Geophysical Journal International

SummaryPredicting seafloor topography (ST) from altimetry-derived gravity data is an effective method for obtaining ST in sea areas with sparse bathymetry. Classical ST inversion methods primarily utilize gravity anomaly, whereas vertical deflection (VD)—a fundamental product of altimetry that exhibits greater sensitivity to high-frequency ST is infrequently employed. We propose an iterative method for optimization to predict ST using VD in the spatial domain, which addresses the major problem—high nonlinearity between VD and ST. It considers the Airy-isostatic compensation and removes the non-topographic components while preserving short-wavelength signals. Our method predicts the optimal ST by iteratively minimizing the squared 2-norm of the weighted residual vector between the forward-modelled and observed VD. A synthetic test conducted in a part of the South China Sea preliminarily validates the method’s effectiveness. A real-data experiment in the Arctic Ocean shows that the root-mean-square (RMS) of differences between the ST_VD model constructed using our method and checkpoints is 110.43 m, representing improvements of 6.45, 18.85, and 13.95 per cent over the topo_27.1, ETOPO1, and IBCAO V3, respectively. Accuracy verification in different depth ranges and profile analysis indicate that ST_VD exhibits significant advantages in shallow depth (≤2,000 m), while it is relatively inferior in deep depth (>2,000 m). Radial power spectra reveal that ST_VD possesses higher energy at short wavelengths (less than ∼10 km), and its energy at intermediate-long wavelengths is consistent with the comparison models. The results demonstrate our method can effectively recover detailed ST in shallow areas and enhance the short-wavelength ST.

SummaryMagnetite-apatite (MtAp) deposits have attracted considerable attention due to their complex genesis and economic importance. These deposits are rich in magnetite ore and can bear significant rare-earth elements, but their exact formation mechanisms remain controversial. This study aims to understand the formation processes of MtAp deposits by investigating the role of iron-rich magmatic liquids. Focusing on the El Laco deposit, northern Chile, we follow the hypothesis that iron-rich liquids separate from silicate magma through liquid immiscibility. Building on previous research, this study employs a three-phase one-dimensional (1D) mechanical model to simulate the separation and accumulation of immiscible iron-rich melts within increasingly crystalline magma. The model reproduces the previously suggested transition from isolated droplet settling to an interconnected drainage network and quantifies the relative efficiency of both modes of phase separation. Using scaling analysis, we define porous, mush, and suspension flow regimes and construct a regime diagram for three-phase flow. The results indicate that the separation efficiency of immiscible iron-rich melts is maximised in the mush flow regime at intermediate crystallinity. The model-derived accumulation rate of iron-rich melts can be used to estimate the time required to form magnetite deposits of a given scale. Our findings support the physical viability of the liquid immiscibility hypothesis for the genesis of MtAp deposits, offering new insights into the mechanical efficiency of melt separation and contributing to a broader understanding of the formation mechanisms of other valuable deposits that have been linked to immiscible melts.

SummaryThis study focuses on the hydrophone component of a dense ocean bottom cable dataset from the North Sea. This data had already been used in the past to illustrate the high resolution power of full waveform inversion based strategies. We have developed a highly scalable implementation of a visco-acoustic full waveform inversion engine making it possible to double the frequency content of the inverted data compared to previous studies, using simultaneously up to almost 50,000 CPU. This results in a multi-parameter reconstruction of the subsurface, where the P-wave velocity, the density and the quality factor are reliably reconstructed down to 2 km depth, with a resolution of about 10 meters.

SummaryHarmaliére is an active landslide located in a mountainous region of southern France where the presence of a thick layer of clays provides favorable conditions for the development of slowly moving landslides. However, at Harmaliére, the alternation of sudden reactivation and quiet episodes suggests that specific structural and geomechanical properties control its kinematics. In order to shed light on its subsurface properties, we deployed for one month a dense network of seismic nodes within the landslide and recorded active-source and ambient noise seismic data. These datasets have been first independently processed with dedicated interferometry-based processing and inversion workflows to reconstruct P-wave (active sources) and S-wave (seismic noise) velocity models. Full wave-equation tomography is then performed to improve the reliability and resolution of the obtained elastic model by iteratively fitting the virtual gathers obtained by cross-correlation of the ambient-noise recordings. As opposed to conventional ambient-noise tomography, the approach fully accounts for topography and 3D elastic heterogeneities. The obtained high-resolution 3D models are then qualitatively interpreted in terms of landslide properties and geological lithologies, that can influence landslide kinematics.

SummaryDebris flows pose a significant threat to the sustainable development of mountainous regions. As an effective real-time sensing technique, microseismic monitoring plays a critical role in the detection and analysis of debris flow activity. However, current microseismic monitoring technologies face challenges in distinguishing mixed signals originating from different sources, limiting our understanding of the full dynamic evolution of debris flow events. To address this issue, we propose an unsupervised deep clustering-based signal classification framework, which focuses on analyzing the signal characteristics at various stages of debris flow events. A two-dimensional spectrogram dataset was constructed, encompassing signals from debris flows, rockfalls, earthquakes, and environmental noise. A deep autoencoder was employed to compress spectral features into a 16-dimensional latent space, followed by clustering using deep embedded clustering and Gaussian Mixture Models. Experimental results demonstrate that, after optimizing the feature space and data partitioning strategy, the proposed method achieves an average classification accuracy of 96.81 per cent across the four signal types. Power spectral density distribution analysis further confirms that this method not only accurately identifies debris flow signals but also effectively captures their energy distribution and dynamic evolution at different stages. Interpretability analysis reveals strong correlations between the extracted latent features and conventional seismological parameters, particularly the peak count of the time-domain autocorrelation function and the first quartile of the central frequency. Based on this method, a complete segmentation of debris flow events was successfully achieved, revealing the typical signal characteristics and temporal evolution of each stage. Cross-station validation indicates that the proposed framework demonstrates strong robustness and generalization across different monitoring locations. In addition, preliminary exploration of its integration with supervised learning suggests its potential applicability in real-time monitoring scenarios, offering a novel approach for debris flow early warning. This study presents an efficient and intelligent method for debris flow signal recognition and dynamic monitoring.

SummaryBayesian formulations of inverse problems are attractive due to their ability to incorporate prior knowledge, account for various sources of uncertainties, and update probabilistic models as new information becomes available. Markov chain Monte Carlo (MCMC) methods sample posterior probability density functions (pdfs) provided accurate representations of prior information and many evaluations of likelihood functions. Dimensionality-reduction techniques such as principal component analysis (PCA) can assist in defining the prior pdf and the input bases can be used to train surrogate models. Surrogate models offer efficient approximations of likelihood functions that can replace traditional and costly forward solvers in MCMC inversions. Many problem classes in geophysics involve intricate input/output relationships that conventional surrogate models, constructed using samples drawn from the prior pdf fail to capture, leading to biased inversion results and poor uncertainty quantification. Incorporating samples from regions of high posterior probability in the training may increase accuracy, but identifying these regions is challenging. In the context of full waveform inversion, we identify and explore high-probability posterior regions using a series of successively-trained surrogate models covering progressively expanding wave bandwidths. The initial surrogate model is used to invert low-frequency data only as the input/output relationship of high-frequency data are too complex to be described across the full prior pdf with a single surrogate model. After a first MCMC inversion, we retrain the surrogate model on samples from the resulting posterior pdf and repeat the process. By focusing on progressively narrower input domain regions, it is possible to progressively increase the frequency bandwidth of the data to be modeled while also decreasing model errors. Through this iterative scheme, we eventually obtain a surrogate model that is of high accuracy for model realizations exhibiting significant posterior probabilities across the full bandwidth of interest. This surrogate model is then used to perform an MCMC inversion yielding the final estimation of the posterior pdf. Numerical results from 2D synthetic crosshole Ground Penetrating Radar (GPR) examples demonstrate that our method outperforms ray-based approaches, as well as results obtained when only training the surrogate model using samples from the prior pdf. Our methodology reduces the overall computational cost by approximately two orders of magnitude compared to using a classical finite-difference time-domain forward scheme.

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.