Geophysical Journal International

SummaryOceanic subduction zone is the dominant pathway for transporting carbon into the interior of the Earth, and thus plays a critical role in deep carbon cycling. Despite being recognized as a key mechanism for carbon release in subduction zones, the metamorphic decarbonation outflux and efficiency remain subjects of ongoing debate. The thermal structure of subduction zone is widely recognized as a primary dynamic control on metamorphic decarbonation, however, the quantitative relationship between metamorphic carbon outflux and simplified thermal parameters of subduction zones (here defined as φ= slab age × subduction velocity/100 in kilometer) remains poorly constrained. On the other hand, previous studies on metamorphic decarbonation have been conducted within two distinct scenarios: the P-T-dependent decarbonation (PTD) system versus P-T-H2O-dependent decarbonation (PTHD) system, yet a quantitative comparison between these two scenarios remains lacking. In order to investigate the metamorphic decarbonation behavior of subducting slab in the PTD versus PTHD systems, we develop a coupled thermo-petrological model by integrating the thermodynamic dataset of temperature-pressure-(H2O)-dependent CO2 content into the thermal model of subduction zones. Systematic numerical models indicate that the metamorphic carbon outflux in the PTHD system is about 50 per cent lower than that predicted in the PTD system. Meanwhile, the quantitative functional relationship has been built between the metamorphic carbon outflux and φ, which reveals that the decarbonation outflux and efficiency decrease exponentially with increasing φ in both systems. Under present-day widespread subduction thermal conditions with the φ values of around 30 km, both PTD and PTHD system models yield low metamorphic decarbonation efficiency, suggesting that a substantial proportion of slab carbon is likely retained in the slab and transported into the deeper mantle.

SummaryThe Sichuan Basin in China has experienced a number of devasting earthquakes in the past 20 years, particularly on the Longmen Shan fault (LMSFZ) with the 2008 Wenchan and 2013 Luschan events. This study employs a hierarchical, four-dimensional (latitude, longitude, depth, time) clustering framework to characterize seismic activity in the Sichuan Basin. After the identification of spatial features of the region (e.g., faults), we then apply two cluster algorithms on the Longmen Shan fault data and compare the identification of the 2008 and 2013 events. In particular, we apply and compare Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Bayesian Gaussian Mixture Model (BGMM) on the identification of mainshock-aftershock sequence (as well as any foreshock events). By applying temporal clustering to the dataset and comparing DBSCAN and BGMM methods, we find distinct differences between the results. Specifically, we find that DBSCAN identifies a simple mainshock-aftershock sequence, while BGMM produces a more complex foreshock-mainshock-aftershock sequence. However, both scenarios have been identified within previous work on these events, highlighting that additional analysis is required and that single cluster algorithms should be applied with caution. The work here in comparing machine learning techniques within an integrated clustering framework is timely and will serve as a guide for more in-depth analysis on earthquake patterns and fault dynamics using these methods.

SummarySurface fibre-optic distributed acoustic sensing (S-DAS) typically requires trenching to achieve adequate coupling, which can be challenging and costly in hardrock environments. As an alternative, untrenched S-DAS offers significant time and cost savings, though at the expense of data quality. In this study, we systematically evaluated the impact of untrenched deployment on DAS data quality through a multi-method comparison with conventional sensors, including three-component geophones and vertical-component accelerometers. All recorded data were converted to particle velocity to enable direct amplitude comparisons across arrays. Phase fidelity was assessed using the surface-wave method, cross-power spectral analysis, and ambient noise interferometry. Untrenched S-DAS recorded amplitude levels that differed from those of conventional sensors, particularly above 50 Hz. However, within the surface-wave band (up to 35 Hz), the data quality was sufficient to derive reliable shear-wave velocity profiles, especially when the data were resorted to common-receiver gathers. Sensitivity and coherence decreased significantly at higher frequencies and larger offsets, and a systematic time delay was observed for DAS in the surface-wave band. Ambient noise interferometry was ineffective for the untrenched DAS array, largely due to variable channel coupling and system-specific noise. This study provides a systematic field comparison of untrenched S-DAS and conventional sensors in hardrock settings, outlining both the limitations and the practical potential of this cost-effective deployment method.

SummaryFaults and fractures heal and seal over time, decreasing along-fault permeability, and increasing reactivation stress. This presents a dilemma in geothermal reservoirs as maintaining permeability is crucial for reservoir longevity, but the reactivation of faults to increase permeability can also cause hazardous seismicity. The healing rate of faults is temperature-dependent and shows significant differences under wet and dry conditions. We investigate the healing behavior of a bare, water-saturated fault surface at temperatures up to 163°C through slide-hold-slide experiments. The gneiss sample from the UtahFORGE geothermal demonstration project, is continuously actively probed with P-waves and monitored for passive acoustic emissions radiating from the fault. Our data show that with increased temperatures, the fault surface friction decreases, healing rate increases and the fault becomes more prone to unstable slip. The decrease in friction and increase in healing rate we measure are larger and occur at lower temperatures than previously demonstrated. P-wave amplitudes and P-wave velocities increase during healing, with amplitudes sensitive to temperature but velocities conversely insensitive. We attribute this to a sensitivity of the P-wave amplitude to changes in contact area with P-wave velocity correlating with mechanical compaction, off-fault microcracks, and the formation of wear products during sliding. The sample continues to creep throughout holds during our hotter experiments, but the creep motion does not erase continuous healing. Acoustic emissions spike upon slip reactivation, where higher event rates and higher slip velocities occur as healing progresses—after longer hold times and at higher temperatures. The amplitude of the P-wave, as well as the acoustic emission rate, show precursory signs of spontaneous reactivation and therefore might have potential in forewarning slip.

SummaryWe present a new, variational, fully nonlinear, probabilistic ambient noise tomography method, which estimates subsurface structure and quantifies the corresponding uncertainties directly in three dimensions (3D) from inter-receiver seismic surface wave dispersion data. We use the method to invert for high resolution 3D seismic velocity models of the upper crust beneath Great Britain using seismic ambient noise data recorded around the region – a task that proved too high-dimensional and hence computationally demanding for Monte Carlo sampling to converge to a stable solution. We compare the inversion results from the new method to those obtained from two standard, indirect inversion methods, in which 2D (geographical) surface wave velocity maps and 1D (depth) shear velocity profiles are estimated in two separate, consecutive steps. The results show that the direct-3D scheme preserves better lateral continuity and produces better data fit than the two-step methods, and provides information about lateral correlations that is absent from the two-step solutions. The inversion results are consistent with large-scale geology of Great Britain, and for the first time provide seismologically-imaged evidence of the Great Glen Fault and other major tectonic faults. We therefore propose that direct-3D inversion schemes should be used where possible for surface wave inversion as they provide improved results at little additional computational cost.

SummaryAccurate three-dimensional coseismic deformation fields are critical for fault mechanics analysis and hazard assessment, but the sparse distribution of Global Navigation Satellite System (GNSS) stations often limits reconstruction accuracy. This study proposes Integrated Dislocation and Strain Models (IDSM) that seamlessly integrate GNSS and Interferometric Synthetic Aperture Radar (InSAR) data. This is achieved by combining a surface-constrained strain model and a subsurface-constrained dislocation model, which adaptively optimizes multi-source data weights through Variance Component Estimation (VCE), challenging the traditional reliance on uniformly distributed observations. Simulation experiments demonstrate that under insufficient GNSS coverage, this method improves deformation recovery accuracy by 10 per cent to 70 per cent in the vertical, north, and east components compared to the ESISTEM-VCE (Extended Simultaneous and Integrated Strain Tensor Estimation from Geodetic and Satellite Deformation Measurements-VCE) method, with particularly significant enhancement in the north component. Applied to the 2021 Yangbi MW6.4 and Maduo MW7.4 earthquakes, the study reveals distinct deformation patterns: the Yangbi event exhibits right-lateral strike-slip rupture with a maximum east-west extensional displacement of 87 mm and vertical subsidence of 59.8 mm, showing antisymmetric horizontal deformation around the epicenter. In contrast, the Maduo earthquake is dominated by left-lateral strike-slip motion, with east-west displacement reaching 1.4 m, while north-south and vertical deformations display patchy distributions along the fault. Error analysis confirms accuracy improvements over the ESISTEM‑VCE method. For the Yangbi earthquake, the Root Mean Square Error (RMSE) decreased by 50 per cent (east), 64 per cent (north), and 44 per cent (vertical) at GNSS validation points. Corresponding improvements of 6.1 per cent (east) and 53.5 per cent (north) were achieved for the Maduo earthquake.

SummaryOceanic transform faults (OTFs) have long been viewed exclusively as vertical, strike-slip structures offsetting mid-ocean ridges, yet their deep geometry and structural complexity remain poorly constrained. Thus, key questions persist, including whether OTFs are single-stranded and continuous, whether they maintain vertical dip angles, if they accommodate mixed-mode slip, and what factors control their geometry. This study addresses these questions through a global statistical analysis of teleseismic earthquake focal mechanisms from 150 OTFs across diverse tectonic settings. We introduce stack maps, a novel method that quantifies fault dip and rake, providing a graphical representation of average focal mechanisms. Our findings reveal that while OTFs tend to conform to the standard vertical, strike-slip model, nearly half exhibit deviations, either in dip or motion, challenging the classical view of these plate boundaries. We identify four distinct OTF categories: (1) those adhering to the standard model, (2) non-vertical faults with transtensive/transpressive components, (3) non-vertical faults accommodating strike-slip motion, and (4) vertical faults with a vertical component of motion. Tectonic regime shifts emerge as a primary driver of structural changes, with non-vertical geometries persisting even after the regime reverts to pure strike-slip motion. This structural memory suggests that fault geometry, once established, remains stable over geological timescales of several tens of Myr. By reconciling previously ’unusual’ focal mechanisms with fault structure and dynamics, this work demonstrates that global seismic catalogues, when analysed statistically, offer robust insights into OTF geometry and tectonic regimes.

SummaryAs a critical category of geophysical data, magnetic anomalies play vital roles in geological interpretation, resource exploration and target detection. For most applications involving magnetic anomaly data, the ideal dataset should have uniformly distributed data points, high resolution and completeness without gaps. However, because of the environmental constraints and measurement limitations, magnetic anomaly data obtained from real-world measurements often fail to meet these requirements. Thus, interpolation techniques present effective and cost-efficient technical approaches for processing measured magnetic anomaly data to meet the aforementioned criteria. To our knowledge, current research on magnetic anomaly data interpolation has primarily focused on gridding methods for interpolating irregularly sampled data into gridded data and super-resolution interpolation methods aimed at enhancing spatial resolution. Meanwhile, studies on interpolation methods specifically designed to fill large-area data gaps remain relatively scarce. To address the challenge of reconstructing large-area missing magnetic anomaly data, we propose a data-driven method for magnetic anomaly data gap filling. First, based on the analysis of the characteristics of magnetic anomaly data, we construct an open-source magnetic anomaly interpolation dataset (MAID) specifically designed for magnetic anomaly data interpolation tasks. Subsequently, we develop a magnetic anomaly data gap-filling generative adversarial network (MADGF-GAN) tailored for magnetic anomaly data gap filling. Upon sufficient training on the MAID training set, MADGF-GAN can directly fill gaps in given magnetic anomaly data. Finally, the effectiveness of MADGF-GAN is validated using four test samples from the MAID test set and Afghan aeromagnetic data. Compared with four existing interpolation methods, MADGF-GAN demonstrates considerable advantages in terms of interpolation accuracy, computational efficiency and practicality. This study demonstrates the potential of data-driven approaches in magnetic anomaly data processing, providing crucial technical support for related geoscientific applications.

SummaryBioleaching is a biologically facilitated process that helps to dissolve valuable metals in order to extract them from the mineral gangue. Applied in the field to heap ores, its efficiency mainly depends on solution flow inside the heterogeneous heaps, which is often tortuous and can remain stagnant in the pores and crevices between the particles. Methodologies that can help to monitor the bioleaching processes are therefore needed to improve operational efficiency. In this article, we present for the first time preliminary laboratory-scale investigations on spectral induced polarization (SIP) during the bioleaching of chalcopyrite (CuFeS2) containing ore material from a mine in Chile. Two column experiments representing different stages of the bioleaching process were monitored under un-saturated and highly acidic environment (pH ~2). Our objective was to explore the feasibility of SIP for detecting changes in electrical properties potentially associated with bioleaching-induced mineral dissolution and alteration. The results show a rapid decrease in SIP phase shift and imaginary conductivity during the early stage of bioleaching, while the real conductivity remains relatively stable. At a more advanced stage of bioleaching, the phase response is weaker and more stable. A relaxation time distribution (RTD) analysis was applied to further investigate changes in polarization mechanisms. Prior to bioleaching, the RTD exhibits a well-defined peak consistent with polarization controlled by sulfide mineral grains, whereas after one month of bioleaching the RTD broadens and shifts toward larger relaxation times, accompanied by a decrease in chargeability. This combined evolution suggests bioleaching-induced modifications of electrochemically active surfaces, potentially related to mineral dissolution and the formation of passivation layers. Estimated particle sizes derived from the RTD analysis are consistent with scanning electron microscopy observations. Although, the absence of a dedicated abiotic control column prevents us from attributing these changes unambiguously to bioleaching alone, these results highlight the potential of SIP as a non-invasive, real-time and integrative tool to monitor leaching processes and to identify zones that may remain weakly affected by leaching.

SummaryModeling crustal deformation induced by fault slip is a fundamental problem in structural geology and seismology. However, the challenges of data sparsity and spatial discontinuity impose significant limitations on conventional forward and inverse methods, often resulting in low computational efficiency and limited accuracy. Although AI-based approaches such as Physics-Informed Neural Networks (PINNs) and Physics-Encoded Finite Element Networks (PEFEN) offer new solutions for sparse-data problems governed by physical laws, their underlying assumption of spatial continuity conflicts with the inherent displacement discontinuities of fault-slip fields. To address this limitation, we propose a novel method—the Split-Node Physics-Encoded Finite Element Network (SN-PEFEN)—which integrates the node-splitting mechanism into the PEFEN framework. By explicitly encoding spatial discontinuities into the nodal topology during mesh preprocessing, SN-PEFEN not only overcomes the theoretical limitations of existing PEFEN models in handling discontinuous fields but also maintains physical consistency. We apply SN-PEFEN to perform forward and inverse modeling of deformation fields induced by complex fault slip in both 2D and 3D heterogeneous media. For a model with over one million degrees of freedom, the forward simulation achieves over 40× speedup compared to traditional FEM (∼1,800 s vs. 42 s), while maintaining comparable accuracy. In inverse modeling, the solution converges within only 100 iterations, with a total runtime of approximately 2,000 s, demonstrating high computational efficiency. This method establishes a new high-efficiency paradigm for analyzing complex discontinuous deformation in geomechanics, offering promising applications in multi-fault system analysis and fault-slip inversion. Furthermore, SN-PEFEN facilitates rapid, physics-based assessments for emergency seismic response and disaster management, while laying the groundwork for next-generation data-driven regional earthquake early warning systems.

SummaryUltralow velocity zones (ULVZs) at the Earth’s core-mantle boundary (CMB) are marked by substantial reductions in seismic velocities. They are often associated with significant increases in density, providing important insights into deep Earth composition and dynamics. In this study, we investigate ULVZs beneath eastern and southern Asia, regions associated with long-term subduction, by analyzing high-frequency (~1 Hz) ScP waveforms recorded at the small-aperture KZ array. After correcting for attenuation along the ScP path, we perform a grid search to match the observed waveform complexities with synthetics generated for a comprehensive suite of 1D ULVZ models. The best-fitting models for each event constrain ULVZ thickness, P- and S-wave velocity reductions, and density anomalies, revealing widespread but laterally variable ULVZ structures, although the influence of finite ULVZ geometry cannot be entirely excluded. The correlations among these parameters point to iron-rich chemical heterogeneity as the dominant origin of the imaged ULVZs, likely reflecting iron enrichment associated with long-term subduction processes.

SummaryWe introduce Virtual Seismic Arrays, which predict full array recordings from a single reference station, eliminating the need for continuous deployment of all stations for continued array operation. This innovation can reduce costs and address logistical challenges while maintaining multi-station functionality. We implement a Virtual Seismic Array using a deep learning encoder-decoder approach to predict the transfer of the seismic wavefield between stations. We train on recordings of secondary ocean microseisms from the Gräfenberg array in Germany to retrieve predictive models for each array station, which together form the Virtual Seismic Array. To evaluate its performance, we beamform original and predicted waveforms to detect the dominant secondary microseism sources. We assess three source regime scenarios: one where only a single dominant source regime is present in both the training and test dataset, another with two different regimes in the training data but only one in the test data, and a third where the training data does not contain the dominant source regime observed in the test data. Our results show strong agreement between predicted and original beamforming results in cases where the observed source regime was part of the training, demonstrating the feasibility of Virtual Seismic Arrays.

SummaryIn recent years, distributed acoustic sensing (DAS) has enabled the observation of strain over tens of kilometres at metre-level intervals by using optical fibre as a sensor. This study presents an analytical solution for the cross-spectrum of ambient noise with DAS data acquired from arbitrarily shaped and/or multiple fibre-optic cables, with the aim of estimating subsurface S-wave velocity structures using the spatial autocorrelation (SPAC) method. Our formulation accounts for both isotropic and anisotropic wave incidence. The analytical cross-spectrum depends on the angles between the horizontal direction connecting the two measurement points and the axial strain directions at the two points. This study demonstrates that both Rayleigh and Love waves contribute to the cross-spectrum, and that their contributions vary in a complex manner depending on the cable geometry, seismic velocity structure, interstation distance between observation points, and source amplitudes. By using this analytical solution, an integrated analysis combining the SPAC method and the ambient noise tomography method is applicable to DAS data acquired from arbitrarily shaped and/or multiple cables. In addition, the analytical expression considering anisotropic wave incidence will be useful for correcting travel-time anomalies caused by source heterogeneity. The application of our formulation to DAS data from winding or multiple cables will facilitate high-resolution and precise imaging of the three-dimensional structure.

SummaryFull waveform inversion (FWI) is a powerful tool in seismic imaging, capable of producing high-resolution models of the subsurface. However, the method remains computationally intensive and sensitive to initial models due to its nonlinearity and ill-posed nature. To quantify uncertainty in FWI results, variational inference (VI) methods, such as Stein Variational Gradient Descent (SVGD), have been increasingly explored. These approaches approximate the posterior distribution by evolving a set of particles using gradient information from the log-posterior. Despite their promise, their effectiveness heavily depends on the quality of the prior used for initialization. In this work, we propose a hybrid framework that improves the efficiency and robustness of VI-based FWI by initializing SVGD with samples drawn from a reconstruction-guided diffusion model. Rather than replacing SVGD with a generative sampler, our approach preserves the theoretical foundations of VI while leveraging the expressive capacity of deep generative models. The diffusion model is trained to generate geologically plausible models conditioned on seismic images, thereby guiding the SVGD initialization toward regions of high posterior support. This initialization significantly reduces the number of required SVGD updates and improves convergence, while keeping the core VI formulation intact. Our results show enhanced posterior approximation and more geologically consistent solutions, with an order of magnitude lower computational cost compared to naïvely initialized SVGD. However, challenges remain, such as the computational demands of likelihood evaluations, the formation of a training set that encompasses all plausible realizations, and sensitivity to reconstruction-guidance weights during sampling. Overall, this method provides a principled and efficient approach to uncertainty-aware FWI, integrating physics-informed inference with data-driven generative modeling for practical applications in full waveform inversion.

SummaryThe degree-2 spherical harmonic coefficients of Earth’s time-variable gravity field are highly sensitive to large-scale mass redistribution within the hydrosphere and cryosphere. Under contemporary global warming, climate-driven mass changes in these reservoirs are a dominant source, yet their individual contributions remain incompletely quantified. Traditional estimates based on hydrospheric models, filtered GRACE spherical harmonic solutions, or GRACE mascon products are limited by incomplete cryospheric representation, spatial leakage, and regularization biases. Here, we apply the Fingerprint Approach that solves the sea-level equation on an elastic Earth to generate geoid fingerprints for four barystatic processes: terrestrial water storage, the Greenland Ice Sheet, the Antarctic Ice Sheet, and mountain glaciers. Using these fingerprints and unfiltered GRACE/GRACE-FO Stokes coefficients for 2003–2024, we reconstruct the individual degree-2 coefficients C20, C21, and S21, along with the associated time series of Earth’s dynamic oblateness (J2) and the mass terms of polar motion excitation ($\chi _{\rm{1}}^{{\rm{mass}}}$, $\chi _{\rm{2}}^{{\rm{mass}}}$). We evaluate the reconstructed contributions against residual geodetic observations from satellite laser ranging and Earth orientation parameters, after removing atmospheric, oceanic, and glacial isostatic adjustment effects. The combined hydrospheric and cryospheric reconstructions reproduce both the secular trends and annual cycles of the residual observed J2, $\chi _{\rm{1}}^{{\rm{mass}}}$, and $\chi _{\rm{2}}^{{\rm{mass}}}$ series. Terrestrial water storage dominates the seasonal variability of J2, $\chi _{\rm{1}}^{{\rm{mass}}}$, and $\chi _{\rm{2}}^{{\rm{mass}}}$, whereas accelerated ice-mass loss from Greenland and Antarctica controls the secular trend, with mountain-glacier mass loss also contributing to the long-term trend in J2. The resulting polar motion excitation drift closely matches the residual geodetic estimate in both magnitude and direction, indicating that contemporary climate-driven mass redistribution can largely account for recent changes in residual geodetic observations, and demonstrating the value of fingerprint-based reconstructions for monitoring climate impacts on the Earth system.

SummaryWe present a forward waveform modeling study to investigate the regional crustal structure of the central-southern Apennines, along a NNE-SSW profile. The profile cross-cuts the Apenninic chain axis and extends from the eastern Adriatic domain, characterized by a thick crust and thick seismogenic layer, to the western Tyrrhenian domain, dominated by tectonic thinning, distributed CO2 gas emissions at the surface and volcanic structures. This region hosted the largest earthquakes in recent history, making precise knowledge of the crustal structure crucial for a comprehensive understanding of seismogenesis and seismic hazard assessment. We analyzed and modelled seismic data from two lower-crustal strike-slip earthquakes in the eastern segment of the profile (2018 Mw 5.1 and 2023 Mw 4.6), recorded by the Italian National Seismic Network (IV). The two events, located along the target NNE-SSW linear profile, provide a unique opportunity to study the westward propagation and evolution of seismic phases. Using a 2D numerical modeling approach, we modelled direct (Sg) and Moho-reflected (SmS) phases on transverse component seismograms, comparing the synthetic to the observed waveforms in terms of arrival times and waveform shapes. A faster Adriatic lower crust with an average shear-wave velocity of 3.85 km/s supports the hypothesis of distributed crustal mafic intrusions at the margin between the Apenninic chain and Adriatic foreland. We estimate a local Adriatic Moho depth of 38 km, in agreement with previous investigations. Furthermore, we identify a strong attenuation zone across the Apenninic chain axis, extending directly from the surface down to 10 km depth, significantly impacting both seismogenic processes and waveform propagation. This first, regional-scale waveform study highlights the significance of waveform analysis for constraining seismic velocities and interface velocity contrasts in the Southern Apennines mountain range.

SummarySeismoelectromagnetic (SE) signals are created in wet poroelastic media by electrokinetic conversions occurring at the pore scale. Two of these signals are frequently investigated: a co-seismic wavefield, bounded to the propagating seismic waves, and an electromagnetic (EM) wave created when a seismic wave passes through the interface between two porous media. Driven by the possibility offered by the SE signals in terms of vertical resolution, compared with seismic reflection methods, a restricted number of authors studied how a thin layer, or a combination of thin layers, can modify SE signals. In this context, the present work presents original experimental and numerical data as a means to further investigate the relation between the thickness of a layer and the EM interface-generated response (IR). In addition, the fact that the IR is very sensitive to contrasts in fluid conductivity, whereas seismic waves are not, is an appealing characteristic of SE exploration. Consequently, we explored the influence of the pore-fluid electric conductivity, combining experimental and numerical methodologies. We identified a strong similarity in how layer thinning affects both seismic waves and IR signals. Moreover, we observed that the maximum enhancement of an IR signal occurs when the layer thickness is approximately half the P-wave wavelength (λP) in the layer. When analysing the influence of fluid conductivity, we showed that the electrokinetic theory adopted in this study provides satisfactory predictions for the waveforms and amplitudes observed experimentally. Finally, we extrapolated our experimental results with field-scale simulations in order to understand how the effects observed experimentally translate to georesources applications.

SummaryOcean-bottom seismometers (OBSs) are reliable instruments to record ground motions and acoustic signals on the sea floor. Precise timing of the data is essential for most seismological analyses. The internal clocks of the OBSs are not GNSS-controlled, so the clock drift mainly caused by ageing of the quartz crystal and temperature effects must be corrected. As part of the BRAVOSEIS experiment, eight OBSs were deployed in the Antarctic Bransfield Strait for 13 months. All OBSs suffered from a large (-15.3 to 5.4 s) and non-linear (-1.2 to -0.6 s residual to linear drift) clock drift. We used noise cross-correlations to determine the clock drift. The parameters for data pre- and post-processing such as filtering and normalisation had to be selected carefully. Overlapping correlation windows were stacked to derive daily Green’s functions. The time shift between consecutive days was calculated and distributed linearly over the data to obtain a continuous data set without gaps or overlaps. Airgun shots were used to constrain the cumulative clock drift of one station without initial synchronisation. Two onshore stations served as GNSS-controlled reference for four OBSs in the northern part of the Central Bransfield Basin. A different noise regime prevailed in the southern part of the basin; therefore, two already corrected OBSs from the northern part were used as reference stations for the southern OBSs. In this way, the clock drift of all OBSs could be corrected accurately.

SummarySpectral induced polarization (SIP) is a promising technique for detecting microbial activity in porous media, yet its interpretation remains limited by the absence of mechanistic models that account for microbial cell structure. In this study, we present a new semi-analytical model for the electrical polarization of microbial cells that treats both the cell plasma and the surrounding medium as electrolytes, and accounts for the cell membrane as well as the influence of the charged surface structures of the cell. We validate our model through numerical simulations based on the Poisson–Nernst–Planck equations. The model builds upon the membrane capacitance model by Sun and Morgan and integrates surface conductivity effects via the O’Konski model and low-frequency polarization using an adapted Dukhin–Shilov approach. The agreement between the numerical results and our new semi-analytical model is good. The model accounts for three dominant polarization mechanisms: (1) diffuse layer polarization at low frequencies (102–104 Hz), (2) membrane-related capacitive effects at intermediate frequencies (105–107 Hz), and (3) Maxwell-Wagner-type polarization at high frequencies (107–109 Hz). In experimental studies, polarization of bacteria typically appears at frequencies around 0.05 and 20 Hz. As the characteristic frequency of polarization processes usually decreases with increasing polarization length scales, the remaining discrepancy between model and experimental observations suggests that measured signals may be influenced by cell aggregates, biofilms, or metabolic byproducts. Our findings provide a foundation for a mechanistic understanding of microbial polarization and highlight the need for future work to extend the model to conglomerates of microbial cells.

AbstractHigh-frequency induced polarisation (HFIP) measurements enable quantification of ground ice content in frozen media by capturing ice relaxation within the frequency range of 1 to 100 kHz. Existing parameterised inversion approaches may bias results by imposing an ice relaxation signature where none exists, assuming a Cole-Cole-type response that may not reflect the true dielectric behaviour of ice, and neglecting low-frequency polarisation. These limitations can lead to high data misfits and ambiguities in interpretation. This study presents an alternative approach that applies independent frequency inversion to directly derive complex resistivity spectra from field measurements, avoiding reliance on predefined models. The resulting inverted spectra provide a representation that more closely captures the true subsurface response. A second, petrophysical, inversion is then performed by fitting a two-component mixture model to the inverted spectra, weighted by the volumetric fractions of its components. One of these components is ice, allowing for the estimation of the volumetric ice content. The approach was applied at Heliport Mire (Abisko, Sweden), a permafrost peatland site, using two complementary profiles: a 50-m 2D profile that captured broad lateral variations of frozen to unfrozen conditions, and an 8-m high-resolution 2D profile that resolved the vertical transition between the upper unfrozen and underlying frozen layers. Independent frequency inversion, across 1 Hz to 57 kHz, successfully produced smooth, coherent spectral responses of true resistivity and phase shift across both profiles. Petrophysical inversion results show diverse conditions along the profile, identifying three distinct zones: ice-rich frozen peat (40-77% ice content), a thawed or degraded peat region (<10% ice content), and unfrozen forest (<5% ice content, effectively representing ice-free conditions). HFIP-derived ice content values were consistent with those derived from laboratory measurements on a permafrost core extracted along the profile. The high-resolution profile distinctly identified the boundary between unfrozen and frozen ground, as confirmed by direct probing measurements. Additionally, the petrophysical model resolves parameters such as shape factor and matrix permittivity, offering further insight into subsurface properties. This methodology advances ground ice characterisation by providing robust quantitative estimates of ice content while retaining spectral information with broader interpretative potential.