. Bickel, Jordan Aaron, Aurelie Cocheril, Alessandro Ghirotto, Matthias Grott, Svein-Erik Hamran, Ozgur Karaketin, Alessandro Maturilli, Birgit Ritter, Alexis Shakas

Geophysics · Cross-listed from eess.SP

Optimizing GEDI Simulator Configuration for European Temperate Forests

Selim Behloul, Nikola Besic, Steven Hancock et al. (+5 more)

Accurate estimation of aboveground biomass density is essential for quantifying forest carbon stocks. NASA's GEDI mission provides valuable canopy structure data, but its sparse sampling necessitates the use of simulators to calibrate biomass models at field inventory locations. The widely used simulator of Hancock et al. (2019) emulates GEDI waveforms from airborne LiDAR point clouds, yet it has never been validated over European temperate forests. Here, we compare approximately 9,500 pairs of observed and simulated GEDI relative height (RH) profiles across French forests using the national airborne LiDAR program as input. We separate two sources of error: waveform modeling differences, assessed by referencing both simulated and real RH metrics to a common ALS-derived ground elevation, and ground detection bias, evaluated by comparing each GEDI L2A processing algorithm against the ALS reference. Under the baseline configuration, the mean absolute bias across the full RH profile reaches 0.69 m in leaf-on and 1.28 m in leaf-off conditions. Switching to intensity-based return weighting and selecting the a3 L2A algorithm reduces these biases to 0.44 m and 0.40 m respectively. The a3 algorithm also achieves near-unbiased ground detection (-0.03 m versus -0.88 m for the default), directly reducing a previously overlooked source of error. We also show that leaf-off acquisitions and low-sensitivity shots, both typically excluded from standard biomass products, are simulated as reliably as their counterparts, substantially expanding the potential calibration and inference datasets.
Read the full abstract

All authors Selim Behloul, Nikola Besic, Steven Hancock, Cedric Vega, Sylvie Durrieu, Jean-Pierre Renaud, Ibrahim Fayad, Philippe Ciais

Geophysics · Cross-listed from physics.flu-dyn

DiffSWE2d: a differentiable Shallow Water Equations solver for end-to-end flood and tsunami modelling

Zhonghou Xu

Solving inverse and optimisation problems with traditional shallow water equations (SWE) solvers can be computationally expensive, particularly when gradients with respect to model inputs or parameters must be estimated through repeated forward simulations. In this paper, we introduce DiffSWE2d, an open-source differentiable shallow water equations solver for end-to-end flood and tsunami modelling implemented in PyTorch. By leveraging automatic differentiation, DiffSWE2d represents the time-marching physics as a differentiable computational graph, enabling gradients to be propagated directly through the numerical solver. We validate the solver against two established benchmark cases and demonstrate its application to tsunami waveform inversion, showing its ability to infer model inputs through gradient-based optimisation. DiffSWE2d provides a flexible framework for integrating physics-based hydrodynamic modelling with modern optimisation and machine learning methods. The source code and reproducible examples are publicly available at: https://github.com/ZhonghouXu/DiffSWE2d
Read the full abstract