Journal of Seismology最新文献

Geological evidence of extreme high-energy sea wave events can be preserved in coastal areas as depositional and/or erosional deposits. In this work we present field evidence for the existence of high energy sea wave depositional features, in the Mostaganem region, northwestern Algeria, which include: (i) unusual marine sedimentary features trapped in a waterway valley, in an old coastal notch, and in low-lying parts of the coastal zone, (ii) carried, imbricate and oriented large boulders of weight ranging between 1.2- 32.6 tons. Both the boulders and the sedimentary deposits fit the characteristics of high energy waves deposits while at least some of them fit the tsunamis waves ones. Hydrodynamic analysis of the joint bound and the submerged boulders, considered in this study, yield values ranging between 0.40m to 29.24m for storm waves height (Hs) and 0.10m to 7.31m for tsunamis waves height (Ht). The threshold minimum velocity ranges between 1.91m/s—17.14m/s. Therefore, the majority of the boulders may have both storm and/or tsunami origin while some of them (weight exceeding 25 tons) requires very high storm waves unlikely to occur in the study area; Consequently, the tsunami origin is the most possible for this case. On the other hand, tsunami modeling, considering near shore active faults, has shown that the maximum heights of 1.21m, 0.81m, 0.35m, 0.41m and 0.34m can be expected at Stidia (where the boulders are described), Les Andalouse, AinTurck (north-west Algeria), Almeria and Cartagena (Southern Spain), respectively. these heights are comparable to the ones described during the Oran October 9th, (I₀ = IX) historical earthquake in northwestern Algeria and Southern Spain. Nevertheless, the maximum wave height needed to move some of the very big boulders found in the study area exceeds 7 m, possible likely, not from near shore earthquakes, but from Iberian or Mediterranean offshore earthquakes considered in previous study as a possible source for northern Algerian tsunamis. This work should encourage further research in the prevention of tsunami hazards along the Algerian coast, densely populated and where important economic infrastructures are located, which is insufficiently studied compared to other Mediterranean regions.

On April 3, 2024, an M_S7.3 earthquake occurred in Hualien County, Taiwan. Some strong ground motion acceleration records were captured by the relevant stations of the Central Meteorological Bureau of Taiwan and the Palert earthquake early warning system. Based on these acceleration records, this paper proposes a method for calculating and analyzing the dominant azimuth of strong ground motion. The distribution characteristics of the dominant azimuth of strong ground motion in each region are quantitatively analyzed from the perspective of epicentral distance and geological division and the characteristics of ground motion in sea area are also analyzed. The research results show that the dominant azimuth of strong ground motion is randomly distributed throughout the entire area affected by this earthquake, with no significant dominant azimuth interval. The dominant azimuth of strong ground motion in the near-field region and the eastern coastal area is clearly distributed, with most of the acceleration dominant azimuth distributed in the range 150°-210°. The dominant azimuth of strong ground motion is uniformly distributed in the western impact plain area of Taiwan without significant dominant azimuth interval, while the dominant azimuth of strong ground motion in the eastern mountainous area and the central hilly area is clearly distributed. In addition, the acceleration time history in the sea area shows the characteristic of low frequency and long period.

Recent earthquakes have adversely affected human life, highlighting the necessity of identifying structural and ground conditions to assess risky areas and mitigate potential damage from future seismic events. Gümüşhane, the designated study area, is susceptible to the impacts of a possible earthquake due to its proximity to the North Anatolian Fault Zone (NAFZ). Accordingly, vertical electrical sounding (VES), multi-channel analysis of surface waves (MASW), and microtremor methods were employed in the central district of Gümüşhane to characterize local ground conditions and to evaluate the resonance hazard potential arising from the interaction between structures and the underlying soil. Dominant period (T₀) and frequency (f₀), spectral amplification (A₀), soil index of vulnerability (Kg), average S wave velocity for 30 m depth (Vs₃₀), ground type, and soil-structure resonance hazard potential were determined. For the study area, f₀ values between 1.32–8.91 Hz (T₀ values varied between 0.11–0.76 s), A₀ values between 0.93–7.63 according to horizontal/vertical spectral ratio (HVSR) analysis, and Vs₃₀ values were between 390–886 m/sec to MASW. As a result of the evaluation of microtremor measurements made in all buildings using the floor spectral ratio (FSR) technique, the soil-structure potential resonance hazard was found to be low. The present study, conducted in Gümüşhane, examined the subject of soil-structure interaction. In the process of planning new residential areas, it is imperative to ensure the reliability of construction based on the local ground conditions. Furthermore, it is essential to determine the risk status of buildings in relation to potential earthquake movements.

Methodological aspects relative to the conversion of probabilistic hazard evaluations in terms of ground motion intensity into hazard in terms of macroseismic Intensity. As a first step, the probabilistic relationships between ground motion intensity and macroseismic intensity are critically re-examined to account for the different formal properties of these two dimensions of the earthquake intensity. Then these relationships are coherently considered in the conversion of hazard curves by accounting for the inherent binning of macroseismic intensity values and of their inherent probabilistic nature. It is shown that approximate procedures currently used to provide this conversion may provide biased outcomes when the dispersion of values relative to ground motion intensity corresponding to macroseismic Intensity is dismissed or not properly accounted for. The impact of this possible bias is evaluated by considering the seismic hazard at reference site condition at the city of L’Aquila in Central Italy.

Understanding clustered earthquake sequences is essential for seismic hazard assessment, as it involves constraining faulting styles and nodal planes of potential ruptures. This study investigates the nature of a dense earthquake sequence (~ 3000 events) initiated on January 27, 2025, in the Santorini-Amorgos region of the Southern Aegean Sea (SAS), a tectonically active Volcanic Island Arc (VIA). We analyzed 23 shallow crustal earthquakes (Mw ≥ 4.5, depth ≤ 10 km) that occurred between February 2–9, 2025, using full-waveform, low-frequency Centroid Moment Tensor (CMT) inversion from regional seismograms. The inversion was complemented by high-resolution Bouguer gravity anomaly data derived from the EIGEN-6C4 satellite gravity model to assess subsurface density variations. The focal mechanisms consistently indicate NE-SW striking, high-angle (≥ 45°) normal faults with NW- and SE-dipping planes and centroid depths ≤ 10 km. Integration of CMT results with gravity anomalies (90–100 mgal) suggests a migrating zone of shallower extensional magmatism (SEM) driving the sequence. These findings reveal a Precursory Seismic Cluster (PSC) and provide new constraints on the seismotectonic and magmatic processes shaping seismic hazard in the region.

In a natural disaster, intelligent Internet of Things (IoT) systems can be utilized to respond appropriately. Recently, the application of IoT technology in seismology, particularly in earthquake detection, has garnered much attention. This approach’s attractiveness lies in its simplicity of installation, minimal processing power requirements, cost-effectiveness, and expansive coverage, even in areas lacking Internet connectivity. However, the locality of installed sensors brings variations in seismic and noise data, making the earthquake detection task very challenging because of the false alarms. Network-based systems connecting multiple IoTs can resolve the issue by running highly computation-intensive algorithms on a powerful server or cloud and aggregating the data sent from those sensors. On the other hand, Standalone IoT devices operate independently, making decisions locally using both traditional and machine learning methods to manage false alarms. However, these techniques struggle to handle diverse noise patterns and often fail to detect low-magnitude earthquakes in noisy environments. While deep learning models can enhance earthquake detection in such conditions, their high computational cost makes them impractical for resource-constrained devices. To address these challenges, this article introduces a lightweight deep learning model incorporating a transfer learning approach for standalone devices. The proposed model outperforms traditional machine learning methods in earthquake detection using IoT sensors while significantly reducing computational demands. Designed to operate without internet connectivity, the Multi-headed Convolutional Neural Network (MCNN) model achieves 99% accuracy without incurring additional processing costs. Furthermore, it demonstrates high adaptability and the ability to update rapidly with minimal configuration changes.

The rapid and accurate identification of natural earthquakes and artificially blasted earthquakes is crucial for effective earthquake monitoring and early warning. We used waveform data from 5480 natural earthquake events and 4482 blasting events recorded by 110 seismic stations in a quarry in Utah, USA from January 2013 to August 2017, to construct a deep machine learning based CNN-ECA model and accurately and efficiently identify and verify these two types of earthquakes. Firstly, these data were preprocessed by removing mean, trend, instrument response removal, resampling (100 Hz), and bandpass filtering (1–20 Hz). Afterwards, the Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), and Short Time Fourier Transform (STFT) methods were used to transform the time-domain data of 1453 natural earthquake events and 1103 quarry blasting events in 2013, obtaining four different types of training sample data: time-domain, frequency-domain (FFT results), and time–frequency domain (CWT and STFT results). Next, the four types of sample data were trained and tested using the Efficient Channel Attention Convolutional Network (CNN-ECA) and traditional Convolutional Neural Network (CNN). The results showed that the CNN-ECA model outperformed the CNN model in all four test samples. Especially when using time–frequency data converted through STFT and FFT as input, the recognition performance of the network model is more significant, with test set accuracies reaching 97.94% and 97.80%, respectively. Finally, the trained CNN-ECA model was used to validate and analyze the natural earthquakes and quarry blasting events recorded between 2014 and 2017. The results indicated that the combined use of FFT and STFT/CWT input data to jointly discriminate seismic events further improved the accuracy of earthquake type identification.

In Cenderawasih Bay, Papua, we improved the assessment of earthquake hazards by integrating seismic parameter analysis with gravity anomaly data. This region, which is known for its seismic activity and complex tectonic structures, offers an ideal setting for disaster risk-mitigation studies. In eastern Indonesia, tectonic plate convergence involves several microplates, creating deformation zones with arc-continent collisions, subduction, shear faults, strike-slip faults, and extensional faults. Our study examined the relationship between earthquake activity and gravity anomalies to enhance the understanding of seismic activity distribution in the Cenderawasih Bay region. Seismicity parameters were analyzed using the IRIS Earthquake Catalog, with aftershocks and foreshocks removed through Reasenberg declustering to focus on independent events. The average values for the seismicity parameters, specifically the magnitudes of completeness (M_c), a, and b, were 4.5, 7.3, and 0.95, respectively, indicating significant tectonic activity. Gravity anomalies were analyzed using a two-dimensional radial power spectrum and three-dimensional Euler deconvolution, which resulted in an estimated depth range for the gravity anomaly source between 15 and 33 km. The results demonstrate that seismic activity is primarily concentrated in the northern region and along the main fault lines of Cenderawasih Bay. There is a correlation between the seismicity parameter b-value and variations in gravity anomalies, with low b-values associated with low to moderate gravity anomalies in regions of high crustal pressure. In contrast, elevated b-values are associated with significant gravity anomalies in low-pressure regions. The seismicity parameter a-value was low to moderate, suggesting tectonic activity. High values correlate with significant gravity anomalies, and the inverse is true. Integrating seismicity analysis with gravity anomaly characterization provides insights into the earthquake distribution in Cenderawasih Bay. This methodological approach not only improves the accuracy of earthquake hazard assessments in the region, but also aids effective disaster risk mitigation.

We present a seismic tomography study of the subsurface structure beneath Hachijojima Island, one of the volcanic fronts in the Izu-Bonin Arc, Japan. Seismic observations were conducted over two 7-month periods in 2019 and 2021, utilizing 55 densely installed stations on the island. During these periods, a total of 179 local earthquakes were recorded — 119 in 2019 and 60 in 2021 — resulting in 4671 P-wave arrival times and 3927 S-wave arrival times. The 3-D tomography, derived using the double-difference technique, revealed a shallow low-velocity region between the island’s two main volcanoes, Nishiyama and Higashiyama, suggesting the presence of volcanic sediments near the surface. Additionally, a high-velocity anomaly was identified at a depth of 4–5 km, extending vertically from deeper regions beneath Nishiyama. This feature is interpreted as a magma pathway from past volcanic activity, with high P-wave velocities and elevated Vp/Vs ratios indicating possible fluid presence. At greater depths, low P-wave velocity perturbations and elevated Vp/Vs ratios suggest a magmatic plumbing system comprising a mid-crustal magma chamber at approximately 8–12 km depth and lateral magmatic pathways at 10–20 km depth. Furthermore, a distinct zone characterized by reduced P-wave velocity and increased Vp/Vs is interpreted as a shallow magma chamber with H₂O-saturated magma accumulation. These findings provide valuable insights into the subsurface magmatic processes beneath Hachijojima Island, which are crucial for improving volcanic hazard assessment.

We investigate seismic velocity changes in the rock mass related to mining induced seismic events and ore exploitation by computing a one-month long 4D elastic model of Kiirunavaara mine (Sweden). We focus on a specific mine sector, where a single (varvec{M_W})=2.0 event occurred on May 22 (02:31 local time), damaging the infrastructure. We make use of P- and S-first-arrival times obtained from the permanent seismic system for computing the full 4D (continuous 3D volume in time) seismic velocity model of Kiruna mine using a trans-dimensional Monte Carlo sampling. The trans-dimensional approach guarantees that the resolution, both in space and in time, is strictly data-driven. Our results give the following insights into the velocity differences at the mining levels and at different time-length scales. (a) We observe a striking correlation between spatial variations of (varvec{V_P}) and ore-body geometry, confirming the robustness of the velocity model. Clay zones appear as a low (varvec{V_P/V_S}) ratio zones, as seen in previous tomographic studies. (b) High-frequency (hourly) fluctuations of the rock mass (varvec{V_P}) around the ore-passes are highly correlated with seismic sequences in the same rock volumes. In particular, (varvec{V_P}) increases rapidly when ore-passes are seismically active and (varvec{V_P}) values keep a high value for few (1-4) hours after the end of the seismic sequence. (c) The smoothed velocity model, computed as averaged model over a 2-days moving window, suggests that low-frequency (varvec{V_P}) fluctuations can be compared to stress cell measurements located closely.