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

Wednesday 7 October

Geophysics · Cross-listed from cs.LG

Self-attention summary networks for subsurface velocity-model building from common-image gathers

Shiqin Zeng, Yunlin Zeng, Abhinav Prakash Gahlot et al. (+2 more)

Common-image gathers (CIGs) contain physically meaningful information about velocity-model errors through reflector focusing and residual moveout, but in conventional imaging workflows they are typically used only as diagnostic tools. In this work, we propose a multiscale self-attention summary network that maps high-dimensional 3D CIG volumes into compact conditioning embeddings for probabilistic subsurface velocity inversion. These learned embeddings preserve offset-dependent kinematic structure and spatial coherence while reducing variability caused by background-velocity mismatch. Conditioned on these summary embeddings, a flow-matching model learns a transport from a Gaussian source distribution to the posterior distribution of plausible velocity fields. Numerical experiments show that, compared with direct conditioning on raw CIGs, the proposed summary network improves posterior velocity inference. In particular, the multiscale attention design provides greater robustness to background-model mismatch, yielding more accurate posterior reconstructions and lower predictive uncertainty.
Read the full abstract

All authors Shiqin Zeng, Yunlin Zeng, Abhinav Prakash Gahlot, Zijun Deng, Felix J. Herrmann

Geophysics · Cross-listed from cs.LG

Twist Flow for Inverse Problems

Shiqin Zeng, Zijun Deng and Felix J. Herrmann

In Bayesian inverse problems, posterior sampling requires generating samples that are consistent with given observations while capturing the range of plausible solutions. Direct conditional generative models introduce latent noise to model this ambiguity, but paired inverse-problem training can still encourage an almost deterministic map from the observation to the target. As a result, generated samples may be observation-consistent while under-representing posterior variability, especially when the posterior is multimodal, leading to undercoverage, mode distortion, or artificial transitions between distinct feasible solutions. We propose joint twist-flow, an augmented flow-matching formulation that learns a continuous transport from the augmented source state $(z_x, y)$ to the augmented terminal state $(x, z_y)$. Here x is the target variable, $y$ is the observation, $z_x$ is the Gaussian reference coordinate for posterior sampling, and $z_y$ is a Gaussian likelihood-side coordinate associated with the observation branch. Under a Gaussian observation model, $z_y$ is motivated by the normalized observation residual associated with observation compatibility. Its role is not to replace uncertainty in $x$, but to couple generated samples of x to observation consistency, helping reduce likelihood-inconsistent variation while preserving variability in weakly constrained directions. We validate the method on low-dimensional inverse problems with reference posterior samples, where joint twist-flow better preserves multimodal posterior support than a direct conditional-flow baseline. We further evaluate the method on image restoration and seismic subsurface velocity-model inversion, showing increased posterior variability while maintaining observation consistency.
Read the full abstract

Tuesday 6 October

Geophysics · Cross-listed from astro-ph.IM

Assessment of sky brightness in central Portugal: the Dark Sky destination "Aldeias do Xisto" case study

Domingos Barbosa, Dalmiro Maia, Bruno Morgado et al. (+5 more)

A combination of remote sensing techniques, open space data and field testing was developed to derive astronomical and meteorological parameters and assess Dark Sky brightness of the hitherto unstudied Schist Villages region in the mountainous Interior Center of Portugal. This study covered the period 2012-2017 and established the methodology for regular light pollution and sky brightness studies in the region using satellite imagery to reduce cost and time and increase accuracy of the final analysis. Long-term Artificial Light At Night (ALAN) data from VIIRS instrument aboard the Suomi-NPP satellite along with cloud cover inferred from MODUS/AQUA satellite data and orographic data, were analyzed in a Geographical Information Systems (GIS) environment. The derived ALAN values were consistent with a preliminary site survey selection, confirming the accuracy of the identified dark sky spots in the region. The analyzed region area spans 1624 Km$^2$ and the darkest site within it showed a sky brightness of 21.4 mag/arcsec$^2$. Ground-based SQM photometer observations of the Night Sky Brightness (NSB) at the zenith validated the satellite derived ALAN data. The devised field testing is itself a low cost solution that can be used by teams in developing countries to easily assess dark sky potential prior to the development of local observatories and astro-tourism activities. This study supported the initial certification of this region as a Dark Sky Starlight Tourist Destination since 2018, an important milestone for dark sky protection in central Portugal.
Read the full abstract

All authors Domingos Barbosa, Dalmiro Maia, Bruno Morgado, Pedro Cruz, Bernardo Relvas, Valério Ribeiro, Allan Kardec de Almeida Jr., Timothée Vaillant

Geophysics

Principal Surface Fault Rupture Hazard: Moving Beyond Aggregate Displacement Metrics

Saba Marmarchinia and Grigorios Lavrentiadis

Structures crossing active faults are subjected to permanent ground deformation that can impose severe demands on infrastructure. Although probabilistic fault displacement hazard analysis (PFDHA) has advanced considerably, existing principal fault displacement models use aggregate displacement, implicitly assuming that overlapping rupture strands contribute simultaneously to site demand. This assumption is suitable for regional hazard characterization but does not represent finite structural dimensions, where only a subset of ruptures may intersect a structure. This study presents a probabilistic framework that reformulates aggregate principal displacement into a rupture-level representation for infrastructure-specific hazard assessment. Using the Fault Displacement Hazard Initiative (FDHI) database of 75 surface-rupturing earthquakes, principal rupture strands are reconstructed from mapped rupture geometries and displacement observations using event- and segment-based coordinate systems. The framework models three quantities: (1) the number of overlapping principal ruptures, (2) partitioning of aggregate displacement among individual rupture strands while preserving the aggregate displacement budget, and (3) the spatial distribution of rupture strands relative to the primary fault trace. These components are integrated into a generalized PFDHA formulation accounting for earthquake magnitude, style of faulting, rupture curvature, structural footprint, and fault-trace location uncertainty. Benchmark fault-crossing applications demonstrate the framework's ability to estimate the probability that individual rupture strands intersect a structure and the associated displacement demand. The framework provides a physically consistent extension of aggregate-based models and a basis for infrastructure-specific PFDHA that explicitly accounts for rupture geometry and finite crossing dimensions.
Read the full abstract

Geophysics

Ground roll suppression based on the synchrosqueezed wavelet transform and local adaptive subtraction filtering

Zifei Li, Zhengyu Tan and Shaohuan Zu

Ground roll suppression remains a challenge in land seismic data processing, particularly when strong surface wave energy exhibits significant overlap with effective reflection events in the low frequency band. Conventional approaches, such as frequency-wavenumber (F-K) filtering, Radon transform-based methods, and other spatial filtering techniques, often fail to achieve satisfactory results under such conditions due to limited resolution and poor amplitude preservation. To address this problem, a time-frequency domain ground roll suppression method that integrates the synchrosqueezed wavelet transform (SSWT) with local adaptive subtraction filtering is proposed. First, seismic traces are transformed from the time-space domain into the time-frequency domain using SSWT, which provides a highly concentrated time-frequency representation. Based on the distinct distribution characteristics of ground roll noise and effective reflection signals in the time-frequency domain, a ground roll model is extracted. Subsequently, a local adaptive subtraction filter is applied to adjust the amplitude and phase of the extracted ground roll model, and the corrected model is subtracted from the original seismic data to obtain the final suppressed result. Applications to both synthetic and field seismic data demonstrate that the proposed method effectively attenuates ground roll energy while maximally preserving the amplitude and waveform characteristics of reflection signals. Compared with conventional time-frequency-based ground roll suppression approaches, the proposed method exhibits superior amplitude fidelity and improved noise attenuation performance.
Read the full abstract

Monday 5 October

Geophysics · Cross-listed from physics.flu-dyn

A semi-infinite hydraulic fracture propagating in a poroelastic medium

Evgenii Kanin, Andreas Möri, Dmitry Garagash et al. (+1 more)

We solve the near-tip coupled hydromechanical problem of a hydraulic fracture propagating in a permeable poroelastic medium, represented locally as a semi-infinite crack moving at constant velocity. In the moving tip frame, all fracture fields are steady. The porous solid obeys linear poroelasticity, fracture propagation follows LEFM, fluid flow within the tensile fracture is governed by lubrication theory, and the fracturing and pore fluids are treated as identical Newtonian liquids. The primary unknowns are the fracture opening and the fluid displacement function along the fracture, determined from the resulting non-linear system of equations. In a viscosity–toughness scaling, the problem depends on four dimensionless parameters: a dimensionless permeability $\varrho$ (associated with the intensity of fluid leak-in), a dimensionless effective confining stress $\Sigma_0'$ (measuring the relative importance of fluid leak-off and leak-in), the drained–undrained Poisson's ratio contrast $\beta$, and the poroelastic stress coefficient $\eta$. The solution evolves from a drained, toughness-dominated near-tip asymptote with uniform net pressure to an undrained, storage-viscosity-dominated far-field asymptote. Numerical solutions are obtained over parameter ranges representative of various rock types. The fully coupled model is compared with simplified models, namely uncoupled and one-way coupled formulations, over distances from the fracture front spanning laboratory-to-field scales. The resulting relative difference maps in the $(\varrho,\Sigma_0')$ parameter space identify four characteristic regimes: negligible poroelastic effects, dominance of the direct mechanical poroelastic response, importance of the poroelastic backstress, and a fully coupled regime. The latter occurs mainly at large $\varrho$ and small $\Sigma_0'$, corresponding to strong leak-in relative to leak-off.
Read the full abstract

All authors Evgenii Kanin, Andreas Möri, Dmitry Garagash, Brice Lecampion

Saturday 3 October

Geophysics

Broadband Ground-Motion Synthesis by Conditioning Denoising Diffusion Models on Low-Frequency Waveforms

Kadek Hendrawan Palgunadi, Simon Dirmeier, Maria Koroni et al. (+2 more)

Seismic hazard and structural analyses require broadband ground-motion time histories, yet the high-frequency band (above 1~Hz) is the hardest to obtain: physics-based simulations are too costly at these frequencies, and stochastic methods lose the deterministic phase, directivity, and long-period coherence that control structural demand. We present HighFEM-2, a latent denoising-diffusion waveform model trained with rectified flow matching on a multi-modal diffusion transformer. It synthesizes three-component acceleration up to 50~Hz, conditioned on a low-frequency waveform below the source corner frequency and on magnitude, hypocenter and station location, and $V_{S30}$. The low-frequency waveform supplies the deterministic source, path, and directivity, so the network learns only the regional high-frequency scattering. Conditioning each station on its own waveform lets HighFEM-2 generate records for all stations of an earthquake that share its source and low-frequency path terms. Trained on 195{,}138 Italian INSTANCE records ($M_w$ 1.4–6.5, within 200~km), HighFEM-2 produces realistic waveforms: it reproduces observed peak amplitudes with small bias ($0.042 \pm 0.088$ log units for PGA, $0.057 \pm 0.090$ for PGV) and matches observed envelopes, Fourier spectra, and integrated velocity and displacement. The conditioning waveform can come from a simulation or a filtered record, and ensembles of realizations sample the high-frequency variability given that input. HighFEM-2 thus provides fast, regionally calibrated broadband time histories for hazard and risk assessment, including near-fault scenarios where recordings are scarce.
Read the full abstract

All authors Kadek Hendrawan Palgunadi, Simon Dirmeier, Maria Koroni, Laura Ermert, Men-Andrin Meier

Friday 2 October

Geophysics

Eigenwaves: Finite-frequency Eigenrays in Elastic Anisotropic Media

Zvi Koren

The Eigenray method solves two-point boundary-value ray-tracing problems in heterogeneous anisotropic elastic media using a variational formulation based on the eikonal equation. Here, we extend the method to finite frequencies using the trajectory-mechanics formulation in which finite-frequency effects are represented by a frequency-dependent potential that couples phase and amplitude. We show that finite-frequency propagation can be described within the same variational framework through an equivalent effective medium, preserving the structure of the original method while introducing corrections to trajectories, phase, amplitudes, geometric spreading, wavefront curvature, and caustic behavior. The resulting Eigenwave formulation requires only local modifications of the ray velocity and its derivatives and reduces to classical ray theory in the high-frequency limit. Numerical examples involving multipathing and cusp caustics demonstrate accurate modeling of finite-frequency propagation effects. While ray-based amplitudes become unreliable near caustics, a phase-space (Maslov) treatment successfully reproduces finite-difference wavefield solutions. These results establish Eigenwave theory as a unified framework connecting ray theory and finite-frequency wave propagation in heterogeneous anisotropic media.
Read the full abstract

Geophysics · Cross-listed from physics.ao-ph

A Multivariable Approach to the Regional Calibration of the Galvez Davison Index across Mexico Climate Regimes

Mauricio López-Reyes, Carlos Calvo-Sancho, Angel Meulenert et al. (+5 more)

The Gálvez Davison Index (GDI) is a thermodynamic diagnostic designed to summarize convective favorability in the tropical regions, yet its transferability across complex terrain and heterogeneous hydroclimates remains uncertain. Here we assess the climatological behavior and precipitation relevance of the GDI over Mexico using ERA5 hourly fields and CHIRPS daily precipitation for 1981 to 2024. We characterize the seasonal cycle and spatial contrasts of GDI and evaluate its association with rainfall using grid point and regional Pearson and Spearman correlations between daily maximum GDI and daily precipitation. To introduce an independent convective proxy, we also quantify the relationship between hourly GDI and ERA5 outgoing longwave radiation (OLR). Event level discrimination skill is assessed with receiver operating characteristic (ROC) curves and area under the curve (AUC) across regions and months. While GDI precipitation correlations are moderate and region dependent, GDI OLR coupling is consistently stronger, partly because OLR approaches a lower bound once deep convection develops, supporting its use as an intermediate proxy between thermodynamic favorability and rainfall. However, GDI values are systematically lower over elevated terrain because of the pressure level formulation of the index, which should be considered when interpreting the GDI over mountainous regions. ROC results indicate skill above random chance in nearly all region month combinations, with AUC frequently exceeding 0.75 during the core convective season. Building on these diagnostics, we propose a regionally calibrated GDI convective regime scale for May to October, adapted from the original GDI framework but recalibrated for Mexico using event conditioned distributions defined from OLR and precipitation and quantified with bootstrap uncertainty.
Read the full abstract

All authors Mauricio López-Reyes, Carlos Calvo-Sancho, Angel Meulenert, José Francisco León-Cruz, Jose Manuel Gálvez, Javier Díaz Fernandez, Juan Jesús González-Alemán, María Luisa Martín Pérez

Geophysics

Spatially Organized Hillslope Weakening Localizes Retrogressive Thaw Slumps across the Tibetan Plateau

Yuting Yang, Gang Mei, Nengxiong Xu et al. (+2 more)

Long-term permafrost warming on the Tibetan Plateau is continuously and broadly reshaping cold-region landscapes, and has produced numerous localized and clustered retrogressive thaw slumps (RTSs). However, the question why RTSs are highly localized and clustered under the widespread permafrost warming remains incompletely understood. To understand and explain the RTS localization and clustering, we propose the Hillslope Weakening Field (HWF), which represents the spatial distribution of relative hillslope weakening states jointly formed by multiple environmental conditions, including thermal conditions, moisture and soil properties, topographic setting, permafrost and ground-ice conditions, and land-surface cover. We examined the correspondence between the HWF and RTS spatial selectivity across seven study regions on the Tibetan Plateau. Positive HWF-RTS correspondence was observed in six of the seven regions, with consistently strong relationships in five. In these five regions, RTSs preferentially occurred on more strongly weakened hillslopes, with occurrence rates 3.35-5.09 times the regional background rate within the 10% most strongly weakened slope units. Moreover, high-weakening hillslopes were not spatially isolated but correlated and connected across the landscape. RTS-bearing slope units and their surrounding hillslopes showed higher weakening levels than RTS-unrecorded slope units and their surroundings, and the weakening contrast declined with distance. These results suggest that RTSs preferentially occur on more strongly weakened hillslopes and cluster where high-weakening states are spatially continuous and connected. Our findings show how spatial differences in hillslope weakening states can help explain why geomorphic responses to widespread permafrost warming are highly heterogeneous across permafrost landscapes.
Read the full abstract

All authors Yuting Yang, Gang Mei, Nengxiong Xu, Yongshuang Zhang, Jianbing Peng

Geophysics · Cross-listed from math.HO

Structure-Driven Methodology: An Emerging Cross-Domain Paradigm

Shengchang Chen

This article proposes and articulates structure-driven methodology as an emerging cross-domain paradigm. In contrast to the objective-driven paradigm that has dominated inverse-problem solving for over two centuries, structure-driven methodology advocates identifying and exploiting the intrinsic structure of a problem to construct the solution method directly, rather than approaching the solution through iterative search. Drawing on examples from the Liouville-variant Goldbach conjecture, the three-dimensional Kakeya conjecture, Noether's theorem, and the author's nearly four decades of geophysical inversion practice, the article illustrates the common logic of structure-driven thinking in number theory, pure mathematics, physics, and seismic imaging. The author also systematically proposes the Structure-Driven Inversion (SDI) paradigm, comprising Mathematical Structure-Driven Inversion (MSDI) and Physical Structure-Driven Inversion (PSDI). Structure-driven methodology does not negate objective-driven methodology but complements it: objective-driven is a general search strategy, whereas structure-driven is a custom direct strategy. The article concludes that the significance of this shift lies not only in efficiency gains but also in cognitive deepening—when facing complex problems, the first question is no longer “how to solve the problem faster” but “what is the structure of the problem.”
Read the full abstract

Thursday 1 October

Geophysics

Quantum spin regulates the catalytic cycle beyond binding energy

K. Regenauer-Lieb, W. Li, Z. Xia et al. (+7 more)

Conventional catalytic descriptors evaluate symmetric, dissipative responses ($\mathcal{D}$), remaining blind to the antisymmetric, non-dissipative circulation ($\mathcal{L}$) associated with cyclic catalyst turnover. We address this by applying an augmented Onsager operator, $\mathcal{A} = \mathcal{D} + \mathcal{L}$, to catalytic interfaces. Through Onsager–Casimir parity rules, we show that electron spin governs this non-dissipative circulation within the reduced state space, as antisymmetric coupling necessitates a time-reversal-odd coordinate. We map these multi-physical couplings (Thermo-Hydro-Mechanical-Chemical-Electrical-Spin) using a Hasse diagram and introduce a basis-invariant Gateway number to quantify circulation relative to dissipation, thereby expanding standard one-dimensional volcano plots into a two-dimensional activity surface. Using metal–nitrogen single-atom sites on doped carbon as a model, published electronic-structure calculations supply the static response quantities from which both operator components can be constructed. Finally, we propose an explicitly falsifiable experimental test utilising magnetic field modulation near a spin-crossover to probe the effect of quantum spin on this non-dissipative regime.
Read the full abstract

All authors K. Regenauer-Lieb, W. Li, Z. Xia, Y. Jiao, Y. Liu, Y. Chen, Q. Xie, Q. P. Sun, B. Hamblin, V. Calo

Geophysics

Multi-Modal Surface Wave Inversion using Physics Guided Variational Autoencoders

Sujith Swaminadhan, Andrey Bakulin, Sergey Fomel et al. (+1 more)

The growth in seismic acquisition techniques has led to large-scale surveys, making traditional manual dispersion analysis logistically and computationally prohibitive. While deep learning techniques can offer an automated alternative, standard convolutional neural networks (CNNs) often fail to account for the non-uniqueness of the inverse problem and can produce geologically unrealistic, smooth velocity models. To address these challenges, we propose a physics-guided multi-modal variational autoencoder (PG-VAE) for scalable near-surface characterization. Our framework introduces a multi-stream fusion strategy that maps independent latent features from both raw shot gathers and spectral-dispersion images by leveraging the physics of Rayleigh-wave dispersion. We implement a specialized decoder architecture with learned transposed convolutions and residual refinement to enforce a structural bias towards layered earth models. Physics guidance is incorporated via a differentiable surrogate network that serves as a forward-consistency regularizer, penalizing predictions that violate the physics of Rayleigh-wave dispersion. The network is trained on diverse synthetic data generated via elastic modeling, and then validated on unseen synthetic data, and subsequently evaluated on an ultra-high-density field dataset from the Devine test site in Texas. We also employ gradient-weighted class activation mapping (Grad-CAM) to examine the input features contributing to the network predictions. The proposed method could serve as a scalable tool for near-surface characterization and as an initial model-building tool for high-resolution elastic full-waveform inversion.
Read the full abstract

All authors Sujith Swaminadhan, Andrey Bakulin, Sergey Fomel, Yangkang Chen

Geophysics

SW1D: A Python package for seismic surface wave solutions in 1-D elastic media

Piyush Kolhe, Arjun Datta, Shijil Umer et al. (+1 more)

We present SW1D, a Python version of a legacy code for the forward computation of Rayleigh and Love wave dispersion curves as well as synthetic seismograms, in elastic, isotropic, laterally homogeneous media. This code solves the surface wave eigenvalue problem for 1-D media using a propagator-matrix technique and subsequently obtains surface wave excitations, in the frequency domain, for a point source described by its moment tensor. SW1D is validated through rigorous theoretical analyses and benchmarked against a well-established seismological software package. Ultimately, we demonstrate its place within an ecosystem of Python-based seismological tools designed for niche applications such as modelling surface wave propagation across lateral discontinuities, or forward modelling of ambient noise cross-correlations with arbitrary spatial distributions of ambient seismic sources.
Read the full abstract

All authors Piyush Kolhe, Arjun Datta, Shijil Umer, Bivas Das

Geophysics

Flow-rate controls on trapped hydrogen redistribution in a heterogeneous sandstone: a synchrotron X-ray micro-CT study

Catherine Spurin, Sojwal Manoorkar, Sharon Ellman et al. (+7 more)

Subsurface hydrogen storage in porous geological formations is a promising strategy for balancing temporal mismatches between renewable energy generation and demand. The viability of large-scale hydrogen storage depends on predicting hydrogen distribution, trapping, and recovery during cyclic injection and withdrawal operations. However, these multiphase flow processes are influenced by rock heterogeneity, capillary hysteresis and trapping at the pore-scale. In this work, fast in-situ synchrotron X-ray tomography was used to directly visualise hydrogen mobilisation during brine imbibition in a heterogeneous sandstone. By systematically varying the brine injection rate, the influence of flow conditions on hydrogen recovery, and the evolution of trapped hydrogen were quantified. Significant mobilisation of disconnected hydrogen clusters was observed during imbibition, even at low capillary numbers ($< 10^{-6}$). Rock heterogeneity influenced trapping behaviour, particularly during the early stages of imbibition. Our results highlight the importance of incorporating pore-scale heterogeneity and transient trapping mechanisms into hydrogen storage predictions.
Read the full abstract

All authors Catherine Spurin, Sojwal Manoorkar, Sharon Ellman, Ian B. Butler, Aliakbar Hassanpouryouzband, Damien Freitas, Fernando Alvarez-Borges, Robert Atwood, Katriona Edlmann, Eike Thaysen

Geophysics

Coarse-to-fine multi-resolution hash encoding for implicit full waveform inversion

Linrong Wang, Shaowen Wang, Fan Min et al. (+1 more)

Implicit full waveform inversion (IFWI) reparameterizes the velocity model with a neural network, but it suffers from spectral bias and requires many iterations to converge. Multi-resolution hash encoding uses a learnable feature grid to recover high wavenumber details at multiscales and provides strong local representations. However, activating all resolution levels from the start of the inversion introduces high wavenumber components, which can exacerbate cycle skipping. In this paper, we propose a coarse-to-fine IFWI (C2F-IFWI) that activates the multi-resolution hash levels progressively rather than all at once. Specifically, only a few coarse base levels are kept active at initialization, while the remaining levels are introduced progressively. A frozen hash level contributes no gradient until it is activated, so the coarse levels resolve the long-wavelength background before the finer levels are introduced regardless of the frequency range of the data. A continuous activation index increases with iterations and determines how many levels are active at any point in training. Each newly admitted level is then faded in gradually through a smooth weighting rather than switched on abruptly, which keeps the optimization stable during transition. This scheme allows model capacity to grow synchronously with wavenumber without adding any additional trainable parameters and with negligible computational overhead. Experiments on the synthetic Overthrust and BP 2004 models and on the Viking marine field dataset show that, compared with the all-resolution hash baseline, the coarse-to-fine scheme converges faster, attains higher accuracy, and is markedly more robust to the choice of initial model and to strong data noise.
Read the full abstract

All authors Linrong Wang, Shaowen Wang, Fan Min, Tariq Alkhalifah

Geophysics

GeoFWI3D: Large-scale 3D Velocity Model Dataset for Deep Learning-assisted Seismic Imaging

Sujith Swaminadhan, Yiran Shen, Chao Li et al. (+11 more)

We introduce GeoFWI3D, a large-scale open-source benchmark dataset of geologically plausible 3D subsurface models designed to accelerate deep learning (DL) assisted seismic imaging and full waveform inversion (FWI). FWI is a physics-driven, wave-equation-based optimization technique used in seismic imaging to estimate the subsurface properties. However, traditional FWI is highly non-linear, non-unique, and ill-posed. The iterative process often converges to local minima when the starting model is insufficiently accurate. Most importantly, 3D FWI is prohibitively expensive. With the advent of DL, a direct mapping between the shot gathers and subsurface properties can be established by training a network on realistic models. The primary bottleneck for such approaches is the lack of large-scale, realistic training datasets. GeoFWI3D addresses this gap with 10,000 geologically diverse velocity models at 96x96x96 resolution, spanning four structural complexity classes: pure stratigraphy, faulted networks, salt diapirism, and complex coupled fault-salt systems. Each model is accompanied by co-registered multi-modal labels including compressional velocity (Vp), zero-offset seismic reflectivity, relative geologic time (RGT), and semantic fault/salt masks. To facilitate systematic evaluation, we introduce benchmark tasks covering fault detection, joint salt body segmentation and chronostratigraphy prediction, FWI, wavefield and traveltime surrogates using neural operators, and generative modeling with a 3D diffusion model. We present baseline results for each task to establish reference performance metrics for future users. The dataset is publicly available under Creative Commons Attribution 4.0 International.
Read the full abstract

All authors Sujith Swaminadhan, Yiran Shen, Chao Li, Kai Gao, Ting Chen, Sergey Fomel, Tolulope Agbaje, Yang Cui, Liuqing Yang, Jaewook Lee, Robin Dommisse, Umair bin Waheed, Mrinal K. Sen, Yangkang Chen

Wednesday 30 September

Geophysics · Cross-listed from cs.LG

A library for differentiable signal processing and machine learning on the sphere

Thorsten Kurth, Max Rietmann, Mauro Bisson et al. (+6 more)

The two-dimensional sphere embedded in three-dimensional Euclidean space S2, plays a central role in a variety of scientific and engineering domains, including geophysics, planetary science, geodesy, atmospheric physics, quantum chemistry, cosmology, and virtual reality, among many others. As machine learning increasingly permeates these fields, the demand grows for robust tools that process and model functions on the sphere, while respecting the inherent topological and symmetry properties of the domain. We present torch-harmonics, a comprehensive library that offers efficient, differentiable implementations of advanced signal processing and machine learning (ML) methods for spherical data. These include the spherical harmonic transform (SHT), the spherical analogue of the Fourier transform, vector spherical harmonics, discrete-continuous and spectral convolutions, as well as both global and neighborhood spherical attention mechanisms. Beyond traditional representations, torch-harmonics provides the building blocks for state-of-the-art spherical ML architectures such as spherical transformers in order to enable scalable, rotationally-aware learning and inference in modern scientific and engineering applications.
Read the full abstract

All authors Thorsten Kurth, Max Rietmann, Mauro Bisson, Andrea Paris, Alberto Carpentieri, Jean Kossaifi, Anima Anandkumar, Christian Hundt, Boris Bonev

Geophysics · Cross-listed from physics.chem-ph

Elasticity of polycrystalline davemaoite constrains its grain size in the lower mantle

Peiyu Zhang, Liang Yuan, Rong Huang et al. (+3 more)

Earth's lower mantle hosts large low shear-velocity provinces (LLSVPs) beneath Africa and the Pacific, but their origin remains debated. Davemaoite, a major lower-mantle mineral, has been proposed as a contributor to these anomalies; however, its experimentally measured shear modulus is approximately 30% lower than first-principles predictions, a longstanding discrepancy between theory and experiment. Here, using million-atom machine-learning molecular dynamics simulations, we show that nanoscale grain-boundary disorder, invisible to conventional x-ray diffraction, can reconcile this gap. Introducing 5.8 vol% disordered regions reduces the shear and bulk moduli by 37% and 12%, respectively. This shear-selective softening can reproduce LLSVP-like seismic anomalies in basalt-rich assemblages, but only at disorder levels corresponding to grain sizes smaller than those expected in the lower mantle. Combining the disorder-elasticity relationship with seismic constraints, we derive a minimum davemaoite grain size of approximately 100 nm. This limit is consistent with mantle grain growth models and with attenuation-based evidence for coarse-grained LLSVPs.
Read the full abstract

All authors Peiyu Zhang, Liang Yuan, Rong Huang, Yu Ye, Xiang Wu, Junfeng Zhang

Geophysics · Cross-listed from physics.flu-dyn

Rigorous vertical averaging for pseudo-3D modeling of precipitation obstructed fluid flow

David Krach, Felix Weinhardt, Holger Class et al. (+2 more)

Computing effective hydraulic properties of porous materials with a dynamically changing pore morphology presents a complex and critical challenge in both theoretical and applied sciences. This paper presents a tool to effectively compute the intrinsic permeability k(x,t) for domains with both temporally and spatially varying pore structures, by utilizing a sample of snapshots of microfluidic experiments with calcium carbonate precipitation. To compute large domains at reasonable cost, a pseudo-3D Stokes solver with an additional viscous drag term is employed. In order to simulate the intricate domains with highly heterogeneous pore space, we derive vertically averaged governing equations including a vertically averaged drag term from scratch and without limiting assumptions. For exemplary precipitates (semi-spheres) of varying sizes within a rectangular channel with fixed solid boundaries, results are compared across multiple modeling approaches, each employing different drag force formulations. In addition, the performance of the derived models is examined for segments of an EICP experiment in terms of accuracy (comparison with 3D solvers) and computational efficiency. Finally, an entire experimental domain is used to demonstrate the capabilities offered by the solver thanks to its efficiency as well as accuracy and the permeability tensor k is computed and compared with experimental results. In all these applications, the vertically averaged drag term proves to be the optimal solution in terms of speed and accuracy, thus allowing for an efficient analysis of the non-uniformity of local fluid flow fluctuations resulting from alterations in the pore space.
Read the full abstract

All authors David Krach, Felix Weinhardt, Holger Class, Holger Steeb, Martin Schneider

Geophysics

Iterative separation of coherent blended signals in common shot gathers using synchrosqueezed curvelet-Radon constraints

Zifei Li, Shaohuan Zu and Haojun Chen

Blended data acquired via simultaneous-source seismic exploration conventionally require post-acquisition deblending, which typically relies on the coherence differences introduced by firing time delays (time dithering). To reduce the dependency of the deblending process on these time dithers, a novel joint constraint based on the synchrosqueezed transform and the Radon transform is proposed, operating directly in the common-shot gather (CSG) domain. Specifically, by exploiting the differences in propagation directions of the blended signals within CSGs, the synchrosqueezed transform is first employed for an initial iterative separation to extract individual sources directly from the continuous records. Once the majority of the valid signals are separated, the Radon transform is subsequently applied in further iterations to suppress the residual blending interference, thereby preventing amplitude damage to the effective signals. Compared to conventional non-CSG deblending methods, this approach bypasses the reliance on time-delay coherence differences, thus enabling real-time quality monitoring of individual sources during field acquisition. Furthermore, compared to other existing CSG-based separation techniques, the proposed joint-constraint iterative framework demonstrates superior performance when handling complex data. Applications on both synthetic and field blended datasets demonstrate that high-fidelity data separation can be successfully achieved independently of the time-dithering constraints. Finally, because the proposed method operates completely independently across different CSG slices, it is highly amenable to parallel computing, facilitating the efficient processing of massive datasets within a short timeframe.
Read the full abstract

Geophysics

A limit theorem linking continuous plate models to the topology of the plate boundary network

Seung-Sep Kim

Plate tectonics is described in two registers: a discrete one, in which rigid plates tile the sphere and meet at trivalent junctions, and a continuous one, in which Bercovici and Wessel (1994) replace hard plate outlines by smooth shape functions of finite boundary half-width $\delta^*$. We prove that the two registers are connected by a limit theorem. Generalizing the shape functions to a signed-geodesic-distance construction, we show that they converge almost everywhere, exponentially in $1/\delta^*$, to the plate indicator functions; that the sharp plate mosaic is a finite regular trivalent CW decomposition of the sphere under an explicit structural hypothesis; and that a component-counted weighted Euler characteristic $\chi_w^{\delta^*}(\tau)$, built from the super-level sets of the shape functions, equals $V-E+F=2$ for all $\delta^*$ below an explicit threshold $\delta_0(\tau)$, with its face, edge and vertex terms converging separately to the numbers of plates, boundary arcs and triple junctions. The theorem is proved unconditionally on the signed-distance offset cover under positive-reach, separation and bounded-sector hypotheses, and transferred to the normalized partition-of-unity field for thresholds $\tau<1/3$ by a comparison lemma whose junction-ball step, a planar single-crossing property, is certified numerically. The residual $|\chi_w-2|$ defines a topological diffuseness index for diffuse plate boundaries. For the present-day PB2002 network the hypotheses are measured to be non-vacuous, with $\delta_0(0.15)\approx2.8$ km. The theorem is the mathematical foundation of the topological audit of plate reconstructions presented in a companion paper (Kim, 2026, submitted to Geoscience Frontiers).
Read the full abstract

Tuesday 29 September

Geophysics · Cross-listed from physics.flu-dyn

Three-Dimensional Simulation of Flood Propagation at Syabrubesi, Nepal

Rainald Löhner and Harbir Antil

Three-dimensional flood propagation through the Bhote Koshi–Langtang Khola junction at Syabrubesi, Nepal, is studied using an incompressible free-surface model. The calculation is motivated by the August 2026 flood and examines the passage of an imposed inflow through the local bends and changes in channel width. The Navier–Stokes equations and an interface transport equation are solved on unstructured tetrahedral meshes constructed from pre-event terrain data. For an inlet speed of $10\,\mathrm{m\,s^{-1}}$, the solution indicates surface speeds above $40\,\mathrm{m\,s^{-1}}$. The advancing front spreads across the junction; as the flow develops, water deepens and slows around the bend. Higher speeds persist in the confined upstream and downstream regions.
Read the full abstract

Geophysics

Finite-Horizon Triggering Cores of Earthquake Sequences

Didier Sornette and Giuseppe Petrillo

Models of triggered seismicity commonly aggregate the contributions of all past earthquakes into a total predicted rate. This aggregation does not distinguish a forecast dominated by one recent earthquake from an equal-rate forecast supported by many events distributed across a longer history, even though these configurations can have different persistence and sensitivity to catalog and parameter uncertainties. We introduce the finite-horizon triggering core size \(N_{p,T}(t)\), defined as the minimum number of past earthquakes whose expected contributions account for a fraction \(p\) of the total triggering potential over the future interval \([t,t+T]\). The definition applies to any triggering model that assigns nonnegative event-specific expected contributions over a specified future window. We develop its theory for marked Hawkes and epidemic-type aftershock sequence processes, where the weights are the expected numbers of direct offspring produced by individual past earthquakes. The finite horizon makes the observable well defined for every normalized Omori kernel with exponent \(\theta>0\), including the long-memory regime in which the corresponding infinite-horizon quantity diverges. We derive continuum and marked-Poisson benchmarks, an exact representation of the marked-Poisson distribution, and high-\(p\) distributional laws. For finite-horizon Omori memory, the triggering core grows as \((1-p)^{-1/\theta}\) with a realization-dependent amplitude controlled by the total triggering weight; for exponential memory, the growth is logarithmic.[...]
Read the full abstract

Geophysics · Cross-listed from physics.chem-ph

Anomalous pressure-dependent viscosity of basaltic melts and its role in asthenosphere melt accumulation

Hongkun Zeng, Peiyu Zhang, Liang Yuan et al. (+3 more)

The asthenosphere's mechanical weakness enables plate tectonics, but its origin is debated. Partial melt has been proposed to cause this softening, yet recent studies suggest that the measured viscosity minimum in basaltic melts, an essential control on melt mobility, is an experimental artifact. Using quantum mechanics-based, machine learning-accelerated molecular dynamics, we extend simulation timescales by more than a factor of 1000 and achieve percent-level precision. We show that basaltic melt exhibits a robust viscosity minimum (approximately 20% below 1-bar values) at approximately 3 GPa, driven by pressure-induced reorganization of aluminum coordination that facilitates shear relaxation while silicon-oxygen polyhedra remain structurally rigid. Our results reveal a depth-dependent rheological transition: melt mobility peaks below approximately 150 km, promoting efficient extraction, but declines sharply during ascent, causing melt to stagnate beneath the lithosphere. This mechanism provides a physical basis for the dual seismic signatures of a melt-depleted deep asthenosphere and a melt-enriched layer near the lithosphere-asthenosphere boundary.
Read the full abstract

All authors Hongkun Zeng, Peiyu Zhang, Liang Yuan, Youjun Zhang, Xiang Wu, Junfeng Zhang

Monday 28 September

Geophysics · Cross-listed from cond-mat.soft

Efficient, Geometry-only Prediction of Advection-dominated Particle Transport through Saturated Soils

Hao Liu, Rizwan Khaleel, Markus Rolf et al. (+3 more)

The environmental transport of microplastic particles has raised concerns because of their potential ecological and human-health impacts. A particular challenge for experiments on microplastic transport in soils is the fact that soil is a highly complex and non-transparent porous medium. Thus, our knowledge of transport mechanisms of microplastics in soils and related ecological implications is still limited and computational simulations offer an additional possibility to obtain important insights into the physical transport behaviour of microplastics. Yet, direct particle-resolved simulations in realistic soil structures for macroscopic sample sizes remain a computational challenge. We introduce an efficient pore-network model, minD-PNM, that predicts particle breakthrough curves in water-saturated soils using experimental X-ray micro-computed tomography ($\mu$CT) pore geometries as the only input. The model combines an approximately $t^{-3}$ pore-scale transit time distribution with a minimum-dissipation partition of multi-inlet-multi-outlet pores, and propagates these distributions through the pore network to predict breakthrough curves. We benchmark minD-PNM against lattice Boltzmann and immersed boundary simulations for small systems, and against microplastic column experiments in quartz sediments for macroscopic sample sizes. In both cases, the predicted breakthrough curves agree well with the corresponding reference data. Because it requires only geometric information, minD-PNM scales to macroscopic sample volumes offering a practical tool for contaminant-hydrology studies of particulate pollutants.
Read the full abstract

All authors Hao Liu, Rizwan Khaleel, Markus Rolf, Martin G.J. Löder, Christina Bogner, Stephan Gekle

Geophysics

Does High Signal-to-Noise Ratio Identify Better Training Data for Seismic Deep Learning?

Ziye Yu, Xin Liu and Yuqi Cai

Hard signal-to-noise ratio (SNR) screening removes weak seismic records, but does retaining clearer waveforms improve learning enough to compensate for the discarded data? We test this question in phase picking and ambient-noise dispersion, first matching training counts and then restoring the full eligible dispersion pool at fixed compute. Phase-picking screening gave no consistent unfiltered-test benefit under fine-tuning or random initialization, including a control matched on distance and phase composition. Dispersion screening increased error at the original five-epoch endpoint but improved on an equally sized unscreened set after longer training. Retaining all 29,788 eligible fit paths instead of 9,929 randomly sampled paths reduced unfiltered-test error by 3.0% across three seeds at the same update budget. Full-available and strict-SNR training then had similar mean errors, 0.0405 and 0.0406 km s$^{-1}$, with mixed seed-paired ordering; strict training retained a 9.9% advantage on the SNR-selected test. In two-day monitoring, SNR regulated pick streams but event recovery depended on its measurement definition. High SNR did not reliably identify better training data: gains depended on the task, training schedule, and test domain, and fixed-count comparisons omitted the benefit of retaining additional valid examples.
Read the full abstract

Geophysics

Convergent representations of elastic-wave structure emerge across deliberately distinct seismic training routes

Ziye Yu, Yuqi Cai and Xin Liu

Elastic-wave propagation connects laboratory fracture with earthquakes, raising the prospect of seismic models that reuse waveform structure across physical scales. Here we show that the phase picker PNSN and the multi-task model SeismicXM organize unseen laboratory acoustic emissions around common waveform relations. Their representations agree on 512 held-out records relative to matched random networks. Four earthquake-model families also track displaced laboratory arrivals without fitting target neural weights. Receiver-function classification is accessible to frozen pretrained features, although standardized random features remain competitive. Noise correlations test the extension to continuous propagation outputs: a nonlinear SeismicXM ensemble improves on random topology, while a period-wise median remains more accurate. Waveform interventions and synthetic controls reveal that the observed agreement depends on feature calibration and the waveforms being compared. These results establish convergent waveform organization across distinct seismic training routes and show that cross-scale arrival transfer is already accessible to compact models.
Read the full abstract

Geophysics

From Wildfire Severity to Snow Persistence: A Multisource GeoAI Study of the 2020 Creek Fire

Parastoo Farajpoor and Mohammadreza Narimani

Accurate prediction of post-fire snow conditions does not necessarily establish how wildfire changed those conditions. We developed an explainable geospatial artificial intelligence framework combining multisource Earth observations, meteorological information, and matched before-after comparisons to examine seasonal snow persistence following the 2020 Creek Fire in California's Sierra Nevada. Harmonized Landsat Sentinel-2 observations were used to estimate the fraction of clear-sky observations containing snow during October-July for eleven water years, 2016-2026, on a 500 m analysis grid. We matched 3,778 snow-zone cells inside the fire perimeter to comparable unburned controls using terrain and pre-fire snow conditions; absolute standardized mean differences after matching were no greater than 0.047. HLS persistence agreed closely with MODIS, with a mean annual spatial correlation of 0.947. The landscape-average before-after control-impact contrast was +0.0017, with a year-level 95% confidence interval of -0.023 to +0.027. A stronger response emerged in the highest burn-severity class, where observed persistence increased by 0.026 relative to matched controls, equivalent to 2.6 percentage points. Under 5 km spatial cross-validation, XGBoost predicted raw post-fire persistence with an out-of-fold coefficient of determination of 0.811, whereas predictive skill for the fire-adjusted anomaly reached 0.046. Elevation and temperature together accounted for 65.7% of mean absolute model attribution. These findings distinguish a severity-associated optical snow response from the terrain-climate relationships that dominate predictive skill. Combining matched comparisons with explainable GeoAI provides a practical framework for forest monitoring that separates accurate environmental mapping from inference about disturbance effects.
Read the full abstract

Geophysics · Cross-listed from cs.LG

Physical Cross-Modal Masked Autoencoding for Seismic-to-Well Representation Learning

Meher Gajula, Keyla Gonzalez, Ben Lasscock et al. (+1 more)

Learning from scientific measurements often requires aligning modalities with different spatial support and resolution. Subsurface characterization is an extreme dense-sparse problem in which 3D seismic provides volumetric but indirect measurements and well logs provide high-resolution 1D measurements at sparse borehole locations. We introduce a physically grounded cross-modal masked autoencoder (CM-MAE) for seismic-to-well representation learning. The model jointly tokenizes seismic volumes and well-log depth patches, embeds both modalities in continuous physical coordinates using four-axis rotary position embeddings, and reconstructs masked targets with a cross-modal decoder. Sparse Mixture-of-Experts layers provide modality-specific capacity while dense attention allows information exchange between modalities. Pretraining uses 23 seismic volumes covering approximately 178,000 square km across U.S. offshore and onshore basins, together with approximately 92,000 wells. Matched-mask ablations show strongly asymmetric information flow. Seismic context improves held-out well-log reconstruction by 9.73%, while well-log context improves seismic reconstruction by 0.65%. We evaluate seismic-only pseudo-log predictions against independent interpreter-drawn salt-geobody masks to determine whether they contain geologic signal. Across offshore U.S. surveys, compressional-slowness-derived salt scores reach an AUROC of 0.910. In onshore basins, predicted compressional slowness preserves formation-scale structure and partial relative organization in an unseen survey despite calibration drift. CM-MAE learns useful seismic-to-well representations under extreme modality asymmetry, although absolute pseudo-log calibration remains survey-dependent.
Read the full abstract

All authors Meher Gajula, Keyla Gonzalez, Ben Lasscock, Alejandro Valenciano