Seismic Activity Research Articles

Characteristics and initiation mechanism of the large mudstone Dongping landslide induced by heavy rainfall in Gansu Province, NW China

Abstract Background At approximately 4:00 PM on 18 July 2023, a heavy rainstorm lasting one hour triggered a significant mudstone landslide in Dongping, Weiyuan County, Gansu Province, Northwest China. The landslide resulted in the burial of houses, the fracturing and destruction of roads, and posed a serious threat to 16 households. The estimated economical loss from this disaster reached 3.2 million yuan. This study presents a detailed field investigation of the Dongping landslide, focusing on the deformation and failure characteristics through a multi-layered analysis of sliding strata, rock mass structure, slope configuration, and failure mechanism. Moreover, the study explores the key triggering factors of the Dongping landslide, with particular attention to the roles of seismic activity, rainfall, and preferential flow in the development of large-scale mudstone landslides. Results The stratigraphic profile of the Dongping landslide reveals a two-layer structure, consisting of overlying loess and underlying mudstone, with the sliding surface primarily located within the underlying Neogene red mudstone. The initiation location of the Dongping landslide is situated at the rear of the slope, while the main slip-resistant section is located in the middle section of the landslide, exhibiting a predominantly thrust-sliding. After encountering resistance in the middle section, the front part of the sliding mass continued to move, leading to the formation of secondary landslides. The overall movement of the Dongping landslide is characterized by rotational sliding, with the sliding mass remaining relatively intact. Conclusions The initiation of the large-scale mudstone landslide in Dongping was driven by multiple factors. The heavy rainfall served as the direct triggering factor for the landslide occurrence. However, some historical factors, including seismic activity and previous sliding surface, had already weakened the slope structure by degrading the mechanical properties of the landslide mass and creating preferential flow channels, thereby setting the stage for the Dongping landslide. Structural fractures in the landslide area, along with sinkholes formed by a combination of tectonic joints, soil properties, and human activities, constituted preferential seepage pathways for water within the slope. These pathways provided the hydraulic conditions necessary for rainfall-induced landslides, making them the primary controlling factors in the occurrence of the Dongping landslide.

Seismic Design of Underground Structures’ Vulnerability – Review.

This article examines the vulnerability of underground structures to seismic activity, with a particular focus on design and mitigation strategies. Underground buildings are essential to the development of modern infrastructure and must include tunnels, subway stations, and storage facilities. Because of its deep location, seismic design and earthquake performance are both more challenging. In this study of seismic susceptibility, topics such as ground motion characterization, soil-structure interaction, structural reaction, and failure mechanisms specific to underground structures are discussed. According to the paper, their susceptibility can be affected by geotechnical conditions, the kind of structures they have, and the construction methods that are used. The seismic stability design and analysis process involves measuring the dynamic response of subsurface structures to seismic loads. The goal is to keep earthquake and seismic forces from toppling structures in any way that is possible. Advanced analytical and numerical approaches are used in seismic stability design. Some examples of these methods are finite element analysis (FEA), discrete element modeling (DEM), and the boundary element method (BEM). In addition to that, it investigates contemporary methodologies for assessing seismic vulnerability as well as international design standards. In addition, seismic retrofitting and design advances for underground structures are analyzed and discussed in this paper. This document compiles and evaluates previous research to gain an understanding of the seismic susceptibility of subsurface structures and to promote more seismic engineering research on this extremely important topic.

Immiscible metamorphic water and methane fluids preserved in carbonated eclogite.

Subduction zones metamorphic fluids are pivotal in geological events such as volcanic eruptions, seismic activity, mineralization, and the deep carbon cycle. However, the mechanisms governing carbon mobility in subduction zones remain largely unresolved. Here we present the first observations of immiscible H2O-CH4 fluids coexisting in retrograde carbonated eclogite from the Western Tianshan subduction zone, China. We identified two types of fluid inclusions in host ankerite and amphibole, as well as in garnet and omphacite. Type-1 inclusions are water-rich with CH4 vapor, whereas Type-2 are CH4-rich, with minimal or no H2O. The coexistence of these fluid types indicates the presence of immiscible fluid phases under high-pressure conditions (P = 1.3-2.1 GPa). Carbonates in subduction zones can effectively decompose through reactions with silicates, leading to the generation of abiotic CH4. Our findings suggest that substantial amounts of carbon could be transferred from the slab to mantle wedge as immiscible CH4 fluids. This process significantly enhances decarbonation efficiency and may contribute to the formation of natural gas deposits.

2021 Alaska earthquake: entropy approach to its precursors and aftershock regimes

Abstract. We have conducted an entropy analysis in Alaska, a seismic-rich region in a subduction zone that exhibits a nontrivial behavior: the subduction arc alters the seismic activity from the eastern zone to the western zone, demonstrating a decrease in activity along the subduction. We analyze this zone through the Tsallis entropy and the mutability (or dynamic entropy) for the first time. Considering 13 870 seismic events after appropriate filtering, we analyzed a data set for the selected Alaska zone between 2000 and 2023. We have found agreement between the results for the two entropies. We have followed the value of the q parameter of the Tsallis entropy (Sq) finding values between 1.70 and 1.85, in concordance with values found in other seismic regions of the planet. The values of Sq decrease slightly over time but show a broad increase before the major earthquakes. Just opposite to Tsallis entropy, mutability shows a tendency to decrease prior to the major earthquakes. We used the simpler mutability method to further analyze this zone upon dividing the region into four subzones. The results show how mutability can identify the seismic activity in each zone. This study shows how an entropy approach can shed light on understanding the seismicity in subduction zones.

Seismic Risk Assessment for the City of Sofia, Bulgaria

An earthquake is the most destructive natural phenomenon, with its sudden onset and rapid spread impacting large areas. Among the various geohazards, seismic ones dominate in terms of their social and economic effects on human life and the urban environment. In the present study, a deterministic earthquake ground motion scenario for the city of Sofia (the capital of Bulgaria) is presented. The earthquake risk posed to Sofia is quantified by considering the city’s seismic context, which contributes to its hazard and risk. The assessment of seismic hazards and the generation of earthquake scenarios make up the first step of seismic risk evaluations, as risk reduction strategies can only be developed with a better understanding of these threats. Risk assessment and its associated management comprise the most effective approach to estimating the impact of seismic hazards on the city of Sofia, which exhibits high seismic activity. Our findings provide a basis for local governments to review their susceptibility and preparedness. The consideration of earthquake scenarios in the creation of policies for seismic risk reduction will allow us to focus on the prevention of earthquake effects rather than on the activities following these disasters.

Analysis of the Liquefaction Potential at the Base of the San Marcos Dam (Cayambe, Ecuador)—A Validation in the Use of the Horizontal-to-Vertical Spectral Ratio

Ground liquefaction potential analysis is a fundamental characterization in areas with continuous seismic activity, such as Ecuador. Geotechnical liquefaction studies are usually approached from dynamic penetration tests, which pose problems both in their correct execution and in their evaluation. Our research involves analyzing dynamic penetration tests and microtremor geophysical surveys (horizontal-to-vertical spectral ratio technique, HVSR) for analyzing the liquefaction potential at the base of the San Marcos dam, a reservoir located in Cayambe canton (Ecuador). Based on the investigations performed at the time of construction of the dam (drilling and geophysical refraction profiles) and the application of 20 microtremor observation stations via the HVSR technique, an analysis of the safety factor of liquefaction (SFliq) was conducted using the 2001 Youd and Idriss formulation and the values of the standard penetration test (SPT) applied in granular materials (sands). In addition, the vulnerability index (Kg) proposed by Nakamura in 1989 was analyzed through the HVSR records related to the ground shear strain (GSS). The results obtained in the HVSR analysis indicate the presence of a zone of about 100 m length in the central part of the foot of the dam, whose GSS values identified a condition of susceptibility to liquefaction. In the same area, the SPT essays analysis in the P-8A drill hole also shows a potential susceptibility to liquefaction in earthquake conditions greater than a moment magnitude (Mw) of 4.5. That seismic event could occur in the area, for example, with a new activity condition of the nearby Cayambe volcano or even from an earthquake from the vicinity of the fractured zone.

Triggering the 2022 eruption of Mauna Loa.

Distinguishing periods of intermittent unrest from the run-up to eruption is a major challenge at volcanoes around the globe. Comparing multidisciplinary monitoring data with mineral chemistry that records the physical and spatio-temporal evolution of magmas fundamentally advances our ability to forecast eruptions. The recent eruption of Mauna Loa, Earth's largest active volcano, provides a unique opportunity to differentiate unrest from run-up and improve forecasting of future eruptions. After decades of intermittent seismic and geodetic activity over 38 years of repose, Mauna Loa began erupting on 27 November 2022. Here we present a multidisciplinary synthesis that tracks the spatio-temporal evolution of precursory activity by integrating mineral and melt chemistry, fluid inclusion barometry, numerical modeling of mineral zoning, syn-eruptive gas plume measurements, the distribution and frequency of earthquake hypocenters, seismic velocity changes, and ground deformation. These diverse data indicate that the eruption occurred following a 2-month period of sustained magma intrusion from depths of 3-5 km up to 1-2 km beneath the summit caldera, providing a new model of the plumbing system at this very high threat volcano. Careful correlation of both the geochemistry and instrumental monitoring data improves our ability to distinguish unrest from the run-up to eruption by providing deeper understanding of the both the monitoring data and the magmatic system-an approach that could be applied at other volcanic systems worldwide.

Investigation on the transmission of Love waves due to an impulsive line source in a heterogeneous double porous rock structure.

The region around the reservoir sometimes encounters the seismic response of ground. The phenomena of Love waves propagation also caused by seismic activity which can further significantly influenced by an impulsive line source present in the rock structures of ground. The current study elucidates the propagation behavior of Love waves induced by an impulsive line source within the anisotropic layered heterogeneous double porous rock structures commonly found in proximity to the reservoir areas. The expression of the dispersion relation for the Love waves propagation due to an impulsive line source has been derived by using the Fourier transformation and Green's function technique. The above-mentioned rock composite structure is consisted of two different portions: upper transversely isotropic double porous (TIDP) layer medium and lower heterogeneous isotropic double porous half-space (IDP) medium. In the aforesaid half-space medium, both types of heterogeneities viz. linear and quadratic have been considered. The impacts of heterogeneity parameters associated with the lower heterogeneous IDP half-space, porosity parameter (corresponds to both upper TIDP layer and lower IDP half-space) and anisotropic parameter of TIDP layer on Love waves phase velocity have also been studied. Some special outlines have also been depicted graphically.

Classification of images derived from submarine fibre optic sensing: detecting broadband seismic activity from hydroacoustic signals

Abstract Distributed acoustic sensing (DAS) is an optoelectronic technology that utilises fibre optic cables to detect disturbances caused by seismic waves. Using DAS, seismologists can monitor geophysical phenomena at high spatial and temporal resolutions over long distances in inhospitable environments. Field experiments using DAS, are typically associated with large volumes of observations, requiring algorithms for efficient processing and monitoring capabilities. In this study, we present a supervised classifier trained to recognise seismic activity from other sources of hydroacoustic energy. Our classifier is based on a 2D convolutional neural network architecture. The 55km-long ocean-bottom fibre optic cable, located off Cape Muroto in south-west of Japan, was interrogated using DAS. Data were collected during two different monitoring time-periods. Optimisation of the model’s hyperparameters using Gaussian Processes Regression was necessary to prevent issues associated with small sizes of training data. Using a test set of 100 annotated images, we have shown that the top performing model is around $92\%$ accurate in classifying geophysical data from other sources of hydroacoustic energy and ambient noise.

Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities

Abstract Enhancing resilience against seismic hazards in earthquake-prone regions is essential for reducing the devastating impacts of disasters. Seismic resilience refers to a community’s ability to withstand and recover from earthquake impacts, while preparedness gaps are the areas where current measures are insufficient to effectively respond to or mitigate earthquake damage. This study focuses on Montenegro – a region with frequent seismic activity – aiming to assess resilience levels, identify critical gaps in preparedness, and evaluate the effectiveness of existing response strategies. Using qualitative methods, including semi-structured interviews, the research gathered insights from residents of Montenegro’s most vulnerable cities: Nikšić, Podgorica, Bar, Kotor, Cetinje, Budva, Herceg Novi, and Berane. Participants, chosen for their first-hand experience with significant earthquake impacts, provided valuable perspectives on various aspects of resilience, from local government response to individual preparedness. This research revealed significant disparities in resilience across communities: for instance, approximately 62.5% of the respondents highlighted inadequate education as a barrier to effective earthquake preparedness, and only 37.5% reported awareness of basic earthquake response procedures. Furthermore, while some communities, such as urban areas with accessible services, reported higher preparedness levels, rural areas showed deficiencies, with 50% of the respondents from these areas identifying a lack of organized drills and limited public awareness initiatives. These findings underscore the urgent need for community-specific preparedness programs and enhancements in both structural resilience and public education to bolster community readiness effectively. Also, findings highlight the need for customized preparedness programs tailored to specific community needs, alongside improvements in structural safety measures and educational outreach. The study underscores the importance of a comprehensive approach involving detailed risk assessments, community-focused preparedness training, and stronger public awareness initiatives. Furthermore, the study calls for enhanced local government capabilities to sustain proactive response measures, including rapid mobilization of emergency resources and regular disaster simulations, to build long-term resilience across communities.

A Six-Year (2014–2020) Statistical Correlation Study of VLF Terminator Time Shift with Earthquakes in Japan

In this study, we present a six-year (2014–2020) statistical analysis of VLF subionospheric propagation data at 19 VLF receivers from the VLF transmitter with call name JJI and frequency 22.2 kHz, all located in Japan. Moderate and strong earthquakes (EQs) (ML≥4.5 and depth ≤ 50 km) that occurred in the wider area around Japan during the same time period with the available VLF data are investigated. The terminator times’ (TT) shift in VLF amplitude data as a possible precursor of an EQ are statistically examined, focusing on the correlation with seismic activity. The concept of the effective EQ magnitude (Meff) is used in order to define the total EQ energy possibly affecting the midpoint of each path for each day. It is important to note that dates when geomagnetic storms or solar flares occurred as well as dates corresponding to the already known winter effect on TT statistics in the north–south direction were excluded. The cross-correlation between TT statistical anomalies and seismic activity, represented by Meff, was extracted. Maximum cross-correlation values were found for most of the cases prior to the subsequent seismic activity, indicating a link between the ionospheric anomalies and the subsequent seismicity. Finally, the wide temporal range of the cross-correlation maxima temporal locations is justified by the inhomogeneity of the lower ionosphere, coupled with the anisotropy of the preseismic effect of the impending seismicity, highlighting the complexity of the EQ preparation processes.

Investigating the Performance of Data-Driven Ensemble Machine Learning Models in Preliminary Designing Multi-Stage Friction Pendulum Bearings

Over the last few decades, the field of base isolation systems has demonstrated enormous advancements through the innovation of novel systems, and there has been an enhancement in the performance of structures equipped with isolation under conditions of moderate and severe seismic activity. One of the most important systems is the multi-stage friction pendulum, which offers a spectrum of effective pendulums across different regimes, thereby achieving enhanced capability for energy dissipation. The difficulty in designing these bearings at the preliminary stage comes from the fairly long process of trials and errors in selecting the properties of each sliding surface so that the required effective period, effective damping, and displacement capacities are achieved. This study investigates the performance of various data-driven ensemble machine learning models in directly designing multi-stage friction pendulum bearings. Within the context of the analysis, the most recent generation of friction pendulum that has been introduced to the literature, the quintuple friction pendulum, is used as the investigation employs a case to assess the dependability of the design strategy and the efficacy of a multi-output machine learning techniques. In general, the findings of this study have shown that machine learning models provide an accurate way to directly design quintuple friction pendulum isolators and attain the necessary properties of the isolators. The practicality of this research lies in highlighting how machine learning can be applied to considerably expedite the design and optimization of multi-stage friction pendulum bearings and in defining the most suitable machine learning technique to adopt in such tasks.

Dynamic response of rock landslides and avalanche debris flows impacting flexible barriers based on shaking table tests

Flexible barrier structures are commonly installed in mountainous regions to resist the impact effects of rock avalanches. Without reliable physical data, the study of rock landslide initiation and the impact mechanism of avalanche debris flow under seismic excitation remains poorly understood. In this study, a model of rock slope-flexible barrier system was developed to simulate seismic slope failure behavior and debris flow impact on flexible barriers. Seismic waves in shaking table tests were triaxially loaded to effectively simulate actual seismic responses. The results indicate that the response of the Acceleration Amplification Factor (AAF) is closely associated with seismic damage and deformation within the overlying rock layers and differential propagation of seismic energy on either side of the shear band triggers rockslide initiation. Furthermore, the progressive failure mode of modeled slopes under multiple seismic events is slip-compression cracking failure. Crushing spreading at the intersection of the cracking and slip surfaces is the source of the loose fractured rock mass. Finally, this paper examines the impact patterns and dynamic responses of large individual blocks and loose fractured deposits on flexible barriers. Large blocks cause localized strong acceleration in the steel wire nets, increasing the risk of local damage, whereas loose deposits tend to induce overall seismic deformation and instability of the supporting structure. The natural seismic damage mechanisms of the barrier structure are revealed. It is recommended that flexible barrier structures in earthquake-prone mountainous areas incorporate a reasonable footing design to ensure the columns can deflect out-of-plane in response to seismic activity.

Seismic Activity Analysis in California: Patterns, Trends, and Predictive Modeling

For decades, California has been considered one of the most seismically active areas in the United States, if not the most, due to the frequent occurrence of earthquakes from tectonic activity, notably along the San Andreas Fault. Understanding seismic activity is a matter not only of safety but also of major importance for urban planning. In the recent past, Machine learning algorithms (ML) have emerged as a promising invention for advancing earthquake prediction by facilitating the pinpointing of patterns in large datasets that may be hard to detect through traditional techniques. The primary objective of this research project is to assess historical seismic data to identify trends and patterns in California’s seismic activity. Equally important, the goals of this research focused on developing predictive models that can provide insight into the possibility of seismic events in the future. To assess seismic activity in California, data were gathered from a credible and reputable source, most notably, the United States Geological Survey (USGS) Earthquake Database, which provides detailed records of earthquakes spanning over six decades. This dataset included all recorded seismic events in California, capturing the key details about the place of occurrence in latitude and longitude, the magnitude of the seismic event, the depth at which it happened, and the time when the seismic activity took place. To devise and curate a reliable earthquake prediction algorithm for California, distinct machine learning models were considered, comprising, Random Forest, XG-Boost, and Logistic Regression. Overall, the Random Forest algorithm exemplified high accuracy. Optimizing this algorithm will lead to more reliable predictions, potentially aiding disaster preparedness and risk mitigation in California's earthquake-prone areas. The findings from seismic activity analysis have deep implications for urban planning and disaster preparedness in California. Knowledge of the pattern, place, and magnitude of earthquakes over time will help urban planners and policymakers structure communities that can remain resilient during such calamities. For instance, higher earthquake frequencies would automatically call for increased stringency in building codes, especially along fault lines, to ensure that houses situated on such lines are designed to withstand major seismic activity. This operation may even limit or reallocate urban development out of high-risk zones into less risky zones.

An Integrated Approach for Structural Crack Monitoring: Combining Plastic Tell Tales and Image Analysis for Enhanced Structural Health Evaluation

Structural Health Monitoring (SHM) calls for the development of the ideas of safety and main ability of civil structures. The most critical of the aspects explored in the paper is the safety monitoring of the structural configuration of any civil engineering work. Supervision of any sign of an odd shift from the usual state of structure enables one to prevent and or counter severe loss. It also depends on other environmental parameters such as load, nature of a seasonal parameter and the type of soil. Within this research project the Al-Beruni Academic Block of Sarhad University of Science and Information Technology is taken to monitor the cracks using Glass Tell-Tales, to collect data on cracks of buildings and during seismic activity, observe, study the cracks that are present.

Seismic activity characteristics of the Chinese continent based on a “hybrid” probability forecasting model

Seismic activity characteristics of the Chinese continent based on a “hybrid” probability forecasting model

Using Resistivity Structure to Study the Seismogenic Mechanism of the 2021 Luxian Ms6.0 Earthquakes

Over the past few years, there has been a noticeable change in the occurrence of seismic disasters in Sichuan, China. The focus has shifted from Western Sichuan to the previously more stable Southeastern Sichuan. The recent Ms6.0 earthquake in Luxian, Southeastern Sichuan, on 16 September 2021, has once again captured the interest of scholars, who are closely examining the seismogenic environment and potential seismic hazards in the region. We conducted a magnetotelluric (MT) array survey in the Luxian earthquake area to explore the deep seismogenic environment of the 2021 Luxian Ms6.0 earthquake zone and understand the potential effects of industrial extraction on seismic activities. Here are the insights we obtained: Underneath the anticline in the Luxian Ms6.0 earthquake area, there is a structure that mainly exhibits high resistance. On the other hand, beneath the syncline, a structure with medium to low resistance is observed. The epicenter of the mainshock was identified near the intersection of high- and low-resistance media within the Fuji syncline area. Smaller aftershocks that followed the mainshock were mainly concentrated in the low-resistance layers at depths of 3–5 km in the Fuji syncline area. MT survey results have confirmed the existence of a detachment zone in the shallow crust near the epicenter of the Luxian Ms6.0 earthquake. It is believed that this detachment layer played a significant role in the seismogenic process of the Luxian Ms6.0 earthquake. During different stress conditions, this layer became active and caused the compression and faulting of a hidden fault below, resulting in the Luxian Ms6.0 earthquake. After the main earthquake, a series of smaller aftershocks with varying focal mechanisms occurred as the stress fields continued to release. It is important to note that the Luxian Ms6.0 earthquake highlights the ongoing high stress levels in the southern region of the Sichuan Basin. This emphasizes the need for continued monitoring and consideration of potential seismic hazards in the southern Sichuan area.

Single Station Seismic Event Location With a Neural‐Guided Mixture Model: Model‐Based Weighting for Improved Accuracy

AbstractIn many regions of the world with complex seismic activity, the availability of seismic stations is limited. This motivated us to develop a probabilistic method for single‐station earthquake location. Single station location methods may provide an efficient and cost‐effective way to monitor small‐magnitude seismic events over large areas of the world. This method relies on the outputs of two neural networks, which predict the initial spatial location of specific areas (convolutional) used in the prior probability and seismic phase recognition (combination of convolutional and Long Short Term Memory) used in the likelihood function. These estimates and a 3D travel time likelihood function are used in a Bayesian framework. In this model, the initial spatial location is expressed as a Multivariate Gaussian Mixture Model, and the likelihood function refines the localization using the arrival time difference between S and P waves in a 3D space. The likelihood function uses a Fast Marching Eikonal method with a 3D velocity structure. We tested this method on the Ridgecrest 2019 seismic sequence (Mw = 7.1) and on West Texas and found promising results for locating with a single station. Our approach demonstrates that using a Bayesian model with strong priors obtained through deep learning algorithms is effective. Our model can predict epicenter and depth with mean errors of 7.55 km in longitude, 13.54 km in latitude and 0.630 km in depth at the two different locations studied. This method demonstrates the effectiveness of combining deep learning for initial estimations with probabilistic 3D location refinement.

Analysis of Vibrational Characteristics of the Bank of China Tower in Hong Kong with ANSYS

Abstract. With the rapid urbanization and technological advancements in construction, modern high-rise buildings have become a symbol of urban development. However, these structures face unprecedented challenges due to their increasing height and the complex dynamic loads they must withstand. This paper presents a comprehensive analysis of the Bank of China Tower in Hong Kong, a prominent architectural landmark, using ANSYS software to perform modal and transient structural analyses. The primary structure of the tower consists of five steel-reinforced concrete columns, designed to resist dynamic responses under extreme conditions such as high wind speeds and seismic activities. The modal analysis identified multiple natural frequencies and vibration modes, providing insights into the dynamic behavior of the structure. The transient structural analysis, conducted under seismic wave loading, demonstrated the tower's admirable performance under specified seismic conditions, highlighting its sufficient safety margins. This study emphasizes the importance of advanced materials and innovative structural designs in ensuring the stability and safety of high-rise buildings. The findings contribute significantly to the field of structural engineering, offering valuable insights for the design and analysis of future high-rise structures.

A Roman bath with broken windows in Asine, Argolis. The result of repeated earthquakes?

A substantial assemblage of Roman window glass—consisting primarily of “cast”, matt/glossy examples, but also including cylinderblown, double-glossy window glass—was discovered during the 1926 excavations of a Late Roman bath in Asine, Argolis, the Peloponnese, Greece. It is clear that this material emanates from damage done to the building, and the question of whether this had human or natural causes is discussed in this paper: was it the “barbarian” invasion of the Visigoth king Alaric in the late 4th century AD that led to the windows being broken? Or, was the damage caused by the earthquakes known to have hit the Eastern Mediterranean area in Late Antiquity? Traces of destruction typical of earthquakes were found in the bath building, and the destruction occurred in a period known for its high seismic activity: the so-called Early Byzantine Tectonic Paroxysm, which led to the conclusion that the bath was hit by at least one, possibly several, earthquakes, causing the windows to shatter and fall out of their frames. Some of the glass sherds were in all probability hidden under dust and debris and were never recovered despite the fact that glass was extensively recycled at the time.