Geophysical Journal International

SummaryGeodetic measurements of ground deformation are crucial to identifying and interpreting geophysical processes. We develop a method to fuse data streams from multiple geodetic techniques into a single, three-component deformation field with quantified uncertainties, and without invoking a geophysical model. The fusion is formulated as a semi-parametric latent factor model: a linear mapping ties each observation to the underlying 3-D displacement, while the displacement components are represented nonparametrically with multi-output Gaussian process priors. To achieve practical performance at regional scale, we deploy two complementary sparse GP engines: an Informative Vector Machine (IVM) that selects a small, most-informative active set for fast subset-of-data inference, and a Sparse Variational GP (SVGP) that summarizes the full dataset with inducing points and optimizes a global variational bound. Together, these reduce the scaling of the computation to near-linear in data size and cubic only in the active/inducing set, enabling the potential of fusion of dense geodetic data while maintaining rigorous uncertainty quantification. We demonstrate the approach on coseismic deformation from the 2020 Sparta, NC, USA and 2016 Meinong, Taiwan earthquakes, fusing interferometric synthetic aperture radar (InSAR) with either light detection and ranging (LiDAR) or global navigation satellite system (GNSS) data, respectively. The fused solutions show marked improvements in the precision and coherence of the resolved deformation field and deliver robust, spatially explicit uncertainty estimates. The methodology is readily extensible to time-varying observations to produce four-dimensional (space-time) deformation fields, offering a scalable path to richer characterization of transient geophysical phenomena.

SummaryThe 2019 Mw 6.4 Durrës earthquake in Albania caused severe loss of life and economic damage, highlighting the seismic hazard along the Adriatic–European plate boundary. This study provides the first high-resolution analysis of the aftershock sequence based on data from a dense local seismic network deployed three weeks after the mainshock. Using machine-learning detection and phase-picking tools, we identified 19 152 aftershocks (Ml − 1.8 to 4.6; Mc ≈ 1) over a nine-month period. Based on a newly derived 1D velocity model with station corrections, accounting for large vertical and lateral velocity variations, we relocated the events applying cross-correlation based differential travel times and the double-difference algorithm. The refined seismicity images clearly reveal several sub-parallel ∼30° NE-dipping blind fault structures; the most prominent one, between 12 and 18 km depth, probably hosted the Durrës mainshock. The blind thrust faults lie beneath thick sediments and cut through a carbonate platform and into the Adriatic basement, indicating thick-skinned deformation. Our observations may be interpreted as incipient large-scale slicing and underplating of subducted Adriatic crust. Additional shallow seismicity within a duplex structure in the hanging wall points is relevant for seismic hazard, as even a relatively moderate earthquake occurring close to the surface could cause significant damage.

SummarySeismograms contain a variety of signals in addition to direct waves. The local later phase in the S coda (LPS; strongly reflected or scattered S-waves by subsurface heterogeneities when the hypocentre, station, and the origin of the later phase are located relatively close to each other) is useful for investigating fine-scale subsurface heterogeneities such as significant velocity contrasts and localized scatterers. With the development of dense seismic observation networks, accurate and rapid automatic processing techniques for large numbers of seismograms have become important. The introduction of deep learning techniques enables us to automate the processing of seismograms, such as phase detection and arrival time picking of direct waves with high quality and speed. To utilise the information on LPSs contained in large volumes of seismic waveform data, we developed the LPS-detector, a convolutional neural network-based automatic LPS detection model. We trained the LPS-detector using single-station data from the Moriyoshi volcanic area in northeastern Japan, which is known for observing distinct LPSs. We create an original training dataset with 5 000 earthquakes in this area. The training dataset was separated into the training (3 200), validation (800), and test (1 000) subsets. The LPS-detector yielded a 0.910 area under the curve (AUC) of the receiver operating characteristic (ROC) curve and 0.966 AUC of the precision-recall (PR) curve. These scores indicate good performance for automatic LPS detection, which is comparable to that of manual detection. Additionally, we confirmed that the LPS-detector could detect LPSs at other stations if the waveform characteristics were similar to those of the training subset. The large volume of the LPS catalogue obtained by the LPS-detector provides a new insight into the LPS origin in the Moriyoshi volcanic area. The LPS-detector detected 3 951 new LPSs outside of the training period. Combined with manual detection, 7 599 LPSs were detected in this area. Based on the comprehensive LPS catalogue, we found that the origin of LPSs in this area lies just beneath the earthquake swarm and is inclined at approximately 12° with a south-westward dip. These results indicate that the LPS-detector enables us to extract LPS information from seismogram big data and explore fine-scale subsurface structures.

SummaryThe British Palaeogene Igneous Province records the palaeomagnetic field at a time when it had returned to a reversing state following the Cretaceous Normal Superchron. Whilst the province is well studied for palaeomagnetic directions, palaeointensity results remain scarce. New palaeointensity and palaeomagnetic directional results are presented from a Palaeocene basaltic dyke swarm on the Sleat Peninsula, Skye (NW Scotland). The mean palaeomagnetic direction from 24 dykes has declination 356.7° and inclination 60.0°. The corresponding palaeopole lies at 75.2°N, 181.8°E with associated 95 per cent confidence interval, A95, of 6.6° which is close to, but distinguishable from, previous results from this swarm. The angular dispersion of virtual geomagnetic poles (VGPs), SB, was 17.9 ± 3.2°, consistent with stationary VGP dispersion throughout the Palaeogene. Three experimental palaeointensity methods were attempted: Shaw Double Heating Technique, thermal Thellier, and microwave Thellier. Palaeointensity experiments were successful from two dykes giving results of 15.8 ± 2.3 μT and 12.0 ± 3.4 μT which correspond to virtual dipole moments of 27 ± 4 ZAm2 and 21 ± 6 ZAm2. These are notably weaker than the long-term average and the recent field. Additionally, we present proof-of-concept of a novel method for estimating palaeointensity. The preliminary method was applied to results from the thermal Thellier experiments and is based on reproducing non-ideal Arai plot behaviour using a phenomenological model of thermal remanent magnetisation. This inversion highlighted laboratory field orientation as a major control on Arai plot shape with antiparallel fields predicted and observed to cause major distortions. It produced similar palaeointensity results to the conventional approach but derived from significantly more specimens.

Summary3D magnetotelluric (MT) inversion is a powerful tool for imaging the Earth’s electrical structure, yet its computational demands remain a major challenge, particularly when simulating responses across broad frequency bands. Conventional inversion schemes rely on a unified mesh for forward simulation at all frequencies, which inflates the number of model parameters and greatly extends runtime. To overcome these limitations, we present a finite-volume-based frequency-domain survey decomposition (FVFSD) method that adaptively constructs forward simulation meshes according to the skin depth of each frequency while maintaining a fixed horizontal discretization. This design decouples forward simulation meshes from the inversion mesh, striking a balance between accuracy and efficiency. To further improve the treatment of resistivity contrasts, an equivalent circuit scheme is employed to compute effective conductivities within control volumes, outperforming conventional volume-weighted averaging. We validate the proposed method through comprehensive numerical experiments, including synthetic benchmarks and real-world case studies from the Akebasitao region (China) and the Southern African MT experiment. Results demonstrate that FVFSD achieves accuracy comparable to standard finite-difference forward simulations, while significantly reducing computational time. In large-scale MT inversions, this acceleration directly translates into faster convergence and more efficient recovery of lithospheric-scale resistivity structures. The method is fully compatible with existing inversion algorithms and solvers, making it straightforward to integrate into standard workflows. Overall, FVFSD provides a scalable and accurate strategy for advancing 3D MT inversion, with clear implications for lithospheric studies, resource exploration, and tectonic investigations.

SummaryDistributed Acoustic Sensing (DAS) captures seismic wavefields through precise measurement of phase changes in back-scattered laser pulse signals in an optical fiber that reflect distributed strains or strain rates over continuous fiber segments. The limitation of the DAS wavefield measurement for near surface imaging remains unclear and is worthy of verification with conventional dense nodal arrays. This study compares co-located DAS and dense nodal array recordings in near-surface active surveys conducted in Shenzhen, China. Our field experiment reveals the spatially fixed, step-like distortions of coherent signals in DAS wavefield recordings. These artifacts are attributed to the gauge-length spatial average acting upon fiber segments with heterogeneous sensitivity, which can arise from variations in backscatter intensity and cable coupling conditions. These distortions cause partial deviations between waveforms of DAS and co-located seismometer from their theoretical relationship. A statistical comparison of phase differences between DAS and seismometer waveforms across all nodal points reveals a normal distribution, indicating the prevalence of such distortions in DAS recordings. Moreover, the distortion becomes more severe as frequency increases, resulting in overly dispersed phase difference distributions between DAS and seismometers. This leads to discrepancies in the dispersion curves extracted from them at high frequencies, as dispersion extraction depends on the relative energy peaks contributed by the dominant channels. This study demonstrates that within the frequency range not severely affected by distortion, DAS can effectively extract comparable dispersion curves and velocity structure profiles in place of the nodal array for near-surface imaging applications, as validated through full-profile comparisons. But the DAS wavefield distortions limit advanced applications of the complete wavefield for better subsurface characterization, and imply that DAS data simulation and instrument response analysis should consider channel ensembles.

SummaryA temporary array of seven ocean bottom seismometers (OBS) was deployed offshore the Guerrero subduction zone in Mexico to monitor previously unreported shallow seismicity. These OBS instruments are especially valuable for studying earthquake activity in the Guerrero seismic gap, where a future large event could severely impact densely populated regions of Mexico. This study investigates the shallow seafloor structure, including site effects, shear wave attenuation, and velocity models, using both earthquake data and ambient seismic noise. We employed spectral inversion to estimate the quality factors of shear wave attenuation and site effects. Additionally, we calculated the microtremor horizontal-to-vertical spectral ratio (HVSR) as a proxy for site response and invert it using constraints from hydroacoustic seafloor profiles, parametric sub-bottom profile system (TOPAS), to derive the shallow velocity structure beneath the stations. The inclusion of TOPAS data in the inversion significantly improved convergence, reduced misfit, and resulted in more reliable subsurface models. The HVSR inversions indicate the presence of water-saturated sediments within the upper 250 m, characterized by shear-wave velocities ranging from 55.2 to 1950 m/s and Vp/Vs ratios between 1.80 and 27.84. Strong attenuation effects, typical of marine environments, were observed, with Q(f) values as low as Q = 86f0.62 in the forearc accretionary wedge. Our attenuation estimates are consistent with those found in other offshore subduction zones, contributing to a broader understanding of shallow structures in similar tectonic settings worldwide. We found strong agreement between the estimated site effects and HVSR results, underscoring their close relationship and supporting the reliability of our site response estimates. This is the first study in Mexico to use OBS data to characterize offshore attenuation, site effects, and velocity structure, information that will support future seismological analyses, including earth structure imaging and investigations of both large earthquakes and shallow slow earthquakes in the Guerrero seismic gap.

SummarySeismic sources are typically characterized as stochastic slip distributions on complex fault geometries, which pose significant challenges for computational modelling. Source scaling laws, however, offer a streamlined alternative by correlating simplified fault geometry and slip characteristics with earthquake magnitude. So far, distinct scaling laws have been developed for different tectonic settings and fault mechanisms. However, regional variations in source parameters have not been explicitly quantified. For example, it remains unclear whether earthquakes occurring in similar tectonic environments (e.g. subduction zones) and fault mechanisms (e.g. reverse faulting), but in different regions such as Japan, South America, or Indonesia, exhibit comparable source characteristics, or how such variability should be incorporated into scaling relations. To address this gap, the present study performs a comprehensive exploratory analysis of earthquake source attributes derived exclusively from finite-fault models, including stress drop, alongside standard fault geometry and slip parameters. The analysis spans multiple groupings defined by tectonic setting, fault mechanism, seismic region, focal depth, crustal type, as well as fault-plane inversion modality and spatial resolution, which are examined to account for modelling-related variability across datasets. Stress-drop proxy and slip-parameter estimates, particularly for large magnitude earthquakes, display systematic deviations from self-similar scaling assumptions. Fault-plane modality, defined by the type of seismic and/or geodetic data used in the inversion, and fault-plane resolution, quantified by subfault discretization, are found to be associated with systematic differences in inferred slip and asperity parameters, and help explain part of the intra-event variability observed when multiple models exist for the same earthquake. These factors are therefore incorporated explicitly to isolate physical variability from modelling effects. Based on these findings, existing source scaling laws are revised using a mixed-effects regression framework. Tectonic setting, inversion modality, and fault-plane resolution are treated as fixed effects, while fault mechanism and seismic region are modelled as random and nested-random effects, respectively. The refined scaling relations provide more robust estimates of fault geometry (length, width, area, and asperity dimensions) and slip statistics (mean slip, maximum slip, and slip standard deviation), and are particularly valuable for region-specific computational source modelling and physics-based seismic hazard analysis.

SummaryThe reaction-infiltration instability has been identified as an important mechanism responsible for the formation of high-porosity melt channels in the upper mantle. To better understand this mechanism, we perform linear stability analysis and non-linear numerical simulations in a compacting, chemically reactive porous medium. Strong interactions between compaction and reaction lead to two distinct unstable features: (1) high-porosity channels and (2) compaction-dissolution waves. Here we present a regime diagram to show that, compared to high-porosity channels, compaction-dissolution waves are favoured in systems with lower reaction rate, lower compaction viscosity (i.e., more easily compactible medium), and smaller solubility gradients. This regime diagram predicted by linear stability analysis shows good agreement with the non-linear numerical simulations. Under geologically relevant conditions, both high-porosity channels and compaction-dissolution waves may form in the mantle, although channels are more commonly expected.

SummaryThe Sanriku-Oki subduction region in northeastern Japan is a tectonically active zone where the Pacific Plate subducts beneath the Okhotsk Plate, generating frequent earthquakes. In this study, we present a three-dimensional unstructured S-wave traveltime tomography model of the off-Sanriku forearc using local earthquake data recorded by ocean-bottom Distributed Acoustic Sensing (DAS). The dense spatial sampling provided by DAS enables cost-effective, high-resolution imaging of the forearc region that is difficult to achieve with conventional ocean-bottom seismometer deployments. Eight local earthquakes recorded near the DAS cable provided 209,193 high-quality S-wave arrival times after applying data-selection criteria. These earthquakes were recorded using two DAS interrogator units: the AP Sensing N5200A (70 km coverage, 5 m channel spacing) and the OptaSense QuantX (100 km coverage, 2 m channel spacing). The initial reference models were constructed on a 3D tetrahedral mesh with target cell sizes of 1.5 and 3.0 km, accurately incorporating the DAS cable geometry and earthquake hypocentres. Synthetic traveltimes were efficiently computed using the Fast Sweeping Method (FSM) and the traveltime models were subsequently refined through traveltime inversion. The resulting S-wave tomography models reveal significant spatial heterogeneity within the off-Sanriku forearc region. Low S-wave velocities (Vs ∼ 0.78–0.85km s−1) indicate shallow, unconsolidated Neogene sediments, underlain by higher velocities in the Cretaceous basement (Vs ∼ 1.2–2.4km s−1). The lower crust and uppermost mantle wedge exhibit clear along-strike segmentation of the forearc. In the landward domain, high S-wave velocities in the lower crust (4.15–4.4km s−1) and uppermost mantle (4.6–4.65km s−1) indicate a mechanically strong overriding plate and a thick, cold mantle wedge, with no evidence of significant partial melting or serpentinisation associated with subduction processes. In contrast, the central offshore region exhibits moderately reduced S-wave velocities (3.9–4.1km s−1), suggesting a mechanically weaker overriding plate. Toward the trench, S-wave velocities decrease markedly within the forearc crust, defining a low velocity zone associated with crust–crust interaction above the subducting Pacific Plate and likely reflecting fluid-rich, deformed forearc material in the shallow subduction environment.

SummarySeismic wave speed monitoring is important for the non-destructive evaluation of material properties in response to external forcing. Coda wave interferometry (CWI) uses travel time perturbations in multiply-scattered seismic wave trains – the seismic coda – to detect subtle perturbations in bulk wave speed. However, conventional body-wave CWI cannot separate the coupled contributions of P and S waves, which are sensitive to different material properties. We introduce energy partitioning inversion which decouples these modes by combining a scattering model with CWI measurements within non-equipartitioned coda windows. We applied this methodology to repeated ultrasonic pulse surveys during two laboratory loading experiments on Clashach sandstone: a dynamic experiment (constant strain rate until brittle failure) and a quasi-static experiment (modulating stress to maintain constant acoustic emission rate and slow down the failure process). Relative travel time perturbations and their full covariance between all pairs of surveys were measured across multiple coda windows and inverted for a single perturbation profile using a least-squares method to minimise the variance of the profile. Using an isotropic point scatterer model to predict mode partitioning with respect to the coda lapse time, we invert travel time perturbations for the scattering mean free path travel time and relative P and S wave speed perturbations via Markov-chain Monte Carlo inversion to quantify uncertainty. P and S wave speed perturbations were resolved with 95 % credible intervals of 0.025 % and 0.008 %, respectively. During the quasi-static experiment the temporal resolution was sufficient to capture a quasi-linear decrease in P and S wave speeds by ~ 50 % and ~ 14%, respectively, from peak to failure. The peak P and S wave speed perturbations were ~ 33% lower and ~ 75% higher, respectively, compared to those found in the dynamic experiment. These results demonstrate that CWI and energy partitioning inversion enables the robust, uncertainty-quantified evaluation of separate relative bulk P and S wave speed perturbations in strongly-scattering media.

SummaryThe rigidity and intraplate deformation of the Indian plate has long been a subject of debate. To understand the present-day intraplate deformation, we utilized data from 34 well-distributed continuous Global Positioning System (cGPS) stations across the stable part of the Indian subcontinent to derive refined Euler pole parameters (51.834° ± 0.0495° N,3.9708° ± 0.9351° E, Ω = 0.52067° ± 0.002684°/Myr) and establish an updated Indian Reference Frame (IRF). The intraplate velocity field suggests a westward motion of the Northern Tectonic Block (NTB) (up to 2.5 mm/yr) relative to the stable Southern Tectonic Block (STB) (≤1 mm/yr) along the Narmada-Son Lineament (NSL) in the Central Indian Tectonic Zone (CITZ). The rotation pattern inferred from strain analysis also exhibits a progressive increase in the anticlockwise rotation from the STB (~2 nrad/yr) to NTB (~6 nrad/yr). Thus, the subtle, yet systematic variation in the intraplate deformation pattern from STB to NTB across the NSL strengthen the non-rigid behaviour of the Indian sub-continent and reflects its accommodation of far-field plate boundary forces generated by the Indo-Eurasian collision in the Himalayan region. To further substantiate these findings, we propose to conduct advanced investigations using a denser network of cGPS stations over the Indian subcontinent.

SummaryUncertainty quantification is indispensable for reliable magnetotelluric (MT) data interpretation, given the inherent non-uniqueness of MT inverse problem solutions. However, traditional sampling-based probabilistic schemes often require millions of costly forward predictions, making them computationally prohibitive. To address this, we develop a trans-dimensional Bayesian inversion framework that incorporates a novel forward modelling operator and a reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm for robust uncertainty quantification. The forward modeling method leverages the extended Fourier DeepONet (EFDO) network. Once trained, the EFDO achieves up to a 300-fold acceleration in forward predictions compared to a conventional Finite Volume (FV)-based solver. Furthermore, we utilize an adaptive Delaunay model parameterization during the sampling process to allow for efficient model space exploration. We demonstrate the efficacy of the proposed approach through numerical experiments and application to the COPROD2 MT field dataset. Overall, this work advances Bayesian MT inversion by enabling rapid, high-dimensional inference of subsurface electrical resistivity structures, thereby facilitating reliable geological interpretation.

SummaryDistributed Acoustic Sensing (DAS) has emerged as a valuable complement to conventional seismic monitoring techniques. By converting fiber-optic cables into dense arrays of virtual sensors, DAS enables the application of standard large-array processing methods. However, its directional sensitivity—limited to strain measurements along the fiber axis—may restrict its potential for full wavefield analysis. To address this limitation, we investigate the capabilities of DAS on a fiber-optic cable installed both horizontally, near the surface, and vertically, in a borehole, thereby creating a so-called 3D-DAS array. The survey was carried out in the southern Munich region (Germany) to monitor local seismicity associated with nearby deep geothermal operations. In this study, we present the data acquisition setup and describe a processing workflow developed to characterize source and wavefield parameters of seismic events from DAS recordings. The workflow is illustrated using a nearby ${{M}_w}$ = 0.48 seismic event. Taking advantage of the configuration of the fiber optic cable, we demonstrate that the 3D-DAS array enables estimation of the wavefield back-azimuth, incidence angle and slowness, and compare these results with those provided by a local network of seismometers. In addition, seismic source parameters, including seismic moment and stress drop, are estimated from DAS data acquired in the 250-meter-deep vertical well. These parameters are derived after converting strain-rate to ground motion, a process quantitatively validated using a co-located three-component broadband seismometer. The results and waveform evaluation demonstrate that the 3D-DAS array provides reliable and comprehensive measurements, independently of the existing local seismic network.

SummaryIn many fields of geoscience, researchers study the Earth’s properties by solving inverse or inference problems. Probabilistic approaches have gained increased attention over the past decade because they address the non-linearity and non-uniqueness properties of many naturally-inspired inverse problems and allow uncertainties in the solutions to be estimated. However, implementing such methods is computationally expensive and requires expertise in inverse and inference theory, high performance computing, and the geoscientific theory to be inverted. This makes the methods inaccessible to many geoscientists. In this paper, we first review the theoretical background of a particular suite of probabilistic algorithms referred to as parametric variational inference (PVI), and introduce GeoPVI, an open-source Python package designed to facilitate the implementation of these methods. With GeoPVI, users can model uncertainties in their geophysical parameter estimates efficiently given their expertise in inverse theory. It differs from sampling-based, non-parametric variational methods in that the probabilistic solution – the posterior or post-inversion probability distribution function that describes uncertainty in the model parameters of interest – is parametrised by explicit mathematical expressions. These expressions allow for the efficient storage and transfer, and for the evaluation of the posterior probability density for any set of parameter values. We demonstrate how to use the package to solve a set of problems, including tomographic imaging using travel time data, full waveform inversion, surface wave dispersion inversion, and vertical electrical sounding. We provide built-in forward functions to simulate first arrival travel times and full acoustic waveform data (in two spatial dimensions), and external forward functions can be incorporated into the package easily. We also demonstrate how to change prior information efficiently post-inversion, using the method of variational prior replacement. Contributions from the community are welcome, to make the package more broadly applicable.

SummaryElectrical resistivity tomography (ERT) is a widely used and effective tool for hydrogeological investigations. Conventional ERT inversion approaches are based on gradient-based algorithms, which typically provide deterministic optimal solutions, which are subject to uncertainty. Such uncertainty could have significant impact on hydrogeological interpretation using ERT. Model appraisal is a critical step after inversion, however, conventional appraisal methods are qualitative and thus subjective. To address these limitations, this study introduces a probabilistic variational inference (VI) method, referred to as Stein variational gradient descent (SVGD), to quantify both resistivity distributions and associated uncertainties in ERT inversions. Synthetic examples are conducted to investigate the effects of configurations and noise, and to compare the performance of SVGD with conventional inversion and model appraisal techniques. A field case study and its model validation are also presented to demonstrate the practical advantages of uncertainty quantification in field. The results indicate that SVGD can effectively reduce artifacts introduced by regularization and provide more comprehensive quantitative insights into subsurface structures compared to conventional approaches. The study also reveals limitations in the interpretation of basic statistics of uncertainty estimates, highlighting the need to examine the entire posterior distributions of parameter values. Additionally, this study demonstrates that the final uncertainty arises from a trade-off among multiple factors, such as geometry of subsurface structures, measurement techniques and data noise levels. Finally, we also discuss some comparisons with other probabilistic frameworks in hydrogeophysics, highlighting its potential to improve uncertainty and probability quantification in ERT, and possible future developments in hydrogeophysical coupled inversion.

SummaryUnderstanding the internal structure of the Earth is achieved using geophysical data and inversion is a powerful mathematical technique used by resource explorers to do so. Inherent ambiguity means that an infinite number of petrophysical models exist that can explain the geophysical data, so constraints such as geological models and petrophysical data have been employed to reduce the solution space. The constraints, like the data, are subject to noise and error, resulting in uncertainty propagating to the final model because inversion is designed to use the algorithm and constraints to find the single ‘best’ solution. Current practice assumes the best solution is found by optimising for the lowest misfit between the data and model; however, if the data is uncertain, the model fit to that data is likewise uncertain and potentially misrepresentative. Optimising misfit also means that inversion is subject to overfitting. Overfitting occurs when a model achieves the lowest misfit values by inadvertently fitting to data noise. Overfitting inversion occurs when the model has too many free parameters with no constraints, resulting in near-surface anomalies that can be mistakenly identified as legitimate targets for exploration rather than model artefacts. This contribution describes the use of spatial uncertainty calculated from geophysical data, providing free parameter constraints to reduce overfitting for geophysical inversion. The spatial uncertainty estimate is taken from a geostatistical model calculated using Integrated Nested Laplacian Approximation (INLA). A region in the East Kimberley, northern Western Australia, is subject to gravity inversion using Tomofast-x, an open-source inversion platform. Inversion is conducted using different configurations. Inversion is run without spatial uncertainty constraints, as is current practice, and then with spatial uncertainty constraints to test their effect on the resulting petrophysical model. The geostatistical model offers different percentiles from the geophysical model representing the extrema of estimated gravimetry values in the 10th and 90th percentiles. Inversions are run using these ‘extrema’ alongside the current practice of using the 50th percentile (or ‘mean’) gravity models as the observed field. Examination of inversion using and not using spatial uncertainty constraints shows that overfitting can be reduced. Using the extrema percentiles as the observed field has lesser benefits to reduce overfitting.

SummaryThe Earth’s ancient magnetic field is challenging to constrain from the rock record in large part due to the presence of non-ideal magnetic recorders in addition to processes, like alteration, that affect the ability of a material to reliably record field strength. Of the magnetic minerals present on Earth’s surface, magnetite is one of the most commonly used to simultaneously recover palaeomagnetic direction and intensity. Recent work on archaeological artifacts and clinker deposits (sedimentary rocks baked by coal seam fires) has identified a potential new mineral capable of recording the full-vector magnetic field: ɛ-Fe2O3, a high-T metastable phase of hematite. The palaeomagnetic potential of ɛ-Fe2O3, specifically regarding palaeointensity, has not been studied in depth. Further, recent work on synthetic ɛ-Fe2O3 has raised questions about the reliability of this phase for palaeointensity recording. To understand whether ɛ-Fe2O3 is a trustworthy full-vector magnetic recorder, more work is needed to assess this phase in its natural form. Here, we present results from Thellier-style palaeointensity experiments using a lab-induced thermoremanent magnetization (TRM) on natural ɛ-Fe2O3 present in Quaternary age clinker samples from the Custer National Forest, Montana, USA. The experimental setup was designed in attempt to isolate the ɛ-Fe2O3 phase from other magnetic carriers. The results of our study suggest that natural ɛ-Fe2O3 can reliably record palaeointensity and palaeodirections, yielding palaeointensity estimates within 5% and directions consistent with the applied laboratory TRM field. These new results suggest that ɛ-Fe2O3 bearing artifacts and clinkers can be robust full-vector magnetic recorders. Overall, this study adds confidence to previously obtained archaeomagnetic data and to a novel palaeomagnetic recorder, clinkers, opening the door to a more detailed characterization of the recent field.

SummaryContinental collision is prevalent along the Tethyan tectonic belt, characterized by diverse deformation patterns across regions, including concentrated deformation in the Alps, integral deformation throughout the Tibetan Plateau, and separate deformation within the Iranian Plateau. However, the mechanisms governing the diversity of deformation in different collisional orogens along the Tethyan tectonic belt remain poorly understood. Accretion of continental terranes during the closure of the Paleo- and Neo-Tethys oceans generated a highly heterogeneous lithosphere along the southern margin of Eurasia, a crucial factor in interpreting continental deformation. This study employs 2D thermo-mechanical numerical modeling to assess how tectonic inheritance-induced rheological heterogeneities govern deformation patterns in continental collision orogens. Our simulation results reveal three end-member deformation patterns resulting from variations in the rheology of the upper plate within the collision system. When the upper plate is uniformly strong, it prevents deformation from propagating into the interior of the continent, resulting in concentrated deformation in the collision front. If the upper plate is uniformly weak, deformation occurs throughout the entire upper plate, resulting in an integral deformation pattern. When a rheologically weak block is embedded in the strong upper plate, deformation concentrates in the collision zone and the weak block, resulting in separate deformation within the upper plate. Changes in the rheology of the bounding plates, the convergence rate, and the total convergence amount would not alter the basic deformation pattern of the continental collision system, if the rheology of the upper plate remain unchanged. Based on our simulation results, we suggest that the rheological characteristics of the upper plate govern the deformation patterns in continental collision systems. Our simulation results provide first-order explanations for the observed diversity of deformation in different continental collision systems along the Tethyan tectonic belt.

SummaryThe dispersion of Scholte waves provides a fundamental basis for inverting shallow seafloor elastic parameters. With the expansion of marine exploration, an isotropic seabed approximation has become increasingly inadequate. Therefore, in this study, Scholte-wave dispersion was analyzed in vertically transversely isotropic (VTI) media and the sensitivities of key parameters were quantified. Using a reduced delta-matrix formulation, a numerically stable dispersion equation for fluid-solid-coupled VTI media was derived and validated with elastic wavefield modelling and frequency-velocity spectra. Sensitivity tests on three representative seabed models [velocity increasing with depth (VID), a low-velocity layer (LVL), and a high-velocity layer (HVL)] show that anisotropy amplifies phase-velocity sensitivity to P-wave velocity (VP), especially for higher modes. In contrast, sensitivities to Thomsen parameters ε and δ are secondary but non-negligible. As mode order increases, the sensitive frequency band broadens and penetrates to greater depths. For the HVL model, dispersion is particularly sensitive to the overburden above the high-velocity layer. By contrast, for the LVL model, sensitivity concentrates within the low-velocity layer itself and above it. These sensitivity patterns reflect the influences of different parameters on inversion results and support the development of dispersion curve inversion for anisotropic shallow seafloor.