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Physics Physics Geophysics

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Wednesday 9 September

Geophysics

Holistic law of aftershocks

A.V. Guglielmi

The paper is devoted to the phenomenological theory of aftershocks occurring in the source of a tectonic earthquake following the main shock. The theory was developed by the author jointly with A.D. Zavyalov and O.D. Zotov during the course of a long-term study of aftershocks. The theory is based on the concepts of source deactivation and the source's proper time. The holistic law governing the decay of aftershock activity over proper time follows from the theory. The damping decrement is equal to the source deactivation coefficient. The main focus of this paper is the analysis of the logical structure of the theory. The paper also contains a brief description of the experimental results obtained using the theory. Keywords: earthquake source, aftershocks, Omori's law, Utsu's law, deactivation coefficient, proper time, underground clock, foreshock convergence, aftershock divergence.
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Tuesday 8 September

Geophysics

Large-scale bioacoustic detection using semantic segmentation: a deep learning framework applied to fin whale calls in ocean-bottom seismometer recordings

Jocelyn Japnanto, Alex A. Saoulis, Miriam Romagosa et al. (+5 more)

Ocean-bottom seismometers (OBS), originally deployed for geophysical research, continuously record low-frequency sound for months to years across broad areas of ocean, offering a largely untapped resource for passive acoustic monitoring (PAM) of baleen whales. Realising this potential requires automated detection methods that operate reliably across the varied conditions in large sensor networks. We present a deep learning semantic segmentation framework that detects the 20-Hz notes of fin whales (Balaenoptera physalus) in OBS spectrograms, assigning each pixel a probability of belonging to a call and converting the resulting probability maps into time-frequency bounding boxes describing individual detections. We trained the model on hydrophone data from one OBS deployment in the Azores-Madeira-Canaries region and applied it without retraining to vertical-component seismometer data from a second, geographically distinct deployment, showing that a single trained model generalises across sensor types and recording environments. Applied to 378,912 h of recordings from 46 OBS sites, the detector identified 6.3 million calls, forming the largest fin whale call catalogue assembled to date, with high precision (~97%) across both deployments. The resulting catalogue resolves call timing and spectral structure accurately enough to support ecological analyses, revealing coherent seasonal shifts in three persistent inter-note interval (INI) groups across the singing season and basin-scale patterns in calling activity. By transforming existing geophysical infrastructure into a scalable sensing network, our approach substantially expands the spatial and temporal reach of PAM without new hardware investment, offering a transferable framework for tracking other low-frequency vocalising species and informing conservation planning, marine spatial management, and abundance estimation across large scales.
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All authors Jocelyn Japnanto, Alex A. Saoulis, Miriam Romagosa, Rita Leitão, Gabrielle Arrieta, Mónica A. Silva, Matthew Graham, Ana M. G. Ferreira

Geophysics

PyelogP: Automated Energy-Based Determination of Preconsolidation Pressure in Clay Deposits

Liang Chern Chow and Thierno Kane

Estimating the preconsolidation pressure ($\sigma'_p$) from one-dimensional consolidation (oedometer) tests is critical in geotechnical engineering for settlement analysis. Traditional graphical methods, such as the Casagrande procedure, may introduce uncertainties, particularly when interpreting rounded $e$-log($P$) curves typical of disturbed specimens of soft clays and silt deposits. This paper introduces PyelogP, an open-source Python library designed to calculate $\sigma'_p$ using the strain-energy method proposed by Becker et al. (1987) as an automated and reproducible alternative. The algorithm combines natural cubic spline interpolation, knee-point detection via the Kneedle algorithm, and split-point linear regression within the work-pressure space. Physically informed thresholds, including overconsolidation ratio limits and second-derivative maxima (${d^2 e}/{d(\log \sigma')^2}$), are incorporated to establish pre-yield and post-yield fitting boundaries. The performance of PyelogP is evaluated against a suite of 22 experimental consolidation datasets covering various clay deposits, including Saint-Alban clay and San Francisco Old Bay Clay. The results demonstrate strong agreement with the published values ($R^2$ = 0.912, RMSE = 0.374, MBE = -0.080), while the $O(N^2)$ algorithm requires only a few milliseconds per curve for typical oedometer datasets and less than 150 milliseconds for the largest datasets.
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Geophysics

Bayesian deep learning integration of geophysical and drilling data for 3D prediction of copper mineralization and drill targeting: a case study from the Kogodai prospect, Rudny Altai

Margarita Veshchezerova, Egor Barashov, Evgenii Gusev et al. (+4 more)

Exploration drill targeting in structurally complex terranes is hindered by sparse sampling, heterogeneous datasets, and the ambiguity of geophysical inversions. Here, we present an uncertainty-aware 3D workflow for the acceleration of time-to-discovery in brownfield explorations and apply it to the Kogodai prospect in the Rudny Altai metallogenic province. We jointly analyse existing drilling and geophysical data in a comprehensive approach, revealing hidden patterns in already available data. Drillholes and trenches were desurveyed to a common 3D reference frame, and assays were composited to a consistent spatial support to facilitate joint modelling with geophysical inputs. We develop Bayesian deep-learning models to predict 3D fields of Cu grade together with chargeability and apparent resistivity while quantifying epistemic uncertainty via Monte Carlo sampling. The original contribution of this work is to treat the problem not as pointwise regression between co-located observations, but as joint learning of spatially continuous 3D fields from sparse, heterogeneous exploration evidence. The resulting 3D predictions delineate a principal mineralized trend and several localized candidate zones that coincide with elevated induced polarization (IP) responses, while uncertainty mapping highlights where predictions are robust versus where additional drilling would be most informative. The continuous Cu-grade field can also be thresholded to produce binary prospectivity maps, allowing the sensitivity of target delineation to the chosen cutoff to be evaluated. The outputs are intended for qualitative interpretation and risk-aware drill targeting rather than resource estimation, and we discuss key limitations arising from incomplete provenance metadata for geophysical products and heterogeneity of historical sampling.
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All authors Margarita Veshchezerova, Egor Barashov, Evgenii Gusev, Michael R. Perelshtein, Arlan Kasymzhan, Bolat M. Kabaziev, Nurlan Y. Askarov

Monday 7 September

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

Topographic Disorder, Wind Coupling, and Directional Fire Spread: Critical Behavior in a Terrain-Weighted Forest Fire Model

Juan M. Florez, Eric Suárez Morell, Cristian Millan et al. (+1 more)

We introduce the Terrain-Weighted Forest Fire Model (TFFM), a lattice model in which fire spreads on a spatially correlated Gaussian height field with the asymmetric bond probability $p_{i\to j}=\mathrm{clip}[e^{-\beta+\gamma(h_j-h_i)},0,1]$, plus an additive wind bias. Simulations on lattices up to $L=8192$ reveal a sharp active-to-inactive transition whose critical suppression threshold $\beta_c$ is even in $\gamma$, decreases with $|\gamma|$, and decreases as the terrain correlation length $\sigma_h$ is reduced: slope asymmetry acts as a suppressant because downhill bonds are penalized and fire stalls at local elevation maxima. For rough terrain and low tree density the fire fails to percolate even at zero suppression. Finite-size scaling on a fine $\beta$ grid at $L=2048$–$8192$ gives a correlation-length exponent $\nu=1.8\pm0.17$ from both the susceptibility peak and the width of the transition, and a front-velocity exponent $\delta=0.34\pm0.03$, identical for smooth and rough terrain; neither matches directed percolation ($\nu_\perp=0.73$, $\nu_\parallel-\nu_\perp\approx0.56$) or isotropic percolation ($\nu=4/3$, $\approx0.18$). The single-seed survival probability at $\beta_c$ is independent of $L$ and decays extremely slowly, with a running exponent falling from $\approx0.09$ to $\approx0.04$, excluding directed percolation and pointing to a survival probability that remains finite at criticality, consistent with the $L$-independent value $P^*\approx0.5$ at which $P_{\rm surv}$ drops to zero. Wind raises $\beta_c$ by a factor of $2$–$4$, produces a sharp onset of downwind fire-scar drift at weak coupling, and, at high terrain coupling, decreases the burned fraction at boundary crossing—a terrain-wind competition effect absent from isotropic bond-disorder models. The model yields fire-risk thresholds and fire-scar signatures comparable to satellite burn-scar data.
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All authors Juan M. Florez, Eric Suárez Morell, Cristian Millan, J. Restrepo

Geophysics

SeisBench DAS: A machine learning framework for Distributed Acoustic Sensing

Jannes Münchmeyer, Han Xiao and Frederik Tilmann

Fibre optic sensing, such as distributed acoustic sensing (DAS), has become a widespread technology for geophysical studies. To process the large-scale datasets produced by DAS, several machine learning methods have been proposed. However, without standardization of data and models, these methods lack comparability and interoperability. This introduces a gap between model developers and practitioners analyzing DAS data and inhibits adoption of deep learning for DAS. To address these limitations, here we present SeisBench DAS, an extension to the SeisBench library for machine learning in seismology. SeisBench DAS defines standard formats for DAS benchmark datasets, including standardised metadata and labels, and DAS models. It builds on the xdas framework for data ingestion and virtual array handling, and on PyTorch for reading and applying the machine learning models. Importantly, SeisBench provides an engine to efficiently apply deep learning models to diverse formats of DAS data, bridging the gap between model developers and practitioners. SeisBench DAS is designed as an open and extensible framework, allowing to easily incorporate future developments in deep learning for DAS.
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Geophysics

ANADEF: A Nested-Permutation Alarm for Dual-Parameter Earthquake Forecasting

Hamzeh Mohammadigheymasi, Muhammed Hossein Mousavi and Nuno Pombo

Spatially resolved stress proxies and rate-based seismicity models are increasingly combined for regional earthquake forecasting, yet formally testing their non-redundancy remains largely unaddressed. We present the Nested-Permutation Alarm for Dual-Parameter Earthquake Forecasting (ANADEF) pipeline, integrating a stress-sensitive Gutenberg–Richter $b$-value field, estimated via a penalized 2D B-spline inversion, with a stationary background rate ($\mu$) from space–time ETAS stochastic declustering. Applied to the Zagros Fold–Thrust Belt using an 18-year catalog ($n=40{,}731$, $M_{\mathrm{N}}\geq1.5$, 2006–2024) under a two-stage protocol with non-overlapping training (2006–2014) and target (2015–2024) windows, the model achieves, for $M_w\geq5.0$ ($N=55$), a retrospective Area Skill Score $S=0.69$ (95% CI: 0.64–0.73), reducing alarmed area from $\tau\approx0.38$ ($\mu$-only) to $\tau\approx0.28$ while retaining hit rate $\nu=92.7\%$; since thresholds are calibrated on the evaluation catalog, these are in-sample, not out-of-sample, estimates. A nested permutation procedure re-optimizing thresholds within each null realization tests whether $b$-value adds information beyond $\mu$, absorbing the optimization bias. Significance is null-model dependent: cell-wise randomization yields $p=0.012$, while a conservative spatial-structure-preserving null gives weaker, non-significant evidence ($p=0.057$)—incremental stress information is suggestive but not unambiguously established. Calibrated thresholds were frozen and applied to updated 2015–2024 fields, generating an unvalidated, forward-looking spatial alarm template for 2025–2029. These results establish a reproducible, statistically transparent methodology for testing, rather than assuming, complementarity between stress-sensitive and rate-based predictors, offering a candidate operational template pending prospective validation.
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Geophysics

The 2016 Mw 7.0 Kumamoto Earthquake Sequence, Japan revisited: Insights from Spatio-Temporal Analysis of Seismicity Parameters

Hamzeh Mohammadigheymasi, Muhammed Hossein Mousavi, Marjan Tourani et al. (+1 more)

Understanding how crustal faults accumulate strain, nucleate ruptures, and redistribute post-seismic stress is fundamental to seismic hazard assessment. The 16 April 2016 $M_w$ 7.0 Kumamoto earthquake, rupturing the Futagawa–Hinagu fault zone in the Beppu–Shimabara graben, central Kyushu, Japan, offers a premier dataset for tracking these processes across a full earthquake cycle. Using the JMA catalog (2014–2018) with dynamic completeness estimation ($M_c \approx 2.15$–$2.25$), we examine the Gutenberg–Richter $b$-value, the 3-D hypocentral fractal dimension ($D_c$), and the seismicity-rate anomaly ($Z$-value). During the one- to two-year preparatory phase, $b$ declined from a baseline of $1.35 \pm 0.10$ to a precursory minimum of $0.59$, while $D_c$ contracted from $0.85 \pm 0.15$ to $0.70$–$0.80$, recording stress concentration and microfracture coalescence onto a narrow nucleation zone. A coherent negative $Z$-value anomaly (-1.6 to -2.0) developed along the graben, strengthening with integration time—consistent with, though not proof of, progressive fault locking. Depth-sliced volumes show the low-$b$ locked core ($b \le 0.65$) was stratified at $10$–$12.5$~km depth and sharpened within the final four months before failure. Rupture reversed this within weeks: $b$ surged to $1.25$–$1.35$ and $D_c$ expanded to $2.04$–$2.11$, consistent with coseismic stress drop and aftershock activation, followed by recovery over 2.5 years. Cross-sections reveal sharp aftershock localization alongside two unrelaxed asperities ($b \approx 0.80$–$0.95$) near the Hinagu termination and Mount Aso, consistent with positive Coulomb stress loading. This framework resolves asperity locking, release, and healing better than any single metric; however, since anomalies were identified retrospectively, they should be read as evidence of coherent behavior rather than a validated forecast tool alone.
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All authors Hamzeh Mohammadigheymasi, Muhammed Hossein Mousavi, Marjan Tourani, Nasrin Tavakolizadeh

Sunday 6 September

Geophysics

Hourly U.S.-wide flood simulation beyond the limits of traditional and data-driven models

Wencong Yang, Leo Lonzarich, Yalan Song et al. (+4 more)

As increasingly-damaging floods can strike within hours of a storm and in ungauged reaches, hourly network-wide simulation has become critical societal infrastructure. Here we demonstrate a multi-timescale physics-embedded learning model which outperforms the United States' operational system and surpasses AI-based systems at flood peaks. Covering more than 800,000 river reaches of the conterminous U.S., the model elevates median hourly Nash-Sutcliffe efficiency at 2,831 gauges to 0.683 from 0.461 for the operational National Water Model v3.0, and narrows flood-peak timing errors from 7-8 hours to 4.5-6 hours. dHBV2.0MTS-MC captures 33% more >=50-year floods than NWM3.0 and 159% more than an operational LSTM baseline. Against recent AI models, overall hourly skill is comparable while rare-flood accuracy is distinctly higher, with relative peak-magnitude error reduced by 34% for >=100-year floods. It combines long-term hydrologic context, short-term shocks, and infiltration excess to resolve extraordinary hourly peaks not visible on a daily plot. Process-model parameters and hourly discharge are produced for every reach, seamlessly covering the continent at 7.2 km2 median resolution. This candidate for the next-generation National Water Model sets a new operational accuracy level for national-scale flood prediction.
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All authors Wencong Yang, Leo Lonzarich, Yalan Song, Haoyu Ji, Ming Pan, Kathryn Lawson, Chaopeng Shen

Saturday 5 September

Geophysics · Cross-listed from physics.flu-dyn

Maintenance of a columnar vortex by inertial waves in rotational turbulence

Nikolay A. Ivchenko, Sergey S. Vergeles, Daniil D. Tumachev et al. (+1 more)

We develop a theory that determines the radial profile of the mean velocity in a coherent geostrophic vortex forming in a turbulent rapidly rotating fluid. Following the conditions of our experiments, we assume that the flow in the vortex is sustained by the absorption of short-wavelength inertial waves arriving from the vortex periphery. The nonlinear interaction between waves is assumed negligible in the theory. Comparison with experimental data supports the validity of the developed model.
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All authors Nikolay A. Ivchenko, Sergey S. Vergeles, Daniil D. Tumachev, Sergey V. Filatov

Geophysics

Conservation of Normalized Energy Density in multi-layered media: a proof for the general multi-layer 2D SH problem

Hiroyuki Goto

The Normalized Energy Density (NED) was introduced in Goto et al.(2011) as a quantity that, unlike the conventional energy, is conserved across material interfaces in a layered elastic medium. Its conservation was proved analytically only for a two-layer structure; for three or more layers it was supported by Monte Carlo simulation. I supply the missing proof for a general multi-layer (n-layer) structure in the 2D SH problem. The key step is to stop treating the angular frequency as a single variable and instead lift the layer phases to independent coordinates on the n-torus. The obstruction identified in the original paper—that the ratio of travel-time combinations is generically irrational, so that the periodicity argument used for two layers fails and disappears, and the proof reduces to Weyl's equidistribution theorem combined with n repetitions of the same elementary integral used in the two-layer case. I prove conservation in two independent settings that together cover all cases of practical interest: travel times that are linearly independent over Q, and commensurable travel times, including the equal-travel-time (Goupillaud) discretization. I also show that an arbitrary layered structure can be subdivided by reflectionless interfaces into one whose travel times take only d distinct, Q-independent values, where d is the dimension of the Q-span of the original travel times; this places the two proved cases at the endpoints d=1 and d=n and isolates the remaining open case 1<d<n in a canonical form.
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Geophysics

Seismic P-wave attenuation estimation based on frequency-dependent AVO using Kramers-Kronig relations for gas reservoir prediction

Shengyi Wang, Xuehua Chen, Xingyu Luo et al. (+1 more)

Estimation of seismic attenuation (inverse quality factor) is important for gas reservoir prediction. Two key issues in seismic attenuation estimation are the development of a physically consistent reflection coefficient equation and stable estimation of seismic attenuation from seismic data. To address these issues, this study, within the framework of isotropic linear viscoelastic media, starts from the Kramers-Kronig relations and expresses the viscoelastic stiffness matrix as a function of seismic attenuation. Under the assumptions of weak attenuation and small elastic and attenuation contrasts across the interface, a frequency-dependent PP-wave reflection coefficient equation explicitly containing seismic attenuation terms is derived using scattering theory. The derived reflection coefficient equation not only satisfies the causality constraint, but also preserves a compact mathematical form. Based on this reflection coefficient equation, seismic attenuation is estimated within the framework of frequency-dependent AVO inversion. Synthetic seismic data tests show that the estimated P-wave attenuation attribute is sensitive to variations in reservoir gas saturation, with reservoirs of higher gas saturation exhibiting stronger P-wave attenuation anomalies. Application to field seismic data further demonstrates that the P-wave attenuation anomalies agree well with the gas saturation log and effectively identify high gas saturation reservoirs. This study provides a new approach for extracting P-wave attenuation information from seismic data and achieving high resolution prediction of gas reservoirs.
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All authors Shengyi Wang, Xuehua Chen, Xingyu Luo, Kaixing Huang

Friday 4 September

Geophysics

The tetragonal-cubic transition of davemaoite: Implications for lower mantle seismic anomalies

Yoshiyuki Okuda, Bin Chen, Juliana Peckenpaugh et al. (+5 more)

Davemaoite (CaSiO3 perovskite) is the third most abundant mineral in Earth's lower mantle and a dominant phase in subducted oceanic crust. Its crystal structure is distorted (tetragonal or orthorhombic) at ambient conditions but is considered to transform to cubic at high temperatures. Previous experiments reported low, nearly pressure-independent transition temperatures (approximately 600 K), demonstrating a long-standing discrepancy with theoretical predictions that mostly exceed 1000 K. Here, we determine the phase stability and thermal equation of state of CaSiO3 davemaoite and its titanium-bearing solid solution [Ca(Si0.75,Ti0.25)O3] under simultaneous high-pressure and high-temperature conditions using a laser-heated diamond anvil cell combined with synchrotron X-ray diffraction. We find that the tetragonal-to-cubic transition occurs at substantially higher temperatures than previously reported, with the titanium substitution further stabilizing the tetragonal phase and shifting the transition boundary to even higher temperatures. These findings indicate that CaSiO3 davemaoite in subducted oceanic crust likely undergoes its ferroelastic transition in the mid-lower mantle, whereas Ti-rich davemaoite may remain tetragonal throughout most of the lower mantle, transforming to cubic near the core-mantle boundary. Our results demonstrate that the compositionally dependent phase behaviour of davemaoite can account for the seismic anomalies observed in both the mid-lower mantle and the lowermost mantle.
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All authors Yoshiyuki Okuda, Bin Chen, Juliana Peckenpaugh, Keng-Hsien Chao, Saori Kawaguchi-Imada, Hirokazu Kadobayashi, Zhenxian Liu, Dongzhou Zhang

Geophysics

Bayesian inversion of multilayer $\mathrm{CO}_2$ migration from seismic plume observations using a graph-based finite-rate invasion-percolation model

Elling Svee, Jo Eidsvik, Kasper Hunnestad et al. (+1 more)

Vertical migration of $\mathrm{CO}_2$ in layered sandstone reservoirs is controlled by thin shale barriers whose properties are often poorly known. We develop a Bayesian framework that uses time-lapse seismic plume observations to estimate effective parameters governing lateral and vertical $\mathrm{CO}_2$ migration. The forward model is a fast graph-based invasion-percolation model that extends conventional IP by representing both capillary-controlled filling of structural traps beneath shale barriers and finite-rate transfer through them. Parameters are inferred with approximate Bayesian computation and sequential Monte Carlo sampling, and the posterior samples are propagated into forecasts. Applied to real data from Sleipner, posterior simulations reproduce the broad distribution of $\mathrm{CO}_2$ across nine sand units and its redistribution between 2010 and 2023. In contrast, the quasi-static model fails to reproduce this temporal evolution. Complementary synthetic experiments assess parameter recovery, forecasting, monitoring duration, and model misspecification. These experiments show that the information gained from monitoring depends on the migration events captured, with breakthrough and post-breach redistribution providing particularly strong constraints. This combination of fast simulation, probabilistic updating, and interpretable effective parameters makes the framework well suited to repeated forecast revision during active injection, especially when full-physics inference is too computationally demanding.
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All authors Elling Svee, Jo Eidsvik, Kasper Hunnestad, Philip Ringrose

Thursday 3 September

Geophysics

Estimating subsurface water mass changes with ground-based gravimetry

Isabelle Schmidt, Jackson Ang'ong'a, Peter Bauer-Gottwein et al. (+10 more)

Gravitational force is proportional to the mass of an attracting body; therefore, changes in subsurface mass can be detected using gravimetry. In the near subsurface, mass variations are primarily driven by changes in water storage, meaning that gravity measurements provide direct information on variations in water mass. Traditionally, groundwater and soil water dynamics are monitored using point-scale instruments that require direct installation into the subsurface. In contrast, gravimetry is a non-invasive method that integrates signals over a larger spatial extent. Recent advancements in gravimeter technology have significantly improved measurement accuracy and long-term stability under field conditions, thereby expanding their potential for hydrological applications. This review summarizes recent developments in gravimetric instrumentation and provides essential background on gravimeters used for ground-based measurements and their application in hydrology, aiming to facilitate their broader use. In addition, it presents an overview of studies in hydrology and hydrogeology that have successfully applied gravimetry to quantify subsurface water storage changes, estimate hydrological parameters, and constrain numerical models. A perspective on future advancement in gravimetry and resulting potential applications is given.
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All authors Isabelle Schmidt, Jackson Ang'ong'a, Peter Bauer-Gottwein, Stephan Costabel, Christian Freier, Bastian Leykauf, Tina Lundgren, Miguel Martinez-Dorantes, Achim Peters, Ernst Rasel, Enrico Vogt, Gregor Willkommen, Vladimir Schkolnik

Geophysics · Cross-listed from physics.ao-ph

When a high-mountain slope failure cascades downstream: physical footprint and evolving exposure of the 2026 Gyirong mixed rock-ice cascade

Rui Li

The 26 August 2026 Gyirong cascade connected a high-mountain failure to developed downstream valleys. Event-bracketing observations delineate a preferred changed/source surface of 1.01 km^2, within alternative interpreted envelopes of 0.49-1.84 km^2, and a representative 21.8 km source-to-port route descending 3.40 km. Across two precipitation products, five antecedent windows and six fixed spatial supports, all 60 matched-year ranks remain below the 90th-percentile wet threshold. The source-nearest 7-day temperature mean of 9.43 degrees C exceeds all 25 matched years from 2001-2025. Positive temperature anomalies extend to every tested support, but ranks vary from the 88th percentile to above all historical values. Within the subsequently affected 37.354 km^2 UNOSAT footprint, modelled built-up surface increases from 0.036 to 0.441 km^2 between 1975 and 2020; its fraction of the fixed area rises from 0.096% to 1.181%. Delivered rapid maps contain 695 building points and 15.671 km of roads graded destroyed. These measurements connect the physical footprint with environmental context and historical exposure development. They complement existing process reconstruction while leaving the initiation mechanism and the mechanical role of warming unresolved.
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Geophysics

Low electrical conductivity of dry CaSiO3 perovskite under lower mantle conditions

Yoshiyuki Okuda, Bin Chen, Juliana Peckenpaugh et al. (+1 more)

Electrical conductivity (EC) provides important constraints on the composition and volatile distribution of Earth's deep mantle, yet the EC of davemaoite (CaSiO3 perovskite), a major lower-mantle phase, remains poorly constrained. We measured the EC of nominally dry CaSiO3 perovskite at pressures up to 89 GPa and temperatures up to 2200 K using impedance spectroscopy in a laser-heated diamond anvil cell. Conductivity increases with temperature but decreases systematically with pressure and it is substantially lower than previously reported, yet broadly consistent with recent theoretical predictions for oxygen-vacancy-mediated ionic transport. A distinct change in the temperature dependence coincides with the tetragonal-to-cubic phase boundary, revealing a modest enhancement of ionic transport across the structural transition. Along a normal lower-mantle geotherm, dry davemaoite is comparable in conductivity to bridgmanite near the top of the lower mantle but becomes progressively less conductive with depth. Under cold-slab conditions, dry davemaoite is substantially less conductive than dry subducted MORB and cannot account for the observed high-conductivity anomalies. Dry davemaoite therefore contributes little to bulk lower-mantle conductivity.
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All authors Yoshiyuki Okuda, Bin Chen, Juliana Peckenpaugh, Hirokazu Kadobayashi

Geophysics · Cross-listed from physics.flu-dyn

Two-phase Temperature Reconstruction in Ice-Water Systems

Zhukun Wang, Daisuke Noto, Douglas J. Jerolmack et al. (+1 more)

What controls heat transport when liquid water interacts with ice? Answering this question is essential for understanding water bodies undergoing phase change. Yet experimental progress remains limited by the lack of a minimally invasive methodology for simultaneously resolving temperature fields in coupled liquid water and non-isothermal ice systems. Here, we introduce a physics-based data-assimilation method for reconstructing temperature fields in buoyancy-driven flows interacting with non-isothermal ice. Particle tracking velocimetry provides the liquid velocity field, while thermal and kinematic boundary conditions constrain the inverse problem. The method couples advection–diffusion in liquid water with conduction in ice to reconstruct simultaneous mean temperature fields and quantify heat transport across the water–ice interface, while remaining minimally invasive and compatible with free-surface systems. We demonstrate the method in laboratory experiments in which the temperature range across the liquid water in contact with ice drives cabbeling-induced convection. This framework enables investigation of coupled thermo-fluid dynamics in cryospheric aquatic systems, including heat exchange at the ice-water interface and liquid-phase energetics, with broader applications to phase-change processes in food and energy industries.
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All authors Zhukun Wang, Daisuke Noto, Douglas J. Jerolmack, Hugo N. Ulloa

Geophysics · Cross-listed from physics.ao-ph

Cold Extremes during Dansgaard-Oeschger Oscillations

Ignacio del Amo and Peter Ditlevsen

This paper studies the statistics of extreme cold air surface temperatures in a climate that experiences abrupt changes. We employ a CCSM4 simulation of Last Glacial Maximum conditions that exhibits rapid switching between stadial and interstadial states as data. Non-stationary linear Generalized Extreme Value (GEV) distributions are fitted to find the regions that show the most prominent changes and associate them with physical processes. The results are then compared with a non-linear model, which gives a more detailed picture of how the parameters change as a function of the AMOC strength. While different regions of the world show different extremal behaviours, many regions show an approximately linear relationship between the parameters of the GEV distributions and the strength of the AMOC within the stadial and interstadial states, with some non-linear oscillation or jump where the transition between them takes place. Physical processes such as the expansion and retreat of the sea ice and the relative changes in the strength of the currents are shown to impact the magnitude and variability of the extremes, with significant changes observed in the three parameters of the GEV. They also create teleconnections that are compared whenever possible with various proxies for the temperature of the air and the water. We show how mapping the parameters of the GEV distributions into the AMOC strength gives a way to compare results between different climate models and different climate states. Comparisons, however, need to pay heed to the dynamical characteristics of the state and the location to be meaningful.
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Geophysics

PSDWII: Physical-Structure-Driven Waveform Inversion Imaging

Shengchang Chen

This paper proposes a Physical-Structure-Driven Waveform Inversion Imaging framework (PSDWII), grounded in the recognition that surface-recorded seismic data are directly related to subsurface virtual sources rather than to the model parameters themselves. Based on this recognition, we decompose seismic wave propagation into a three-step physical process: source excitation and incident wave propagation; interaction of the incident wave with heterogeneities, which generates virtual sources with distinct radiation patterns and excites secondary waves; and propagation and reception of the secondary waves. The adjoint of the propagation operator (reverse-time extrapolation) is then used to formulate a linear inversion projection for the virtual sources. By distinguishing different virtual-source expressions and their underlying physical mechanisms, we establish a unified mathematical representation for three inversion tasks: Full Waveform Inversion(FWI), stratigraphic physical properties imaging, and stratigraphic structures imaging. Deconvolution and linear inversion are respectively adopted to achieve multi-parameter decoupling in FWI and in angle-domain common-image gather inversion for stratigraphic physical properties imaging. The PSDWII framework shifts the conventional “mathematical optimization driven” paradigm of waveform inversion to a “physical-structure-driven” one. It neither constructs objective functions nor computes their gradients or Hessian inverses. Within this framework, we develop three specific methods: physical-structure-driven FWI (PSDFWI), physical-structure-driven stratigraphic physical properties imaging with angle-domain common-image gather inversion (PSDSI), and physical-structure-driven stratigraphic structures imaging (PSDMig).
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Tuesday 1 September

Geophysics

TREMORS: An Agentic Assistant for Multi-Datacenter Seismic Data Acquisition

Ryley G. Hill, Richard Alfaro-Diaz, Jonathan MacCarthy et al. (+2 more)

Seismology increasingly depends upon large data retrieval across multi-datacenter platforms. Yet, data procurement often demands domain expertise, user burden, and is difficult to reproduce. As archives continue to grow, translating scientific intent into structured workflows that operate across multiple repositories and produce high quality, AI ready data is becoming an increasingly urgent challenge. We present TREMORS (Text Referenced Event Mapping and Output Renderer for Seismographs), an agentic framework that uses large language model reasoning within a constrained execution graph to automate seismic data retrieval. TREMORS translates natural language queries into a structured intermediate schema, which drives execution through a constrained LangGraph workflow. Example workflows demonstrate support for both event-based and continuous waveform acquisition. The framework is designed to extend across heterogeneous multi-datacenter systems through a portable schema and modular workflow components. This work positions agentic workflows as the catalyst that will connect scientific intent with distributed seismic data systems, enabling a future of reproducible, data-driven inquiry while reducing the burden of routine acquisition tasks.
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All authors Ryley G. Hill, Richard Alfaro-Diaz, Jonathan MacCarthy, Jonas A. Kintner, Christopher W. Johnson

Geophysics

A Frequency-Aware Dynamic Knowledge Distillation Framework: An Effective Tool for Bridging Low- and High-Frequency Seismic Information

Jun Ma, Xinyang Wang and Xintong Dong

Seismic data contain rich information across different frequency bands, with low-frequency components primarily characterizing large-scale geological structures and high-frequency components preserving fine-scale seismic details. Effectively integrating these frequency-dependent components is essential for seismic feature learning to better preserve structural continuity and fine-scale details. Knowledge distillation provides an effective means for transferring informative representations from high-quality data. However, existing distillation-based frameworks usually treat seismic features in a full-band manner, ignoring relationships across frequency bands and thereby limiting the coordinated transfer of low- and high-frequency knowledge. To bridge low- and high-frequency seismic features through knowledge distillation, we propose a frequency-aware dynamic knowledge distillation framework (FADKD-Net), which establishes a teacher-student learning framework and performs frequency-aware knowledge transfer between low- and high-frequency bands. Specifically, FADKD-Net decomposes seismic features into low- and high-frequency components and performs targeted distillation to exploit their complementary information. Low-frequency distillation guides the student model to learn stable structural priors, thereby improving the overall continuity of seismic events. Meanwhile, high-frequency distillation enhances detailed feature modeling and improves the representational capability for complex and small-scale structures. Furthermore, a cross-domain feature alignment strategy is proposed to reduce distributional discrepancies across different surveys and enhance the transferability of the seismic representations learned by FADKD-Net.
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Monday 31 August

Geophysics

Is Seismic Forecasting Possible with Physics-based AI?

Victoria Keane, Manolis Veveakis and Thomas Poulet

Slow slip events within subduction zones offer a unique window into earthquake prediction. The subducting plate drives dehydration reactions in the fault, causing cyclical slip and observable surface displacements. Earthquake footprints can then be identified in these displacement series through coupling the multi-physics governing the subduction process with regional seismic activity. However, data noise and traditional filtering methods obscure the underlying mechanisms. Here, we alleviate this constraint with our physics-based attractor. By accounting for the physics of subduction paired with AI-assisted manifold detection, we are able to predict an earthquake in New Zealand's Hikurangi trench one week early. Additionally, predictability limits extend to 5-6 weeks with decadal repeatability, pointing to the fundamental determinism of the suggested mechanism through which physics-based seismic forecasting is possible.
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Geophysics · Cross-listed from physics.ao-ph

Ice-thickness based scaling of wave attenuation in sea ice: Application and assessment of wave spectra

W. Erick Rogers, Jie Yu, Jean Rabault et al. (+2 more)

This study discusses recent advances in modeling waves in sea ice in the U.S. Navy's regional modeling system. It is applied in the marginal seas of the eastern Arctic Ocean, including the Barents Sea, Kara Sea, parts of the Greenland Sea, Norwegian Sea, and waters north of Svalbard. The focus is to assess the skills of two formulations of wave attenuation by sea ice used operationally in WAVEWATCH III. Both are derived from large field datasets, one from the Arctic and the other from the Antarctic. The new model (IC4M9) describes wave attenuation depending on the ice thickness in association with the dependence on wave frequency, while the earlier default scheme (IC4M6) omits the dependence on ice thickness. The modeling results are evaluated against the satellite wave observations from SWIM/CFOSAT and the buoy measurements from the Svalbard Marginal Ice Zone 2024 Campaign (SvalMIZ-24). The comparisons with SWIM data validate the wave model skill in regions of open water or with light ice coverage. When evaluated against the SvalMIZ-24 data, the statistical performance of IC4M9 is substantially better than that of IC4M6, showing the influence of ice thickness on waves in the MIZ. Moreover, diagnosing systematic errors in the predictions by IC4M9, we find that the ice thickness field provided by the sea ice model CICE to the wave model is biased high in the MIZ, thus penalizing the performance of IC4M9 while not affecting the model IC4M6, which depends on frequency only.
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All authors W. Erick Rogers, Jie Yu, Jean Rabault, Ana Carrasco, Malte Müller

Saturday 29 August

Geophysics · Cross-listed from physics.ao-ph

Is the Atmosphere Really Such a Good Blanket?

Slavoljub Mijovic

The incorrect application of the current methodology for treating and averaging Earth's surface temperatures has contributed to contradictions and misinterpretations in different scientific disciplines, particularly in climate change and global warming research. The methodology previously proposed by the author addresses these shortcomings and provides a physically based framework for further analysis. This paper demonstrates that the contribution attributed to the atmosphere in determining the warming of the Earth's surface may be substantially overestimated, with the estimated effect differing by approximately 10 degrees Celsius from the conventional interpretation. The analysis is based on the ERA5 reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), using hourly skin temperature data for 1950-2025 on a global grid of approximately 1.04 million cells representing the Earth's surface. The proposed method enables an absolute assessment of global warming, confirms the long-term warming trend, and provides a comparative analysis with results obtained using the conventional methodology.
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Friday 28 August

Geophysics

Competition between thermal pressurization and dilatant strengthening in laboratory ruptures

Caiyuan Fan, Gang Lin, Jerome Aubry et al. (+4 more)

In fluid-saturated faults, thermal pressurization (TP) is predicted to induce rapid weakening, whereas dilatant strengthening (DS) can counteract this process through pore-space expansion. However, experimental constraints on their competition remain limited. Here, we investigate these processes using triaxial stick-slip experiments with direct on- and off-fault pore pressure (Pp) measurements on saturated, saw-cut, thermally cracked Westerly granite under effective confining pressures of 30-60 MPa. Results reveal a systematic rupture transition from co- seismic Pp rise (TP-type) to Pp drop (DS-type) with increasing shear strain, accompanied by a fast-slow-fast slip sequence, with slip velocities differing by up to three orders of magnitude. An undrained, adiabatic TP model reproduces the measured Pp rise evolution during early-stage fast events and indicates progressive shear zone widening that reduces TP efficiency. With continued slip, increasing cumulative plastic porosity reflects enhanced dilatancy during DS-dominated sequences. Microstructural observations reveal gouge development and a near- fault damage zone, providing independent evidence for this structural evolution. This evolution modifies fault stiffness and explains the observed transition in slip behavior. Despite contrasting Pp responses, fracture energy scales similarly with slip for both TP- and DS-type events, suggesting comparable rupture energetics. Overall, this study provides direct experimental evidence of TP-DS transitions, demonstrating that TP governs early-stage weakening but diminishes as dilatancy progressively strengthens with shear zone widening and near-fault damage. These results highlight that fault structure and its evolution plays a key role in controlling rupture dynamics in natural faults.
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All authors Caiyuan Fan, Gang Lin, Jerome Aubry, Damien Deldicque, Carolina Giorgetti, Harsha S. Bhat, Alexandre Schubnel

Thursday 27 August

Geophysics · Cross-listed from cs.IR

LitCurate: A Configuration-Driven AI-Assisted Framework for Scientific Database Construction with an Application to Lower-Mantle Equation-of-State Data

Abin Shakya, Wilson Samuels, Dominica Wilson et al. (+4 more)

The growing scientific literature contains decades of experimental and computational results that could support data-driven and physics-based modeling, yet much of this infor- mation remains locked in publications and is not readily usable for large-scale analysis or sci- entific software. Building structured databases from the literature is particularly challenging whenrelevantstudiesmustfirstbediscoveredamonglargecollectionsofpapersandreported quantities must be extracted with enough scientific context to remain usable. We present LitCurate, an open-source framework for building scientific databases from the literature using large language models within an auditable, stage-wise curation workflow. LitCurate integratesliteraturediscovery, relevancescreening, full-textprocessing, andstructuredinfor- mation extraction while retaining intermediate results and provenance, allowing researchers to inspect and revise individual stages rather than treating automated curation as a black- box process. We apply LitCurate to construct an equation-of-state database of lower-mantle and lower-mantle-relevant high-pressure mineral phases from experimental and theoretical studies, comprising 1,334 entries from 205 papers. The resulting dataset links reported equation-of-state parameters to mineral phases, compositions, equation formulations, meth- ods, and parameter constraints, and labels values as source-reported or citation-reported when provenance can be determined. The records are available through a searchable web application. By connecting scientific literature to traceable, machine-readable data, LitCu- rate provides a reusable approach for transforming accumulated literature into resources for scientific analysis and computational modeling.
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All authors Abin Shakya, Wilson Samuels, Dominica Wilson, Gioia A. Marchi, Israa Draz, Chenxing Luo, Renata M. Wentzcovitch

Geophysics

Quantifying the effects of nickel on Earth's inner-core nucleation

Jiahui Zhai, Liangrui Wei, Chen Gao et al. (+1 more)

The formation of Earth's solid inner core marks a major transition in the thermal and chemical evolution of the deep Earth, yet its origin remains paradoxical, as initial nucleation appears to require unrealistically large undercooling in the outer core. Here, we use atomistic simulations to quantify how Ni affects this process under inner-core conditions. While Fe-Ni alloys preserve strong thermodynamic competition between the hcp and bcc phases, the bcc phase consistently forms smaller critical nuclei and has lower nucleation barriers than hcp. Increasing Ni content in the melts further lowers the nucleation barrier and shortens the nucleation waiting time. Local chemical fluctuations also strongly affect the macroscopic nucleation rate. Combining these effects, bcc nucleation in Fe80Ni20 reaches about 250 K of undercooling, approaching geophysical constraints. We demonstrate that Ni enrichment, bcc nucleation, and chemical heterogeneity substantially narrow the inner-core nucleation paradox.
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All authors Jiahui Zhai, Liangrui Wei, Chen Gao, Yang Sun

Wednesday 26 August

Geophysics

Global Prevalence of Anti-similar Earthquakes at Intermediate Depth

Albert Leonardo Aguilar Suarez, Gregory Beroza, William Ellsworth et al. (+2 more)

Anti-similar earthquakes, those with nearly identical but flipped waveforms, remain an uncommon observation. We document three locations where these events occur at intermediate depth. The first of these is Colombia, including the well-established source of anti-similar events in the Bucaramanga Nest, and new reports in other intermediate depth seismicity clusters in the Wadati-Benioff zones of Colombia. The second is the Alaska subduction zone, and the third is the Vrancea seismic nest in Romania. These new observations, combined with reports from Japan and Chile, provide mounting evidence that anti-similar earthquake sequences might be a common or characteristic process of intermediate-depth seismicity. Either conjugate faulting promoted by eclogitization induced stresses or sub-parallel faulting can explain their occurrence at these depths. Careful examination of seismicity in other productive intermediate depth seismicity zones might reveal an even more widespread geography for anti-similar earthquakes and place tighter constraints on their underlying mechanism.
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All authors Albert Leonardo Aguilar Suarez, Gregory Beroza, William Ellsworth, Patricia Pedraza, Germán Prieto

Geophysics

Lab earthquakes confirm the theory of frictional slip pulses

Alina Shafir, Tom Gabrieli, Yuval Tal et al. (+1 more)

Large natural earthquakes are typically mediated by frictional pulse-like rupture, which features a finite slipping zone. Recently, a comprehensive two-dimensional theory of frictional slip pulses has been developed. It predicts that pulses are categorically unstable rupture modes, whose evolution is intrinsically slow. Unsteady pulses satisfy an equation of motion expressed in terms of discrete observables, which is inherently related to their steady-state counterparts. The theory also predicts a transition from decaying to slowly growing pulses. Here, we perform extensive lab earthquake experiments to test the theory. The experiments confirm the theoretical predictions for pulse-like lab earthquakes over a range of prestress levels, rupture nucleation conditions and small-scale fault roughness amplitudes. Specifically, the predicted time-dependent dynamics are tested in a plane defined by the evolving pulse size and peak slip rate, along with measurements of the dimensionless growth rate of pulses, demonstrating their intrinsically slow unsteady nature. The predicted transition between decaying and growing pulses is also experimentally demonstrated. These results constitute major progress in understanding a dominant earthquake rupture mode.
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All authors Alina Shafir, Tom Gabrieli, Yuval Tal, Eran Bouchbinder