Journal of Seismology最新文献

Paliki Peninsula, on Cephalonia Island, is one of the most seismically active areas in Europe, east of the major Cephalonia transform fault. In 1953, a series of three major earthquakes, Mw 5.9, Mw 6.6, and Mw 7.0, produced extensive damage in Cephalonia. More recently, in 2014, a sequence with two main shocks (~ Mw 6.0) caused considerable damage. We integrated a geological and geotechnical dataset from existing sources and used it together with ambient-noise records to study the subsurface structure of Paliki Peninsula along two E-W profiles, crossing the central (13 sites) and southern (seven sites) parts of the peninsula. We combined fundamental frequency microtremor horizontal-to-vertical spectral ratios (MHVSR) and shear-wave velocity, as derived empirically from borehole geotechnical parameters data and, in some cases, from literature, with forward modelling of MHVSR curves, to obtain 2D images of the subsurface structure along the profiles. The depth to the bedrock (Eocene limestones) reach maximum values of 300–450 m to the eastern end of the two profiles, with three overlying soil formations on top of the bedrock: (i) Holocene deposits 2–4 m thick, (ii) Marine deposits, with thicknesses of 4–30 m, and (iii) Marls of varying thicknesses increasing from West to East, with steeper slope in the central profile near the coast. This preliminary image of the subsurface structure of Paliki Peninsula will contribute to a better understanding of local tectonics, earthquake sources, local/path propagation effects, and for improved local seismic hazard assessments and risk mitigation plans.

This study addresses the critical need for accurate prediction models of seismic activity in Turkey, focusing on the main earthquakes and the aftershocks that follow them. The complex geological structure of Turkey, controlled by major fault lines such as the North Anatolian Fault Line and the East Anatolian Fault Line, requires robust analysis to understand seismic hazards better and to implement effective preventive measures. This research aims to fill the gap in the predictive modeling of integer-valued seismic data by comparing the effectiveness of first-order INteger-valued AutoRegression (INAR(1)) models with the more commonly used AutoRegressive Integrated Moving Average (ARIMA) models. To achieve this, we analysed the occurrence of mainshocks and aftershocks on a monthly basis from January 2011 to December 2020. The INAR(1) models were specifically applied to this integer-valued time-series data, and their forecasts were compared with those produced by ARIMA models. Our results indicate that the INAR(1) models provide forecasts closer to the observed values than the ARIMA models for both the mainshock and aftershock datasets. In particular, the INAR(1) models showed superior performance in terms of accuracy, with numerical results showing a reduction in forecast error of about 15% compared to ARIMA models. These results have significant implications for earthquake preparedness and risk reduction in Turkey. Through the use of INAR(1) models, we can improve the accuracy of the prediction of seismic activity and thereby increase the ability to implement safety measures in a timely and effective manner. This study highlights the importance of better understanding and mitigating earthquake risk by using appropriate statistical models tailored to the specific characteristics of seismic data.

The Arraiolos zone is one of the most seismically active intraplate regions in mainland Portugal and is characterized by low- to moderate-magnitude events. However, there is a need to analyse the characteristics of the seismic source parameters in this zone, which is crucial for understanding the origin of the zone’s seismicity and gaining insights into the stresses involved in the rupture processes of seismic events. On January 15, 2018, an earthquake of magnitude 4.9 M_L occurred northeast of Arraiolos, near Aldeia da Serra. This event was followed by aftershocks with magnitudes of up to 3.5 (M_L). In the present study, we estimated the source parameters and respective scaling relationships of 82 earthquakes (0.6 ≤ M_L ≤ 4.9) that occurred in the study area following the 2018 Arraiolos earthquake. The source parameters were estimated from the displacement spectra of the P-waves by automatic fitting of the Brune spectral model (ω⁻²). Consequently, the parameters characterizing the seismic source, such as the scalar seismic moment (M₀), moment magnitude (M_w), corner frequency (({f}_{c})), source radius (r_o) and stress drop (Δσ), were determined. The results show that the moment magnitude (M_w) varies between 0.9 and 4.3. The source radii range from 31.5 m to 775.9 m, and the stress drop values (Δσ) range from 0.4 bars to 97.0 bars, with an average of 7.3 bars. Furthermore, most earthquakes occur mainly between 12 and 13 km depth, with a range of stress drop values (including low and high values). The linear relationships between the local and moment magnitudes are consistent. An increasing trend was observed between the source radii and the seismic moments, as expected. It was found that there is no linear correlation between stress drops and seismic moments. This suggests that the dynamic parameters controlling the rupture of larger magnitude earthquakes may be different from those of weaker earthquakes. Studying seismic source parameters in the Arraiolos area is crucial for understanding the seismogenic dynamics, as well as to improve regional hazard assessment.

East Asia comprises multiple tectonic domains and has been the subject of many regional and local broadband seismic investigations in recent years, especially the ChinArray experiments. These studies have improved the overall seismic data coverage for East Asia, although the distribution of data is extremely uneven. While regionalised group or phase velocity dispersion curves from surface-wave tomography are particularly important for deriving deep shear-wave velocities, calibrating phase velocity measurements and joint analyses with other geophysical data, they are normally derived using quadrilateral cells with a fixed latitude and longitude spacing, such that the cell spacing varies with latitude but not with data coverage. For a region with extremely uneven data coverage, closely spaced cells will worsen the ill-posedness of tomographic problems, whereas widely spaced cells will lower the lateral resolution capability of regions with dense data. Here we propose a new model discretisation approach for two-dimensional surface-wave tomography that divides the study area into triangular cells with variable sizes based on data coverage and apply it to East Asia, where seismic observations are numerous but unevenly distributed. The updated regionalised Rayleigh-wave group and phase velocity maps detect small sedimentary basins with low velocities and large cratons with high velocities, implying that our approach can simultaneously image local-, regional- and large-scale structures in one tomographic system. The regionalised dispersion curves can be used to invert for deep structure directly or jointly with other geophysical observations across East Asia.

The earthquake catalog maintained by the Iranian Seismological Center (IRSC), which includes information on more than 187,000 seismic events occurring between 2006 and 2024, serves as the primary reference for seismological research in Iran. However, this catalog does not distinguish between tectonic and non-tectonic events. In this study, we employed a statistical de-quarrying algorithm, based on the fact that quarry blasts predominantly occur during daytime hours, to identify and remove quarry and mine blasts from the IRSC’s earthquake catalog. By applying this algorithm, approximately 24% of the reported events were identified as quarry and mine blasts, with magnitudes ranging from 0.4 to 2.9. We provide the resulting earthquake catalog in the supplementary materials, along with comprehensive information on more than 142,000 earthquakes with magnitudes ranging from 0.3 to 7.6 over the 18-year period in Iran and adjacent regions. This will facilitate a thorough assessment of the accuracy and uncertainty of the location parameters in the catalog. We strongly advocate for the use of the purified earthquake catalog presented in this study for seismic research, as opposed to relying on the unfiltered IRSC catalog. However, to further enhance reliability, it is imperative to develop a more accurate catalog by distinguishing between non-tectonic and tectonic events in Iran through waveform or spectral analysis, particularly for smaller events.

The complex tectonic framework of Taiwan makes it susceptible to devastating earthquakes that originate on both mapped faults, and at times, on unmapped faults. The unmapped faults especially highlight the limitation of conventional fault–based hazard assessment methods, emphasizing the need for alternative approaches. In this context, we implement a surrogate area–based earthquake nowcasting technique to assess the seismic cycle progression in 10 densely populated cities across Taiwan. We utilize the notion of natural times, the inter–event counts of small earthquakes between successive large events, to calculate the Earthquake Potential Score (EPS) for each city region. To derive natural time statistics, we analyze eight reference probability models, including exponential distribution and its variants, exponentiated group of distributions, and heavy–tailed distributions. Statistical inference of 114 observed natural times shows that the exponentiated exponential distribution provides the best fit. As of April 24, 2025, the EPS values (%) for M (ge ) 6.0 earthquakes in the 10 cities range from 53% to 69%, with the following values: Taipei (69%), Hsinchu (68%), Keelung (67%), Hualien (59%), Nantou (58%), Taitung (57%), Chiayi (56%), Pingtung (55%), Tainan (54%), and Kaohsiung (53%). These EPS values indicate the progression in current earthquake cycle toward a M (ge ) 6.0 earthquake in the corresponding city region. Moreover, there is a consistency in the nowcast scores despite some variations in threshold magnitudes and city regions. The studied approach and results therein offer valuable insights to decision makers to enhance earthquake preparedness and risk management across Taiwan.

Rapidly and reliably distinguishing between natural and nonnatural small-scale earthquakes is crucial for earthquake monitoring and seismic activity analyses. In this study, we propose a vision transformer for seismology (SeisViT) to discriminate between natural and non-natural earthquakes. Our SeisViT is based on a vision transformer (ViT) network that introduces a multihead self-attention mechanism, which can effectively capture and focus on important features from seismic waveforms.The SeisViT model processes three-component raw waveforms from a single seismic station, using data collected from natural and nonnatural earthquakes in China. Through a comprehensive evaluation of hyperparameters—including learning rate, number of transformer encoder layers, and patch size-we optimized the SeisViT architecture to achieve maximal performance. Our results demonstrate that the SeisViT model, with a learning rate of 10^-3, six transformer encoder layers, and a patch size of eight, achieves superior accuracy in discriminating natural from nonnatural earthquakes. Compared to conventional models such as multilayer perceptron (MLP), decision tree (DT), random forest (RF), and support vector machine (SVM), the SeisViT model achieved the highest accuracy (90.17%), precision (89.68%), recall (89.90%), and F1 score (89.79%) on the test dataset. These results underscore the potential of the SeisViT model as a significant advancement for earthquake monitoring with promising applications in seismology.

In this research, we have obtained the slip distribution of the 1996 Nazca earthquake, using a teleseismic waveform inversion method; then, we have conducted the numerical modelling of the tsunami propagation. The results reveal a complex rupture process with a duration around 70 s for a mean rupture velocity of 2.5 km/s. The maximum slip was 2.9 m and the seismic moment was 5(times 10^{20}) Nm, equivalent to a moment magnitude of Mw 7.7. The coseismic deformation field, due to the slip distribution obtained in this research, was used to conduct the tsunami numerical modelling of the 1996 Nazca earthquake. The reported uplift in the coast of San Juan de Marcona was 20 cm and the simulated uplift was 26 cm. This earthquake triggered a small tsunami with only local impact. The maximum simulated tsunami high at San Juan de Marcona tidal station was 1.65 m while the reported value was 1.80 m. We suggest that the 1996 Nazca earthquake can be considered as a part of the 1940-2007 central Peru earthquakes series.

In this paper, we study the geographic and temporal distribution of earthquakes in Guatemala, and their magnitudes, from 1526 to October 2022. We utilized the earthquake catalog of the national seismic network of Guatemala, complemented by two additional sources. First, we describe the development of the detection network and the seismic catalog. Second, we analyze the errors in the catalog and determine the quality of the focal locations, examining their distribution in time and space while separating events into shallow and deep earthquakes. Finally, we calculate seismic parameters such as annual rates of earthquakes and magnitude of completeness. The results indicate a strong variability in time and space of errors and seismic parameters that are linked to changes in the detection network. In conclusion, by highlighting the evolution of this catalog and its features, this paper underscores the importance of considering these spatial and temporal variations in future analyses of seismicity and seismic hazard in Guatemala. This topic can be of interest to other countries with similar characteristics in tectonics and detection networks.

The Haynesville Shale in eastern Texas and western Louisiana has been one of the most productive shale gas plays in the USA. It is notable for being significantly over-pressured, a factor which has often been associated with an increased likelihood of hydraulic fracturing-induced seismicity (HF-IS) elsewhere. However, to date, only one case of HF-IS has been identified in the Haynesville play. Seismic monitoring across the play is relatively sparse, so it is possible that the absence of reported cases represents an absence of monitoring rather than an absence of cases. This study represents an investigation of HF-IS across the Haynesville play, primarily using data from the TexNet seismic monitoring array, which was installed in 2017. We use template matching to increase the population of detected earthquakes, increasing the number of detections by over 200% compared to the catalogs available from regional monitoring agencies. The resulting events can be clustered into several discrete sequences. We use an induced seismicity assessment framework to evaluate whether each sequence was induced and, if so, what industrial activity represents the most likely cause (both hydraulic fracturing and wastewater disposal operations take place within the footprint of the Haynesville play). We find three notable cases of HF-IS, straddling the region between Nacogdoches, San Augustine and Shelby Counties. Having identified these sequences, we examine whether any geological conditions may influence the occurrence of HF-IS. We identify increased formation depth, increased pore pressure gradients, and the thinning or absence of the underlying Louann Salt, which may otherwise serve as a hydraulic barrier between the Haynesville Shale and the basement, as factors that may account for the varying prevalence of HF-IS across the play.