We investigate the regional subsoil structure around the city of Thessaloniki, northern Greece, using Rayleigh wave group velocity dispersion curves (U(f)). Three independent datasets were used. The first two are ambient noise and earthquakes recorded at 12 different sites with broadband seismographs during a period of almost four years. The third dataset consists of small earthquakes recorded by the permanent accelerographic network that operates in Thessaloniki. Ambient noise records were used to estimate Green’s functions between station pairs from the average crosscorrelation of simultaneous noise records. Dispersion curves were estimated from Green’s functions and earthquake records using the Multiple Filter Technique. We explored the possibility of using those dispersion curves to compute tomographic models in the region, independently for each dataset. We found that the seismic noise crosscorrelations and the acceleration records produced compatible results. The seismograph array gives larger U(f) values. We show that this difference is related to the length of the path where group velocity is estimated; the incompatibility of our datasets is the result of the particular distribution with distance within each dataset. The available data imposed relatively large cell sizes for the tomography computations. Although we observe some correlation between our results with a rather complex geology, the cell size used is too large to make detailed comparisons and the restricted amount of data makes the results variable. Our results show the need to analyze the compatibility of different datasets before mixing them in tomographic computations.
Recent increases in seismicity due to anthropogenic activities in southeast New Mexico, USA, demonstrate the need for timely earthquake detection and updated seismicity catalogs. The New Mexico Tech Seismological Observatory (NMTSO) has for many years performed earthquake detection and location in New Mexico using only manual analyst review, but as the number of earthquakes has increased, the implementation of automated methods in the earthquake detection workflow can improve efficiency, accuracy, and completeness of earthquake catalogs. In this study, we applied two machine-learning-based seismic phase auto-pickers to continuous waveform data from multiple seismic networks in southeast New Mexico and west Texas for 2021 to assess the efficiency of these tools for earthquake detection in this region. Results from deep learning tools (the EQTransformer and the PhaseNet auto-pickers) for events located within the New Mexico region were benchmarked against the manually picked NMTSO earthquake catalog derived for the study period. These catalog comparisons, specifically the numbers of missed and false detections, provide a baseline for evaluating machine learning tools in the southeast New Mexico region. We find that both machine learning methods allow detection of significantly more earthquakes than our manual detection, but due to significant numbers of false positives and missed events, they are best employed in conjunction with analyst review. We now employ EQTransformer in our earthquake detection process, thus advancing our ability to provide timely earthquake detection and location in this seismically active area. Article highlights. This study evaluated two machine-learning-based phase pickers for application within the New Mexico Seismic Network. The results demonstrate the importance of parameter optimization to minimize false positive (detections identified as noise events based on manual review) and false negative (earthquakes present in manually reviewed catalog but not present in the machine learning catalog) detections and improve event identification accuracy. By comparing the performance of the different tools, we implemented the most effective workflow for network earthquake monitoring, thereby improving the catalog completeness.
Numerous earthquake cases have shown that long-period ground motions can easily cause panic in high-rise buildings, damage to non-structural components, and even structural damage. However, existing instrumental seismic intensity cannot effectively quantify their strength, and there is a lack of prediction models for far-field long-period ground motions in southeastern coastal provinces and cities of mainland China induced by earthquakes originating in the Taiwan region. Using the peak value of the Absolute Velocity Response Spectrum (Sva) as the intensity measure, this study constructs a long-period ground-motion prediction equation suitable for earthquake early warning, based on 145 earthquakes with M ≥ 5.0 that occurred in the Taiwan region of China from 2012 to 2025 and observation data from seven provinces and municipalities in southeastern mainland China. A mixed-effects model framework is adopted, combining the maximum likelihood and restricted maximum likelihood methods in an iterative stepwise manner to optimize fixed effects and random effects (introducing event-specific random slopes and station random effects). Likelihood ratio tests confirmed that each added random slope significantly improved the fit (p < 0.001). Variance decomposition showed that random effects explain 82.74
The April 1, 2021 Mw 4.7 Guelma earthquake sequence provides new insights into strain localization, fault segmentation, and fluid–fault interactions within an actively evolving transtensional pull-apart basin in northeastern Algeria. We integrate double-difference relocation of aftershocks, focal mechanism analysis, stress tensor inversion, spectral source parameter estimation, and morpho-structural observations to constrain the geometry and kinematics of the activated fault system. Relocated seismicity delineates a 14 km-long, steeply dipping NW–SE structure extending from 2 to 17 km depth and coinciding with the western basin-bounding Hammam Debbagh–Roknia fault (HDF). Focal mechanisms and stress inversion indicate dominant dextral strike-slip motion with a minor normal component under a NNW–SSE-oriented maximum horizontal stress, consistent with transtensional deformation. The limited rupture extent relative to the mapped fault length indicates a segmented basin-bounding fault system, where geometric irregularities and interactions with secondary structures constrain rupture propagation. The spatial coincidence of seismicity with extensive hydrothermal activity, including thermal springs and fissure-ridge travertine deposits, indicates a long-lived, permeable fault zone. Although the estimated stress drop ( 2.7 MPa) falls within the typical range of tectonic earthquakes, persistent background microseismicity and transient seismicity migration are consistent with a mechanically heterogenous fault network in which fluid-assisted processes may locally modulate fault behavior and rupture complexity. Documented Receiver-function results further indicate localized crustal thinning beneath the western basin margin, consistent with the preferential localization of deformation and seismicity along the HDF. At the basin scale, deformation is partitioned between a structurally organized western boundary and a more diffuse eastern margin, reflecting incomplete strain localization within an evolving pull-apart system. The progressive structural organization of the HDF suggests ongoing fault linkage and increasing localization of deformation along the western basin margin, highlighting the potential for future rupture propagation across adjacent fault segments and associated seismic hazard. This study demonstrates how moderate earthquake sequences can illuminate the interplay between structural inheritance, crustal architecture, fluid circulation, and fault-system evolution in transtensional settings.
Meteotsunamis are long-period sea-level oscillations generated by atmospheric disturbances, with periods comparable to those of earthquake-generated tsunamis, typically ranging from a few minutes to about 1–2 h. In the Mediterranean Sea, such events have repeatedly caused damaging coastal oscillations, including the event that struck Lampedusa Island on 11–12 May 2017. Although meteotsunamis are traditionally studied from oceanographic and meteorological perspectives, in this work we apply, for the first time in the Sicily Channel, the approach introduced by Okal (2007, 2021) for seismic tsunamis, assessing whether coastal seismic stations active at the time were capable of detecting and characterizing the meteotsunami signature. We analyze data from five seismic stations located across Lampedusa, Malta, and southeastern Sicily. After instrument correction and band-pass filtering in the 1–5 mHz range, we isolate the dominant horizontal motion through principal component analysis, reconstruct the equivalent sea-surface elevation using the Gilbert Response Function, and characterize directional properties through particle-motion analysis. The network captures a coherent and spatially variable long-period signature of the event across the Sicily Channel. At Lampedusa, where an independent tide gauge is also available, the reconstructed elevation reproduces the timing, dominant period and spectral coherence of the observed sea-level record, although it differs in absolute amplitude. The particle motion appears well-organized and site-specific. This case study shows how coastal broadband seismometers in the Sicily Channel can provide complementary information on the timing and directional characteristics of a meteotsunami, supporting their potential future use in the multi-parameter monitoring of meteo-marine hazards. ∙ Coastal seismic stations can detect a meteotsunami, an extreme sea-level event driven by the atmosphere ∙ The sea level reconstructed from seismic records reproduces timing and dominant period of nearby tide-gauge data ∙ Particle motion shows a well-polarized, site-dependent horizontal axis of the long-period signal
