Search Results (249)

The recently proposed concept of “precursory fingerprint” is a logical consequence of the commonsense statement that seismic structures are unique and that their expected preshock behaviors, including precursory phenomena, are also unique. Our new prediction-related research strategy is conceptually based on the principles of (1) the uniqueness of seismogenic structures, (2) interconnected and interacting geospheres, and (3) non-equivalence of Earth’s surface spots in terms of precursory signal receptivity. The precursory fingerprint of a given seismic structure is a unique assemblage of precursory signals of various natures (seismic, physical, chemical, and biological), detectable in principle by using a system of proper monitoring equipment that consists of a matrix of n sensors placed on the ground at “sensitive” spots identified beforehand and on orbiting satellites. In principle, it is composed of a combination of signals that are emitted by the “responsive sensors”, in addition to the “non-responsive sensors”, coming from the sensor matrix, monitoring as many virtual precursory processes as possible by continuously measuring their relevant parameters. Each measured parameter has a pre-established (by experts) threshold value and an uncertainty interval, discriminating between background and anomalous values that are visualized similarly to traffic light signals (green, yellow, and red). The precursory fingerprint can thus be viewed as a particular configuration of “precursory signals” consisting of anomalous parameter values that are unique and characteristic to the targeted seismogenic structure. Presumably, it is a complex entity that consists of pattern, space, and time components. The “pattern component” is a particular arrangement of the responsive sensors on the master board of the monitoring system yielding anomalous parameter value signals, that can be re-arranged, after a series of experiments, in a spontaneously understandable new pattern. The “space component” is a map position configuration of the signal-detecting sensors, whereas the “time component” is a characteristic time sequence of the anomalous signals including the order, occurrence time before the event, transition time between yellow and red signals, etc. Artificial intelligence using pattern-recognition algorithms can be used to follow, evaluate, and validate the precursory signal assemblage and, finally, to judge, together with an expert board of human operators, its “precursory fingerprint” relevance. Signal interpretation limitations and uncertainties related to dependencies on sensor sensibility, focal depth, and magnitude can be established by completing all three phases (i.e., experimental, validation, and implementation) of the precursory fingerprint-based earthquake prediction research strategy. Full article

The 2020 southern Puerto Rico earthquake sequence highlighted the severe seismic vulnerability of informally constructed two-story reinforced concrete (RC) houses. This study examines the failure mechanisms of these structures and assesses the effectiveness of first-floor RC shear-wall retrofitting. Nonlinear pushover and dynamic time–history analyses were performed using fiber-based distributed plasticity models for RC frames and nonlinear macro-elements for second-floor masonry infills, which introduced a significant inter-story stiffness imbalance. A bi-directional seismic input was applied using spectrally matched, near-fault pulse-like ground motions. The findings for the as-built structures showed that stiffness mismatches between stories, along with substantial strength and stiffness differences between orthogonal axes, resulted in concentrated plastic deformations and displacement-driven failures in the first story—consistent with damage observed during the 2020 earthquakes. Retrofitting the first floor with RC shear walls notably improved the performance, doubling the lateral load capacity and enhancing the overall stiffness. However, the retrofitted structures still exhibited a concentration of inelastic action—albeit with lower demands—shifted to the second floor, indicating potential for further optimization. Full article

Accurate and rapid prediction of structural responses to seismic loading is critical for ensuring structural safety. Recently, there has been active research focusing on the application of deep learning techniques, including Physics-Informed Neural Networks (PINNs) and Long Short-Term Memory (LSTM) networks, to predict the dynamic behavior of structures. While these methods have shown promise, each comes with distinct limitations. PINNs offer physical consistency but struggle with capturing long-term temporal dependencies in nonlinear systems, while LSTMs excel in learning sequential data but lack physical interpretability. To address these complementary limitations, this study proposes a hybrid LSTM-PINN model, combining the temporal learning ability of LSTMs with the physics-based constraints of PINNs. This hybrid approach allows the model to capture both nonlinear, time-dependent behaviors and maintain physical consistency. The proposed model is evaluated on both single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) structural systems subjected to the El-Centro ground motion. For validation, the 1940 El-Centro NS earthquake record was used, and the ground acceleration data were normalized and discretized for numerical simulation. The proposed LSTM-PINN is trained under the same conditions as the conventional PINN models (e.g., same optimizer, learning rate, and loss structure), but with fewer training epochs, to evaluate learning efficiency. Prediction accuracy is quantitatively assessed using mean error and mean squared error (MSE) for displacement, velocity, and acceleration, and results are compared with PINN-only models (PINN-1, PINN-2). The results show that LSTM-PINN consistently achieves the most stable and precise predictions across the entire time domain. Notably, it outperforms the baseline PINNs even with fewer training epochs. Specifically, it achieved up to 50% lower MSE with only 10,000 epochs, compared to the PINN’s 50,000 epochs, demonstrating improved generalization through temporal sequence learning. This study empirically validates the potential of physics-guided time-series AI models for dynamic structural response prediction. The proposed approach is expected to contribute to future applications such as real-time response estimation, structural health monitoring, and seismic performance evaluation. Full article

Aftershocks are inevitable phenomena following a mainshock, especially after a major earthquake. However, the cumulative damage caused by aftershocks and its impact on structural performance evaluation has only recently received significant attention. This study explores the effects of mainshock–aftershock (MS–AS) sequences, including multiple consecutive aftershocks, acting on 3D steel moment-resisting frame structures. Following nonlinear time history analysis, several fundamental variables such as residual interstory drift, maximum displacement, plastic hinge formation, and base shear are evaluated to examine cumulative damage. In this context, the findings depicted in terms of aftershocks play a significant role in exacerbating plastic deformations and damage accumulation in steel moment frames. Subsequently, to mitigate cumulative damage on steel moment frames, retrofitting strategies were implemented. Retrofitting strategies effectively reduce cumulative damage and improve seismic resilience under multiple earthquake events. This research highlights the limitations of single-event seismic assessments and the need to incorporate sequential earthquake effects in design and retrofit practices. Furthermore, it provides new insights into mitigating further damage by retrofitting existing structures under multiple earthquakes. Full article

Enhancing the seismic resilience of urban water distribution networks (WDNs) requires the improvement of both earthquake resistance and rapid recovery capabilities within the system. This paper proposes a multi-objective method to enhance the seismic resilience of the WDNs, focusing on system restoration capabilities while comprehensively considering the hydraulic recovery index, maintenance time, and maintenance cost. The method utilizes a random simulation approach to generate various damage scenarios for the WDN, considering pipe leakage, pipe bursts, and variations in node flow resulting from changes in water pressure. It characterizes the functions of the WDN through hydraulic service satisfaction and quantifies system resilience using a performance response function. Additionally, it determines the optimal dispatch strategy for emergency repair teams and the optimal emergency repair sequence for earthquake-damaged networks using a genetic algorithm. Furthermore, a comprehensive computational platform has been developed to systematically analyze and optimize seismic resilience strategies for WDNs. The feasibility of the proposed method is demonstrated through an example involving the WDN in Xi’an City. The results indicate that the single-objective seismic resilience improvement method based on the hydraulic recovery index is the most effective for enhancing the seismic resilience of the WDN. In contrast, the multi-objective method proposed in this article reduces repair time by 17.9% and repair costs by 3.4%, while only resulting in a 0.2% decrease in the seismic resilience of the WDN. This method demonstrates the most favorable comprehensive restoration effect, and the success of our method in achieving a symmetrically balanced restoration outcome demonstrates its value. The proposed methodology and software can provide both theoretical frameworks and technical support for urban WDN administrators. Full article

On 18 December 2023, a M_w 6.2 earthquake occurred in close proximity to Jishishan County, located on the northeastern edge of the Qinghai–Tibet Plateau. The event struck the structural intersection of the Haiyuan fault, Lajishan fault, and West Qinling fault, providing empirical evidence for investigating the crustal compression mechanisms associated with the northeastward expansion of the Qinghai–Tibet Plateau. In this study, we successfully acquired a high-resolution coseismic deformation field of the earthquake by employing interferometric synthetic aperture radar (InSAR) technology. This was accomplished through the analysis of image data obtained from both the ascending and descending orbits of the Sentinel-1A satellite, as well as from the ascending orbit of the ALOS-2 satellite. Our findings indicate that the coseismic deformation is predominantly localized around the Lajishan fault zone, without leading to the development of a surface rupture zone. The maximum deformations recorded from the Sentinel-1A ascending and descending datasets are 7.5 cm and 7.7 cm, respectively, while the maximum deformation observed from the ALOS-2 ascending data reaches 10 cm. Geodetic inversion confirms that the seismogenic structure is a northeast-dipping thrust fault. The geometric parameters indicate a strike of 313° and a dip angle of 50°. The slip distribution model reveals that the rupture depth predominantly ranges between 5.7 and 15 km, with a maximum displacement of 0.47 m occurring at a depth of 9.6 km. By integrating the coseismic slip distribution and aftershock relocation, this study comprehensively elucidates the stress coupling mechanism between the mainshock and its subsequent aftershock sequence. Quantitative analysis indicates that aftershocks are primarily located within the stress enhancement zone, with an increase in stress ranging from 0.12 to 0.30 bar. It is crucial to highlight that the structural units, including the western segment of the northern margin fault of West Qinling, the eastern segment of the Daotanghe fault, the eastern segment of the Linxia fault, and both the northern and southern segment of Lajishan fault, exhibit characteristics indicative of continuous stress loading. This observation suggests a potential risk for fractures in these areas. Full article

The 6 February 2023 M_W 7.8 and M_W 7.6 earthquakes in southeastern Türkiye ruptured more than 400 km of the East Anatolian Fault Zone (EAFZ), producing one of the most destructive seismic sequences in recent history. Here, we integrate InSAR data, a new GNSS velocity field, and small-magnitude earthquakes to investigate the coseismic deformation, rupture geometry, and interseismic strain accumulation along the EAFZ. Using elastic dislocation modeling with a variable-strike, multi-segment fault geometry, we constrain the slip distribution of the mainshocks, showing improved fits to the surface displacement compared to the planar fault model. The M_W 7.8 event ruptured a number of fault segments over ~300 km, while the M_W 7.6 event activated a more localized fault system with a peak slip exceeding 15 m. We also model two moderate events (M_W 5.6 in 2020 and M_W 5.3 in 2022) along the southwestern part of the Pütürge segment—an area not ruptured during the 2020 or 2023 sequences. GNSS-derived strain-rate and locking depth estimates reveal strong interseismic coupling and significant strain accumulation in this region, suggesting the potential for a future large earthquake (M_W 6.6–7.1). Similarly, the Hatay region, at the southwestern termination of the 2023 rupture, shows a persistent strain accumulation and complex fault interactions involving the Dead Sea Fault and the Cyprus Arc. Our results demonstrate the importance of combining remote sensing and geodetic data to constrain fault kinematics, evaluate rupture segmentation, and assess the seismic hazard in tectonically active regions. Targeted monitoring at rupture terminations—such as the Pütürge and Hatay sectors—may be crucial for anticipating future large-magnitude earthquakes. Full article

To improve the post-earthquake resilience evaluation of concrete buildings with various construction types, this study presents a generalized recovery-based framework that ext-ends the FEMA P-58 methodology. The proposed method introduces a dynamic repair scheduling approach that incorporates two key construction-related parameters: the prefabrication ratio and the types of prefabricated components. These inputs govern the allocation of parallel or sequential repairs, enabling a more accurate estimation of recovery trajectories and downtime. Functional loss over time is modeled through component-level repair sequencing combined with mobilization delays. A case study involving three four-story prestressed concrete frame buildings (cast-in situ, partially prefabricated, and fully precast prestressed concrete (PCaPC) with mortise–tenon (MT) connections) demonstrated the framework’s applicability. The results show that higher prefabrication levels lead to significantly shorter median repair times, with up to a 97-day reduction observed for the fully prefabricated frame. Additionally, recovery differences emerge even between buildings with the same prefabrication ratio but different component configurations. Compared to conventional assessment methods, the proposed framework avoids the overestimation of mobilization and repair duration, offering a practical tool for the design and performance assessment of resilient precast and hybrid concrete building systems. Full article

In early 2025, the Santorini–Amorgos area (Aegean Volcanic Arc, Greece) experienced a seismic swarm, with dozens of M ≥ 4.0 earthquakes and a maximum magnitude of M = 5.2. Beyond its seismological interest, the sequence was notable for triggering rare increased preparedness actions by Greek Civil Protection operational structures in anticipation of an imminent destructive earthquake. These actions included (i) risk communication, (ii) the reinforcement of operational structures with additional personnel and equipment on the affected islands, (iii) updates to local emergency plans, (iv) the dissemination of self-protection guidance, (v) the activation of emergency alert systems, and (vi) volunteer mobilization, including first aid and mental health first aid courses. Although it was in line with contingency plans, public participation was limited. Volunteers helped bridge this gap, focusing on vulnerable groups. The implemented actions in Greece are also compared with increased preparedness during the 2024–2025 seismic swarms in Ethiopia, as well as preparedness before the highly anticipated major earthquake in Istanbul (Turkey). In Greece and Turkey, legal and technical frameworks enabled swift institutional responses. In contrast, Ethiopia highlighted the risks of limited preparedness and the need to embed disaster risk reduction in national development strategies. All cases affirm that preparedness, through infrastructure, planning, communication, and community engagement, is vital to reducing earthquake impacts. Full article

A complex study was performed of the disturbances in geophysics parameters that were observed during a short-term period of earthquake preparation. On 4 March 2024, an $M6.1$ earthquake (N 42.93, E 76.966) occurred with the epicenter 12.2 km apart from the complex of geophysical monitoring. Preparation of the earthquake we detected in real time, 8 days prior to the main shock, when a characteristic cove-like decrease appeared in the gamma-ray flux measured 100 m below the surface of the ground, which observation indicated an approaching earthquake with high probability. Besides the gamma-ray flux, anomalies connected with the earthquake preparation were studied in the variation of the Earth’s natural pulsed electromagnetic field (ENPEMF) at very low frequencies (VLF) $f=7.5$ kHz and $f=10.0$ kHz and at ultra-low frequency (ULF) in the range of 0.001–20 Hz, as well as in the shift of Doppler frequency (DFS) of the ionospheric signal. A drop detected in DFS agrees well with the decrease in gamma radiation background. A sequence of disturbance appearance was revealed, first in the variations of ENPEMF in the VLF band and of the subsurface gamma-ray flux, both of which reflect the activation dynamic of tectonic processes in the lithosphere, and next in the variation of DFS. Two types of earthquake-connected effects may be responsible for the transmission of the perturbation from the lithosphere into the ionosphere: the ionizing gamma-ray flux and the ULF/VLF emission, as direct radiation from the nearby earthquake source. In the article, we emphasize the role of medium ionization in the propagation of seismogenic effects as a channel for realizing the lithosphere–ionosphere coupling. Full article

This study presents a seismic tomography analysis of the Târgu Jiu region in southwestern Romania, an area that experienced an unusual earthquake sequence in 2023. Using P- and S-wave arrival times local earthquakes, we applied the LOTOS algorithm to produce high-resolution 3D crustal seismic velocities models. High Vp and Vs values in the northern and northeastern areas suggest the presence of dense, rigid geological formations, likely associated with consolidated magmatic or metamorphic units. In contrast, the central region exhibits low Vs values, coinciding with an active seismic zone and intersecting major fault structures. This suggests the presence of highly fractured and weakly consolidated rocks, potentially saturated with fluids. The Vp/Vs ratio in the central region reached values of ≥1.8–1.9, indicating fluid-filled fractures that may influence fault dynamics and earthquake occurrence. In the southern region, velocity anomalies suggest weakly consolidated sedimentary units with a high degree of fracturing. These findings contribute to a better understanding of the geodynamic behavior of the Târgu Jiu area and its seismic hazard potential. Full article

A timely, high-resolution earthquake catalog is crucial for estimating seismic evolution and assessing hazards. This study aims to introduce a deep-learning-based real-time microearthquake monitoring system (RT-MEMS) for Taiwan, designed to provide rapid and reliable earthquake catalogs. The system integrates continuous data from high-quality seismic networks via SeedLink with deep learning models and automated processing workflows. This approach enables the generation of an earthquake catalog with higher resolution and efficiency than the standard catalog announced by the Central Weather Administration, Taiwan. The RT-MEMS is designed to capture both background seismicity and earthquake sequences. The system employs the SeisBlue deep learning model, trained with a local dataset, to process continuous waveform data and pick P- and S-wave arrivals. Earthquake events are then associated and located using a modified version of PhasePAPY. Three stable RT-MEMS have been established in Taiwan: one for monitoring background seismicity along a creeping fault segment and two for monitoring mainshock–aftershock sequences. The system can provide timely information on changes in seismic activity following major earthquakes and generate long-term catalogs. The refined catalogs from RT-MEMS contribute to a more detailed understanding of seismotectonic structures and serve as valuable datasets for subsequent research. Full article

An estimate of the trigger effect of the proton flux on seismicity was obtained. The proton flux time series with a time step of 5 min, 2000–2024, was analyzed. In each time interval of 5 days, statistics of the proton flux time series were calculated: mean values, logarithm of kurtosis, spectral slope, singularities spectrum support width, wavelet-based entropy, and the Donoho–Johnston wavelet-based index. For each of the used statistics, time points of local extrema were found, and for each pair of time sequences of proton flux statistics and earthquakes with a magnitude of at least 6.5 in sliding time windows, the “advance measures” of each time sequence relative to the other were estimated using a model of the intensity of interacting point processes. The difference between the “direct” measure of the advance of time points of local extrema of proton flux statistics relative to the time moments of earthquakes and the “inverse” measure of the advance was calculated. The maximum proportion of the intensity of seismic events for which the proton flux was a trigger was estimated as 0.28 for using the points of the local minima of the singularities spectrum support width. Full article

Post-earthquake structural rehabilitation faces critical challenges from aftershock-induced cumulative damage, particularly through residual displacement accumulation that compromises structural realignment feasibility. While residual displacements serve as pivotal indicators for repair-or-replace decisions, the amplification effects of aftershocks on such displacements remain systematically underexplored. This study investigates residual displacement demands of bilinear single-degree-of-freedom (SDOF) systems subjected to mainshock–aftershock sequences. A novel metric is proposed, defined as the maximum residual displacement considering both isolated mainshock and full sequence scenarios, normalized against peak inelastic displacements (termed residual displacement ratio) for predictive analysis. The influence of sequence characteristics (duration, frequency content, aftershock intensity) and structural properties (post-yield stiffness ratio, displacement ductility, natural period) on residual displacement ratios is evaluated. Statistical analysis reveals that aftershocks amplify mainshock-induced residual displacements in the statistical mean sense, with an observed maximum increase reaching up to 72%. The mainshock with stronger aftershocks tends to result in larger residual displacement ratios. A constant-ductility residual displacement ratio response spectrum is finally developed for the repairability assessment of structures against mainshock–aftershock sequences in terms of residual displacements. Full article

The study of the hypocenter distribution of seismic events related to fault structures is a crucial topic since it is linked to the geological features and to the dynamics of an investigated area. The hypocenter spatial distribution of earthquakes is used in a novel algorithmic method to clusterize earthquakes to accurately identify the strike and the dip parameters of seismogenic faults. Our algorithm works as a fast and efficient explorer in a five-dimensional space (x, y, z, τ, ϕ). It randomly selects several seismic events (pivots) and counts in all angular directions, for each pivot, how many hypocenters can be included in a prefixed volume (two dimensions larger than the third). The result is a volume that contains the maximum number of earthquakes occurring within a minimum distance from a flat area corresponding to the searched fault. With this volume is associated a hypocenter occurrence density angular diagram and a likelihood function. The likelihood function is useful to individuate the best value of the fault thickness and to test the hypothesis of fault flatness. Our algorithm was tested on simulated data and then successfully applied to the real case of the 2009 Mw6.1 L’Aquila (Central Italy) seismic sequence. Full article