Geophysical Journal International

SummaryWe revisit the challenge of estimating velocities from global navigation satellite system (GNSS) coordinate time series by incorporating stochastic processes to address the quasi-periodic oscillations (QPOs) in weighted least-squares estimation. We examine two advanced stochastic seasonal models (i.e., Fractional Sinusoidal Waveform (FSW) and Varying Periodic Band-Pass (VPBP)) and evaluate their efficacy on mitigating velocity biases by using both synthetic data and 14 Antarctic GNSS stations. The result shows that pure FSW cannot fully capture the spectral shape of the QPOs since the FSW's spectral shape is controlled by the fractional parameter d, which is unfortunately dependent on the global spectra slope of the GNSS data. In contrast, VPBP can fit the QPOs more freely by using the parameter phi. Nevertheless, both FSW and VPBP models, when used without augmentation, tend to underestimate velocity uncertainties due to spectral flattening at low frequencies. This poses a risk for applications requiring high-confidence geodetic velocity estimates. To solve this issue, adopting a hybrid noise model that is compensated with an additional appropriate noise background (e.g., power law) is recommended. This knowledge can guide future research on secular velocity estimation of GNSS stations.

SummarySpectral induced polarization (SIP) aims to characterize geological materials by measuring the dispersion of their complex conductivity in the frequency domain. Despite the complex-valued nature of SIP data, most machine learning models used for its analysis rely on real-valued representations that discard phase information and may limit performance. This study investigates the benefits of complex-valued neural networks (CVNN) for SIP applications by comparing their performance against real-valued neural networks (RVNN) across three tasks: mineral classification, Cole-Cole parameter estimation, and mechanistic modelling of ionic and electric potential perturbations around polarizable minerals. To ensure fair comparisons and emphasize the effect of complex-valued representations, we design CVNN and RVNN models with matched capacity, aspect ratio, and training duration. Our numerical experiments show that CVNNs consistently outperform RVNNs in the classification task, achieving lower validation loss and up to 5 percent higher classification metrics (p-value = 2.9 × 10−7). We test the Cole-Cole inversion networks on laboratory SIP measurements and validate the parameter estimation accuracy using synthetic data. Test results indicate that CVNNs produce curve fits that are ≈4 % more accurate for the imaginary part of resistivity (p-value = 3.1 × 10−4), and validation results show accuracy improvements of up to 2 percent for chargeability, relaxation time, and the Cole-Cole exponent (p-value = 1.7 × 10−7). CVNNs also yield more accurate approximations of mechanistic model variables, with error reductions of up to 1 percent for ionic concentrations (p-value = 1.6 × 10−4). Our experiments suggest that CVNNs provide modest but statistically significant benefits in SIP applications involving laboratory or synthetic data. While RVNNs may eventually reach comparable predictive accuracy if trained longer, we observe that CVNNs converge more rapidly under matched training conditions. This study provides a reproducible framework for benchmarking neural network architectures in SIP and supports the integration of CVNNs into geophysical workflows where phase responses encode physically meaningful information.

SummaryDistributed Acoustic Sensing (DAS) is a recent technology that turns optical fibers into multi-sensor arrays. In the marine environment, it offers new possibilities for measuring seismic and environmental signals. While DAS can be applied to existing fiber optic cables used for communications, a major limitation of such efforts is that the position of the cable is not always known with sufficient accuracy. In particular, for submarine telecommunication cables, the positioning accuracy decreases with increasing depth. This problem affects the accuracy of earthquake locations and source parameters based on DAS signals. This limitation calls for methods to retrieve the cable’s position and orientation. Here, we propose a method for relocating a linear section of cable “or multiple connected segments” using incidental acoustic sources, particularly boats moving in the vicinity of the cable. The method is based on Target Motion Analysis (TMA) for sources in uniform rectilinear motion. We consider Bearing-Only TMA (BO-TMA) and the Bearing and Frequency TMA (BF-TMA), which respectively use changes in back azimuth (called bearing in navigation) and changes in both back azimuth and Doppler frequency shift as the source moves. We adapt these methods to the 3D case to account for the difference in depth between the fiber and the sources. Both cases lead to a non-linear inverse problem, which we solve by the Levenberg-Marquardt method. On synthetic data, we test both TMA techniques on single and multiple source trajectories and evaluate their accuracy as a function of source trajectory and velocity. We then test the BO-TMA on real DAS recordings of acoustic signals produced by passing ships near a 42 km-long fiber optic cable off the coast of Toulon, southeastern France. In this study case, the position and characteristics of the acoustic source are known. While the Doppler frequency shift at low frequency (30 Hz) is difficult to measure with sufficient accuracy (<0.1○), we demonstrate that effective cable location can be achieved by BO-TMA using multiple ship passages with a variety of trajectories. Once the linear sections of the cable have been relocated, the stage is set to reconstruct the entire cable configuration. More generally, the three-dimensional TMA on linear antennas developed here can be used to locate either the sources or the antenna situated at different depths.

SummaryWe present a full-wave inversion algorithm (FWI) to accurately delineate the subsalt body using seismic borehole data. This ill-posed inverse problem is constrained by introducing geological a priori information through the parameterization of the salt boundary using a level set function. The implicit level set function is spanned by a set of B-spline basis functions for their ability to represent a wide range of shapes. Furthermore, the proposed FWI algorithm combines a meshed discretization with the implicit representation of shapes throughout the inversion process. A weak deformation of the mesh is applied at each iteration of the inversion to maintain the explicit discretization of the shapes when the level set boundary is updated. This method is very accurate when it comes to modelling the scattered wavefields and computing the Fréchet derivatives at interfaces. Three numerical examples using synthetic borehole seismic data illustrate the ability of the method to accurately retrieve the size, location and shape of the salt body when the density and seismic velocities are known.

SummaryFractures in reservoirs are potential conduits for fluid flow. Therefore, it is crucial to know to what extent fluid flowing through a fracture could be lost by seepage to its porous background. For this reason, the hydraulic contact between the porous background and the fracture should be characterized, ideally based on seismic reflections. The representation of a fracture as a thin porous layer can provide insight into this seepage from a dynamic poroelasticity perspective. This is possible because the seismic waves reflected from a fracture are partially converted into the slow P-wave, which is the fluid motion relative to the solid-frame, and are sensitive to hydraulic contact being sealed or leak. It is well known that the P-wave reflectivity of fractures exhibits a marked difference between sealed and leaky cases for small angles of incidence (below twenty degrees) because of the variation in conversion scattering to slow P-wave. Drawing from a recent finding that a vertically polarized shear wave (SV-wave) can also generate a robust slow P-wave, we analyze the SV-wave reflectivity at fractures that can be hydraulically connected or disconnected from the surrounding porous medium, with the aim of advancing fracture characterization. We find that the reflectivity of the SV-wave is sensitive to fluid seepage, particularly at larger incident angles (above thirty degrees) where the amplitude is diminished substantially. Therefore, SV-wave reflectivity can also be used to identify leaky fractures, complementing the information provided by P-wave reflectivity.

SummaryEarthquake early warning systems are designed to provide critical seconds of warning before strong ground shaking, facilitating emergency mitigation efforts. Existing methods, such as neural networks and ground motion prediction equation-based approaches, rely on manually defined parameters and physics-based computations, which introduce human bias and hinder the efficiency of real-time applications. Furthermore, current studies primarily focus on scalar metrics such as peak ground acceleration and peak ground velocity to evaluate earthquake impacts. These metrics are limited to measuring ground shaking intensity and fail to capture the spectral characteristics of ground motion. Therefore, a ground-motion and structural-oriented deep learning-based model is proposed to predict uniform hazard spectral acceleration values across 111 periods ranging from 0.01 to 20 seconds. The framework is initially trained and evaluated on 17,500 ground-motion records from the crustal Next Generation Attenuation West 2 project. Spectral acceleration values are predicted by two subsets: deep learning-based uniform hazard spectral acceleration models 1 and 2. These models effectively utilize feature information from the initial seconds of seismic waveforms, eliminating the need for empirically defined parameters. Two deep learning-based models are developed for two datasets representing two distinct broad geographical regions. Both models utilize a similar deep-learning architecture but vary in input settings and hyperparameters to account for regional seismic characteristics. To assess the model's goodness-of-fit between observed and predicted values, as well as its generalization ability, we rigorously compare the two models with the latest data from the U.S. Geological Survey Earthquake Hazard Toolbox and the Japanese Strong-Motion Earthquake Network, respectively. An explainable artificial intelligence technique has been applied to better understand the framework and analyze how individual input features influence the outputs of the trained models. Integrating cutting-edge deep learning technologies into ground motion and engineering seismology reveals the significant potential of the model in enhancing real-time early warning systems. This integration also provides valuable support to various end-users involved in seismic monitoring, facilitating well-informed decisions in both real-time and near-real-time scenarios.

SummaryWe demonstrate a method for the prediction of seismic discontinuity topography from thermochemical Mantle Circulation Models (MCMs). We find the discontinuity depth by using the peak reflectivity at each location in our mantle transition zone, taking account of compositional as well as thermal variations. We make some comparisons of our predicted topographies with those observed using SS-precursors, developing a simple smoothing filter to capture the distribution of sensitivity of a published topography model – finding that such filtering has a significant impact on the predicted discontinuity topographies. We also consider the significance of lateral variations in reflectivity or reflection amplitude in our predicted datasets and the real Earth. Finally, we consider what aspects of mantle-transition zone discontinuity structure would be matched by the predicted discontinuity structure from an Earth-like MCM – particularly the mean depths of the discontinuities, the amplitude of the topography and the shape of its spherical harmonic spectra.

SummaryA joint tomographic inversion for high-resolution P and S wave velocity models of the crust and uppermost mantle in the Middle East is performed using absolute and differential body wave travel times as well as Rayleigh wave dispersions from earthquakes and ambient noises. Checkerboard tests indicate that the models generally have a resolution of 2° x 2° down to a depth of 100 km and reaches 1° x 1° at a depth of 60 km in areas of high-density data coverage such as the Zagros collision zone. The velocity models reveal that the sedimentary layer in the region is nonuniform with a maximum thickness in the Mesopotamian foreland, Persian Gulf, southern Caspian Sea, and eastern Mediterranean Sea (∼10 km), whereas most of the Arabian Shield has no sedimentary cover. The Moho discontinuity vary considerably beneath the Arabian Plate with its shallowest extent at the Red Sea Rift (∼10 km) and its deepest under the Zagros collision zone (∼50 to 55 km). The Arabian Shield and Arabian Platform have a relatively uniform Moho depth of ∼40 km. Widespread low velocity anomalies in the upper mantle are found along the margins of the Arabian Plate and mountain ranges of the Anatolia and Iran plateaus which coincide with the Quaternary volcanism in the region. Extensive low velocity anomalies are observed in the upper mantle underneath the southern and central Red Sea Rift and the Arabian Shield, which may represent partial melt or upwelling hot asthenosphere material from the Afar plume or East African superplume. The southern Red Sea is in an active rifting stage driven by the upwelling of the asthenosphere, whereas the northern Red Sea is in a hybrid mode of active and passive rifting. The Arabian Plate drift toward the northeast is likely the driving force for the passive rifting. In the Zagros collision zone, crustal thickening with low velocity anomalies in the upper and mid crust is observed. This suggests that the present-day tectonic framework of the Zagros collision zone is the result of oceanic subduction of the Neotethyan Plate under the Eurasian Plate and subsequent continental collision of the Arabian Plate with the Eurasian Plate, during which the lower-velocity felsic upper crust of the Arabian Plate was dragged down under the higher-velocity mafic crust of the Eurasian Plate due to slab pull. The subducted slab has a diversified form with a torn-off central portion. The southern portion slopes steeper than its northern counterpart. The subducted Neotethyan slab likely underwent bending and tearing, and it eventually broke off. The remanent slab underplated to the overriding Eurasian Plate to form a thickened crust under the Zagros orogen. This study corroborates previous findings such as there being different modes of spreading in the northern and southern Red Sea rift and the presence of crustal thickening in the Zagros collision zone, and it unveils more details including asthenosphere material migration along the Red Sea rift and complex suture structure in the Zagros collision zone.

SummaryTimely identification of the triggering mechanism behind the observed seismicity in areas with multiple overlapping human activities is an important research topic that can facilitate effective measures to mitigate the seismic hazard. This task is particularly challenging when dealing with delayed operational data, uncertain focal depths, or uneven seismic monitoring coverage. Here, we propose a deep learning (DL) framework to identify which human activity triggered a certain earthquake in near real-time using only seismic waveforms as input. We use an advanced architecture, the compact convolutional transformer (CCT), to extract high-level abstract features from the three-component seismograms and then use an advanced capsule neural network to link the induced seismicity in West Texas with three potential causal factors, i.e., hydraulic fracturing (HF), shallow saltwater disposal (SWDsh), or deep saltwater disposal (SWDdp). The training data was prepared based on an established probabilistic approach that combined physics-based principles with both real and reshuffled injection data to hindcast past seismicity rates. In the end, each activity was assigned a confidence level for association at the 5 km spatial scale. Even though the training data include only 981 events, we obtain over 90% accuracy for all three causal factors for both the single- and multi-station versions of the model.

AbstractIntradecadal variations in the length-of-day (ΔLOD) can reveal changes in angular velocity interpreted as due to Earth’s core. Previous studies have identified periodic oscillations of around 6 and 8 years. To complement widely used Fourier methods, we investigate the ΔLOD record from 1962-2025 in the time domain, seeking smooth variations using cubic B-splines. We analyse in several ways. A penalised least-squares spline fit allows isolation of coherent variations from analysing the first and second derivatives. Alternatively, a smooth curve fit with least-squares splines allows removal of the long-period behaviour of ΔLOD. From this, we fit the residual with a pure cosine-wave of varying period but examine the data fits carefully in case the signal is non-stationary (for example from impulsive forcing). All approaches show clear evidence of signals with periods around 5.9 — and in the case for the time derivatives — 8.5 years. We find that the pure 5.9-year oscillation breaks down in 2010, with a one-off peak to peak separation of around 4.7 years. After 2014, the variation is once again consistent with an approximate 6 year oscillation. Such a discontinuous, non-stationary effect is not well-characterised by frequency-domain based methods. Seeking to understand this brief interruption of the 6 year oscillation, we extend the study length using a ΔLOD series from lunar occultation data extending back to 1800, and find it suitable to repeat our spline-based analysis from 1830 onwards. From this, we find the 6 year oscillation stable throughout the entire 19th and 20th century, with the exception of 1916–1920, where we observe a similar interruption of the 6 year variation by a single 4 year oscillation. The 2010 disruption to the 6-year oscillation is contemporary with changes in geomagnetic secular variation, modelled core surface flow, and inner core seismic signature. All of these events suggest a step change in core-processes around 2010.

SummaryThe time-variable gravity field obtained from the Gravity Recovery and Climate Experiment/Follow-On (GRACE/GRACE-FO) satellites has been successfully used to detect global water mass changes over the past two decades. However, the north-south striping noise in the GRACE spherical harmonic (SH) solution limits their effectiveness. Efforts to suppress this noise and achieve a higher signal-to-noise ratio (SNR) continue with various product releases, but there is still a great need for improvement. This study presents a new de-striping method called GBVMD, which employs a stepwise enhancing framework combining Gaussian filtering with bi-dimensional variational mode decomposition (BVMD). The methodological breakthrough comes from two innovations: First, it employs adaptive scale decomposition by dynamically adjusting the radius of the Gaussian filter in conjunction with BVMD reconstruction, effectively reducing noise across multiple scales. Second, it features a dual-decision optimization strategy that integrates SNR-driven mode reconstruction and iterative termination, thereby maximizing the SNR while adapting to the specific characteristics of the noise. In simulations, the GBVMD outperforms the five other filters in reducing noise and keeping signals, achieving an improvement in SNR by at least 19%, and reductions in root mean square error and mean absolute error by at least 14% and 11%, respectively. When applied to GRACE/GRACE-FO Level-2 SH solutions, GBVMD led to a higher SNR with an improvement of at least 12% compared to other filters. The GBVMD-filtered SH data showed strong consistency with three Level-3 Mascon solutions across 183 river basins. Comparable results were also found in polar regions, validated by altimetry data. Furthermore, we effectively corrected the leakage errors for two examples in the Caspian Sea and the Great Lakes, demonstrating the advantages of GBVMD-filtered SH over the Mascons for signal reanalysis. We recommend GBVMD for further applications, especially in specific regions such as ocean areas and other satellite missions requiring similar de-striping approaches.

SummaryDue to the extremely destructive characteristics of seismic hazards, and as one of the effective pre-seismic physical signals, abnormal changes in subsurface fluids can provide key precursor information for earthquake prediction. Furthermore, an efficient method for labelling anomalies in seismic monitoring data is urgently needed. Therefore, this paper analyses the change characteristics of subsurface fluid-water level data and proposes an Anomaly Detection Algorithm of Single Variable Time Series Data Based on Dynamic Parameter Tuning (ADSV-DPT) based on three important characteristics (jump, step and steep), which firstly determines the central tendency of the data by calculating the median of the water level data within the initial window, and then utilizes the Median Absolute Deviation (MAD) as a robust dispersion metric to reduce the impact of extreme values on the anomaly detection. The sliding window mechanism is employed to update the median and MAD step by step, thereby ensuring the efficiency and adaptability of the algorithm in processing time series data. Finally, the anomalies in the data are detected by setting dynamic thresholds. A comparison of the anomaly detection efficacy of the proposed ADSV-DPT algorithm with that of three alternative models (namely, K-Nearest Neighbor, KNN; Pruned Exact Linear Time, PELT; and One-Class Support Vector Machine, OC-SVM) was conducted. The experimental results demonstrate that the ADSV-DPT algorithm outperforms the other models in accurately identifying anomalous features. The average precision, recall, and F1-score of the ADSV-DPT algorithm all exceed 85%. The algorithm's capacity for adapting to variations in the data is noteworthy, as is its ability to accurately identify abnormal values that deviate from the established normal range.

SummaryThe quantitative interpretation of geological structures relies on multi-parameter models (MPMs) inversion. However, conventional full waveform inversion that matches simulated seismic data to observed seismic data cannot accurately obtain high-resolution MPMs because of the implicit inter-parameter coupling relations in the multi-parameter wave equation. Additionally, conventional supervised deep learning approaches that require a significant number of annotated labels cannot predict precise MPMs, as only a limited number of sophisticated synthetic MPMs are available as labels. To address this issue, we propose a self-supervised multi-parameter inversion (SS-MPI) to provide high-resolution MPMs from the prior first-arrival-based tomography and reflection-based migration image. SS-MPI creates representative MPMs from the prior information as pseudo-labels to pre-train the deep learning algorithm, which then predicts MPMs as feedback to update these training pseudo-labels iteratively. Synthetic examples of elastic and anisotropic models indicate that SS-MPI outperforms the conventional elastic full waveform inversion (EFWI) and delivers highly accurate and high-resolution MPMs.

SummaryPermafrost degradation in the Arctic is both an indicator of, and contributor to warming global temperatures. In addition to the global impact of thawing permafrost, at the local scale permafrost degradation can result in infrastructure damage, ecosystem changes, chemical and microbial releases, landfill leaks, and river bank erosion. As such, accurate mapping and monitoring of the in-situ permafrost extent is crucial. Differentiation of frozen and thawed arctic soils is commonly achieved through electrical geophysical imaging methods; however, recently a hysteresis-like phenomenon was observed in the electrical conductivity of soils undergoing freezing followed by thawing. This phenomenon can result in over an order of magnitude difference in conductivity when measured on the same sample at precisely the same temperature. Here, we explore this effect in a clean sand with low surface conduction and a frost-susceptible clay-rich soil with relatively higher surface conduction. The unsaturated samples were prepared at a moisture content of 16.5 per cent by weight. Each soil was evaluated under four different NaCl pore-fluid conductivities 10 ppm, 100 ppm, 1000 ppm, 1000 ppm. We then developed a model to simulate the full hysteresis loop for the soil. In both cases the hysteresis effect was clearly observed in the real component of the conductivity. In the frequency dependent imaginary conductivity response only the frost-susceptible soil demonstrated consistent signs of hysteresis along with a soil-specific frequency response.

SummaryDownward continuation is a very interesting approach to enhance the information content of potential field data. However, the calculation of the downward continuation represents a fundamental challenge due to its inherent instability. In this study, a strategy to perform high-order vertical derivatives using the β-VDR method is introduced, called the generalized β-VDR method. Testing on a noisy synthetic model shows that the proposed strategy has the lowest noise compared to other methods. Based on stable vertical derivatives computed by using the generalized β-VDR method, a stable downward continuation method is also presented to enhance the information content of potential field data. The applicability of the generalized β-VDR downward continuation algorithm is demonstrated on both synthetic and real field gravity anomalies and compared to other downward continuation algorithms. In the case of synthetic examples, the proposed method provides sharper images and estimates more accurate amplitudes than other algorithms, even continuing the field to a level close to causative bodies. The real application shows that the proposed algorithm can give a meaningful result that agrees well with seismic data along a profile in the area.

SummaryThere are two fundamental probabilities in the seismic phase picking process – the probability of the existence of a seismic phase (detection probability) and the probability associated with the phase arrival time estimation (timing probability). The nearly ubiquitous approach in developing deep learning phase picking models is to use a kernel, such as a truncated Gaussian, to mask the labeled phase arrival time and train a segmentation model. Once a model is trained, the times of the peaks in the output are taken as phase arrival times (picks), and the height of the peaks are taken as “probability” of the picks. Here, we show that this “probability” represents neither the detection nor the timing probability because this approach forces the output to follow the shape of the kernel. We introduce an approach using two models to estimate these two distinct probabilities. We use a binary classifier with a calibrated confidence to address the detection probability and a multi-class classifier to obtain a probability mass function to address the timing probability. This new approach can make the deep learning-based phase picking process more interpretable and provide options to logically control seismic monitoring workflows.

SummaryThis study introduces a new method for calculating acoustic-gravity waves in a spherically layered atmosphere. The method introduces a model assumption and divides the atmosphere into finely stratified layers to solve the PDE with respect to the radial coordinate. The time-domain synthetic signal is obtained by summing over the orders of the associated Legendre functions and then applying the FFT. The method is applied to numerically simulate wave behaviour, including Earth curvature effects, and compares with the horizontally layered model (HLM). Results show that at near-field distances, our method aligns closely with HLM, but significant differences emerge in the far field, particularly beyond an epicentral distance of 50°, where Earth curvature becomes critical. Our method successfully simulates head waves of seismic phases, and Rayleigh waves, even for waves travelling multiple times around the Earth, which HLM cannot achieve. Simulations using a homogeneous Earth model reveal head wave characteristics consistent with previous studies, with the strongest energy observed in Rayleigh head waves. The application of the AK135 Earth model highlights the visibility of seismic phases through the Earth’s core. We validate our method by comparing synthetic records with actual data from the 1999 Chi-Chi earthquake. The synthetic records show good agreement with observed seismic signals and ionospheric perturbations in terms of arrival time and wave envelope. These results demonstrate the accuracy of our method in simulating acoustic-gravity waves at large epicentral distances.

SummaryThe widespread, multi-year crustal deformation induced by megathrust earthquakes (Mw8+) is primarily controlled by the combined effects of continuous aseismic slip on the fault plane (afterslip) and viscoelastic relaxation driven by coseismic stress perturbations in the upper mantle. However, till today it remains a considerable challenge to separate these two mechanisms in geodetic observations. We derived the first 3-year GNSS observations following the 2021 Chignik Mw8.2 earthquake to investigate the mechanisms of postseismic deformation. We established a model capable of simultaneously simulating afterslip and viscoelastic relaxation, and constrained the upper mantle rheology beneath the Alaska Peninsula. The best-fit model effectively reproduces the GNSS observations and reveals a notable viscosity difference between the mantle wedge and the oceanic asthenosphere, with steady-state viscosities of $3 \times {{10}^{18}}$ Pa s and $4 \times {{10}^{19}}$ Pa s, respectively. The inferred mantle wedge viscosity beneath the Alaska Peninsula is lower than the values reported for south-central and southeastern Alaska, suggesting an eastward increase in viscosity along the subduction zone. Two main patches of afterslip are identified during the first 3 years. The patch of up-dip afterslip overlaps with the 1938 Chignik Mw8.3 earthquake rupture zone, and demonstrates a close spatial correlation with the slow slip event in 2018. The above new results enhance our insights into the spatial variability of regional rheology and slip behavior along the Alaska-Aleutian subduction zone.

SummaryNew Q (1/attenuation) models of the Central Alpine Fault provide unprecedented resolution to 20 km depth by incorporating new t* measurements from dense temporary seismograph deployments in the area. The models reveal significant heterogeneity in the crust, with the main Q features broadly similar along-strike the Alpine Fault but varying at length scales of 10-30 km. Accounting for heterogeneity is an important step towards understanding the seismic cycle of M7+ Alpine Fault earthquakes. Our models show the Alpine Fault as a southeast-dipping zone of very (<300) to moderately (600-900) low Q, contrasting sharply with high Q values (Qp>600, Qs>1000) within the Western Province bedrock and high Q values (Qp∼900, Qs∼1200) associated with uplifted Alpine schists to the east. The wealth of previous geologic and geophysical studies along this section of the Alpine Fault support a detailed interpretation of the observed Q values. We interpret the low Q values along the Alpine Fault as resulting from enhanced fracturing within the brittle crust with a proportion of these fractureslikely filled with fluids, which further enhance seismic attenuation through viscous dissipation. In the ductile crust (below ∼8 km depth), low Q values (<400) are likely predominantly caused by grain-size reduction from very high total shear strain and by small amounts of metamorphic fluids. Low Q values of 200-400 at 20-40 km depth downdip of the Alpine Fault and the generally low Q (<600) within the crustal root farther from the Alpine Fault, suggest increasing role of metamorphic fluids relative to that of grain-size reduction with depth and distance from the fault. The updated model also reveals a newly identified zone of low Q east of the Main Divide, approximately 40 km southeast of the Alpine Fault trace. This zone of low Q indicates significant strain accumulation on faults striking along the eastern flank of the Southern Alps, some of which have produced M6+ earthquakes in recent history. These faults represent a considerable seismic hazard for the South Island. The improved dataset and recent velocity models from temporary deployments also allow us to investigate the influence of the initial velocity model on the resulting t* measurements and Q models.

SummaryThe bulk component of the electrical conductivity of a porous material is related to the (connected) porosity and saturation by power-law functions defining the first and second Archie's laws. Recently, it was shown that for porous materials with fractal characteristics, the power-law exponent of Archie's law could be related to the fractal dimension of such materials. Similarly, the real and imaginary parts of the complex-valued surface conductivity are not just proportional to the specific surface area and saturation of the material but to power law functions of these properties defining two additional “interfacial” Archie's laws, which are called the third (saturated case) and fourth (unsaturated case) Archie's laws in this paper. These new laws have been poorly recognized and studied so far. A number of porous materials and especially clay-rich media are multiscale materials characterized by broad distributions of particle and pore sizes. We extend Archie's laws concept to describe the complex conductivity of such materials. We use both numerical simulations in fractal porous materials as well as published experimental datasets to propose a unified physical interpretation of the exponents entering the four Archie's type power-law relationships, which offer an updated complex conductivity model for natural porous media.