Geophysical Journal International

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.