Geophysical Journal International

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.