Journal of Seismology最新文献

We introduce a new form of probabilistic seismic microzonation maps in terms of ({V}_{text{max}}) peak velocity of strong earthquake ground motion and illustrate the method for the city of Novi Sad in Serbia. The maps we introduce avoid the limitations of hazard maps drawn solely on peak ground acceleration, which are physically limited to one-parameter scaling The new method complements seismic hazard maps based on Uniform Hazard Spectra (UHS) by directly scaling several characteristics of strong motion that cannot be physically related to spectral amplitudes or to peak accelerations. We demonstrate how the new maps can be used to evaluate strains near ground surface during strong ground motion, as well as areas where buildings can be damaged during future strong ground motion. The new microzonation maps of ({V}_{text{max}}), together with probabilistic estimates of relative displacement (SD) spectra, can be used to derive estimates of pseudo-static forces in ground-level columns of long structures.

Earthquakes pose significant seismic hazards in urban regions, often causing extensive damage to the built environment. In regions lacking robust seismic monitoring networks or sufficient data from historical events, ground motion simulations are crucial for assessing potential earthquake impacts. Yet, validating these simulations is challenging, leading to notable predictive uncertainty. This study aims to simulate four scenario earthquakes with moment magnitudes of 6.8, 7.1, 7.4, and 7.7 in Iran, specifically investigating variations in fault plane rupture and earthquake hypocenter. The North Tabriz Fault (NTF), located within the seismic gap in northwest Iran, is selected as the case study due to the lack of well-recorded ground motions from severe earthquakes, despite historical evidence of large-magnitude events. Simulations are conducted using a stochastic finite-fault ground motion simulation methodology with a dynamic corner frequency. Validation of the simulations is performed by comparing estimated peak ground motions and pseudo-spectral ordinates with existing ground motion models (GMMs), supplemented by inter-period correlation analysis. Simulation results reveal high hazard levels, especially in the northeastern area near the fault plane. Intensity maps in terms of the Modified Mercalli Intensity (MMI) scale underscore the urgency for comprehensive preparedness measures. Finally, a region-specific GMM is developed using Artificial Neural Networks (ANN) to predict peak ground motion parameters with an online platform accessible to end-users.

The classification of seismic events is important for earthquake emergency warnings and earthquake catalog database establishment. In this paper, we developed a multiscale convolution and window self-attention network for seismic event classification by combining multiscale convolution with inductive bias capability and self-attention mechanism with long-range information capture capability. This paper employed a pre-processing strategy to acquire the complete seismic waveform and separate seismic data in each component. Additionally, a voting mechanism is proposed to integrate data from three components for classification, improving overall accuracy. The experimental results showed that the overall classification accuracy is 94.02% when considering seismic data from a single component only. However, after incorporating a voting mechanism, the classification accuracy increases to 97.56%, which outperforms other methods. The results demonstrated that the multi-scale convolutional and windowed self-attention networks can effectively and significantly improve the accuracy of seismic event classification, which get a good result in seismic event classification.

To further understand the structure of the Qinhang Metallogenic Belt (QHMB) and its adjacent areas in the southeastern region of China, we collected continuous waveform data from 73 broadband stations recorded from 1th January to December 31th 2021, and applied ambient noise tomography method to obtain the 3D S-wave velocity structure from the surface to the depths of 45 km in the area. We used the time–frequency normalization (FTAN) approach to extract 2035 phase velocity dispersion curves, and applied the Occam inversion algorithm to generate the phase velocity maps of 5 ~ 45 s. After that, we utilized the surf96 program to invert the 1D velocity structure of the S-wave under each grid, and then combined these grid points to produce a high-resolution 3D structure of the S-wave velocity structure. Based on the inversion results, we draw the conclusions as follows: (1) The high-speed body appearing in the middle crust below the Qinling Dabie Orogenic Belt (QDOB) may be the consequence of the direct cooling of partially melted magma formed by the upwelling of asthenosphere material into the middle and lower crust; (2) Since the Late Mesozoic, the subsidence of the lower crust and large-scale basal magma intrusion have caused a northeast-southwest trending high-speed band-shaped anomaly in the middle-lower crust of the area; (3) Against the background of continental extension, the thickening and extension of the crust have led to the enrichment of mineral-rich upwelling in suitable locations, ultimately forming the present-day metallogenic belts in the Middle-Lower Yangtze River Metallogenic Belt (MLYMB) and the eastern of the QHMB.

The high-frequency decay parameter (κ) is investigated using the three-component broadband seismograms from 306 earthquakes with M_L 3.1–5.6 recorded at nine Iranian National Broadband Seismic Network (BIN) stations in the Alborz region and adjacent areas. The individual κ values are calculated for both the horizontal and vertical components of each record. The estimated mean horizontal and vertical κ values are 0.051 and 0.035 s, respectively, indicating slightly lower attenuation of high-frequency energy on the vertical component than the horizontal one. The dependence of the kappa values on path and source parameters such as distance, magnitude, and focal mechanism are also investigated. A clear increasing trend is observed for κ values with hypocentral distances for horizontal and vertical components. The zero-distance kappa (κ₀) values for the nine BIN stations are evaluated, and a mean value of 0.013 s is estimated, which is close to the values expected for generic rock sites. The obtained κ values show no significant correlation with the earthquake size in the magnitude range of our events. Furthermore, the κ values are found to be fairly similar for all faulting types, with a slight decrease in κ for strike-slip events; hence, the kappa values are deemed as independent of faulting type.

A Ground-Motion Model (GMM)'s apparent aleatory variability is inflated by errors in its predictor parameters, specifically the moment magnitude (({M}_{W})). Multiple ({M}_{W}) values can be available for an event (direct or deduced) and various ({M}_{W}) definition approaches have been proposed to assign a unique ({M}_{W}) value to an event. In this study, we investigate the impact of ({M}_{W}) definition on a pan-European Engineering Strong Motion dataset based Fourier GMM, using two datasets with ({M}_{W}) defined by two distinct approaches: [1] the ranking strategy of the Euro-Mediterranean Earthquake Catalogue (EMEC 2019) and [2] the multi-strategy (standardization, ranking, unification, averaging) approach to ({M}_{W}) definition of Laurendeau et al., (Geophys J Int 230:1980–2002, 2022). Large discrepancies in ({M}_{W}) values can be observed especially between ({M}_{W}) ranging from 4.0 to 5.0. While the GMM median predictions remain unchanged irrespective of dataset, we report a large reduction in between-event variability of the GMM at low frequencies (< 1.8 Hz) when strategy [2] is adopted over [1] (18% at 0.35 Hz). This reduction applies to frequencies before the corner-frequency of the Fourier spectrum, as this part of the spectrum depends primarily on seismic moment. We attribute this reduction to the use of direct ({M}_{W}) values in [2] instead of deduced ({M}_{W}) values in [1], the priority scheme in the ranking strategy, and the unification strategy. Our study suggests that the approach used to define a unique ({M}_{W}) in the GMM dataset may have a significant impact on its predictions in seismic hazard assessment.

The current stress tensor inversion method based on the focal mechanism cannot solve problems such as the interference of too many outliers on the results and the slow speed and low accuracy caused by the excessive computation of the inversion process; therefore, we propose a new stress tensor inversion method, GSBBO (grid search, boxplot and Bayesian optimization), which combines machine learning algorithms to sieve out outlier data and improve the inversion speed and accuracy. The method first screens the focal mechanism data via a grid search and boxplot, and this process eliminates the bias of the outliers on the results. Then, to improve the speed and accuracy of the inversion results, the method further inverts the stress tensor by means of Bayesian optimization, which can obtain high-precision results quickly by means of screened datasets and machine learning algorithms. The GSBBO method is validated using artificially synthesized focal mechanism data containing random noise and outliers for three stress systems. The obtained results are compared with those of grid search and Bayesian optimization, and the GSBBO method is able to accurately identify the outliers and provide more accurate results quickly. Applying the method to the area of the Great Wall Station in Antarctica, the results show that the area experiences near-vertical compressive stress and strong northwest‒southeast extensional stress, which is consistent with the extensional stress in the area due to the subsidence of the Phoenix Plate. These findings indicate the continuing subsidence process of the Phoenix Plate in Antarctica.

In this paper, an efficient two-step convolutional neural network (CNN) procedure is proposed to estimate earthquake magnitude using raw waveform data up to only 4 s after the P wave onset. In the proposed procedure, magnitude estimation is split into classification task and regression task. The classification task trains a CNN model to estimate the magnitude range by employing unsure responses that represent the classification decision boundary. In addition, the regression task trains two CNN models to estimate the specific magnitudes of large and small earthquakes, respectively. After training, the classification model achieves an accuracy of 98.63%. The mean absolute error (MAE) of the large earthquake regression and the small earthquake regression models are 0.26 and 0.46, respectively. The ideology behind the two-step procedure effectively address two main issues in earthquake early warning (EEW) systems: reducing missed alert caused by seismometer saturation and improving the accuracy of estimating specific magnitudes. Currently, this procedure has been connected to China Earthquake Networks Center (CENC) for real-time monitoring.

We investigate the properties of a seismic sequence occurred close to the southwestern edge of the Kefalonia Transform Fault Zone where a decadal relative quiescence was observed. The moderate M_w5.5 main shock occurred on 8 September 2022, close to the 1983 M7.0 rupture zone. We compiled a precisely relocated aftershock catalog of a low magnitude of completeness (M_c = 1.8) aiming to identify the seismicity patterns and reveal the characteristics of the spatiotemporal seismicity evolution. The appreciable aftershock activity lasted for almost 4 months being spatially distributed along a NE-SW elongated zone with a length of approximately 16 km. The focal mechanism of the main shock along with the spatial distribution of aftershocks revealed the activation of a dextral strike-slip fault segment. Statistical analysis of the aftershock activity indicated that it conforms to the Mainshock-Aftershock type, with aftershock occurrence decaying rapidly over time indicating a low aftershock productivity. Coulomb stress changes analysis revealed that the expansion of the aftershock zone was strongly affected by the stress transferred by the main rupture. The state of stress in the area before the occurrence of the main shock explains the activation of this fault segment, which could be associated with the structure that accommodated the large 1983 M7.0 earthquake.