Abstract Between June 2019 and June 2022, 1192 earthquakes of magnitude ML 0.2–3.7 were recorded by the Oklahoma state seismic network near the town of Quinton in eastern Oklahoma. In our analysis of the area, we find that the earthquake sequence was plausibly enhanced due to adjacent hydraulic fracturing (HF) activity that broadened the influence of wastewater injection at a single disposal well. The initial cataloged seismicity indicated earthquake epicenters aligning as a northeast-striking fault south of northeast-striking mapped faults. However, careful relocation suggests the seismicity epicenters overlay a series of east–west-striking faults that are subparallel to the maximum-horizontal stress direction. We reanalyze the seismicity to establish the interaction between HF and wastewater disposal (WD) and their role in inducing seismicity in the area. We relocated earthquakes using a double-difference scheme and a local 1D velocity model to reveal three fault segments oriented in a northwest direction. The spatiotemporal occurrence of seismicity suggests increased pore pressure from the point of injection as the primary driver of seismicity. HF is found to “prime” the faults for the WD to induce and propagate seismicity. Furthermore, the preceding HF may have increased the vertical hydraulic connectivity, allowing a direct vertical propagation of fault permeability. The frequency-index method, which utilizes earthquake waveform frequency content, indicates fluid inclusion into the seismogenic faults during the fault reactivation by wastewater injection.

The results of seismic monitoring of the Magadan region, Chukotka Autonomous Area and the shelf of adjacent seas (the Okhotsk, Chukchi, Bering and East Siberian Seas) in 2021 are considered. There were 14 working seismic stations in the region. The catalog includes information about 256 earthquakes with energy classes KR=6.2–13.3. As usual, most regional earthquakes (84%) are localized in the Kolyma area. The total seismic energy ΣЕ released within the region’s borders was equal to 2.43∙1013 J. The classification of Russian North-East earthquakes was performed using energy classes KR of T. G. Rautian scale. The earthquake energy representativeness map with Kmin =6–10 was built. According the map, the Magadan network records without omissions the earth‑ quakes with Kmin=7 in Magadan oblast, with Kmin=8 in the Okhotsk Sea, Kmin=10 in the Chukchi Sea. The strongest Omolon earthquake with KR=13.3 (MPSP=5.0, MS=4.2) occurred on October 9, 06 h 54m in the east of the Magadan region. In the North-East settlements, three earthquakes with an intensity up to 4 were felt. Epicenters of Magadan region earthquakes were plotted on the tectonic zoning scheme. Most earthquakes are confined to the largest deep faults of northwestern and sublatitudinal directions. All hypocenters are located within the earth’s crust. In 2021, the seismicity level of the North-East of Russia according to the “SOUS’09” scale was assessed as “background increased”. Spatially, all Russian North-East earthquakes are traditionally concentrated in large seismogenic belts: Chersky, North-Okhotsk and Trans-Beringian.

The results of the analysis of seismic data from monitoring in Yakutia in 2021, conducted by a network of 20 stations, belonging to the Yakutia branch of the Geophysical Survey RAS, are presented. During this year, 394 earthquakes were recorded throughout the territory of the Sakha Republic (Yakutia). Data on the parameters of seismic activity for each of the 12 districts that make up the analyzed area are provided. A map of earthquake epicenters in Yakutia, including boundaries and major tectonic faults, has been created for spatial analysis. As a result of the study, an increase in seismic energy compared to 2020 was observed. The most seismic active areas in 2021 were the Aldan highlands, the Verkhoyansk and Chersky ranges, and also the Laptev district. The strongest earthquake of the region for this period was recorded within the latter on June 13, 04 h 28m with KR=11.7. In the same region for the event to the south-west from Novosibirsk islands the focal mechanism is given

Abstract Western Kanagawa, located in eastern Japan, has repeatedly experienced M-8 class megathrust earthquakes, referred to as Kanto earthquakes, within a tectonic setting characterized by the NNW-ward subduction of Philippine Sea plate. On 9 August 2024, an Mw 5.0 reverse-fault earthquake occurred near the epicenter of the latest Kanto earthquake in 1923. For future evaluation of destructive earthquake generation in this area, it is imperative to determine the precise location and geometry of this Mw 5.0 earthquake sequence: whether it occurred on the plate interface, in the continental arc, or within the subducting plate. To tackle this question, we conducted following analyses: (1) machine-learning-based event detection using PhaseNet and GaMMA, (2) hypocenter relocation with phase picks and waveform cross-correlations, and (3) hypocenter clustering for identifying rectangular fault planes with hypocenter locations and their point-cloud normal vectors. Our analysis revealed the presence of three fault planes, including two parallel steep SSE-dipping planes consistent with the steeper nodal plane of the focal mechanisms for the mainshock and its largest aftershock. These planes lie approximately 1.5 km beneath the plate interface model, indicating intraplate reverse faulting within the subducting Izu–Bonin arc as this earthquake sequence, as opposed to interplate thrusting or reverse faulting in the continental arc. The extracted fault planes with varying strikes and high non-double-couple components, both indicative of complex faulting, suggest that loading stress is more complex than that of simple subduction. The focal area is in the collision–subduction transition: collisional compression and weakly coupled plate interface. Complex stress conditions, including both plate coupling and arc–arc collision, may have contributed to the earthquake generation. These findings would help assess the future occurrence of Kanto earthquakes and comprehend seismotectonics in the Izu collision zone. Graphical Abstract

We propose a deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequence-to-sequence forecasting framework, enabling it to model long-term dependencies and multiscale patterns in both space and time. By sharing weights across spatial and temporal dimensions, WaveCastNet requires significantly fewer parameters than more resource-intensive models such as transformers, resulting in faster inference times. Crucially, WaveCastNet also generalizes better than transformers to rare and critical seismic scenarios, such as high-magnitude earthquakes. Here, we show the ability of the model to predict the intensity and timing of destructive ground motions in real time, using simulated data from the San Francisco Bay Area. Furthermore, we demonstrate its zero-shot capabilities by evaluating WaveCastNet on real earthquake data. Our approach does not require estimating earthquake magnitudes and epicenters, steps that are prone to error in conventional methods, nor does it rely on empirical ground-motion models, which often fail to capture strongly heterogeneous wave propagation effects.

To evaluate the medium- and long-term effects of a major earthquake epicenter in Kahramanmaras, Turkey, on Axial Spondyloarthritis (axSpA) patients receiving biological therapy in the affected region. A single-center retrospective observational study included 159 patients with axSpA receiving biologic therapy who applied to our outpatient rheumatology department between February 2023 and October 2024. Clinical, laboratory and disease-related parameters pre- and post-earthquake were retrieved from the hospital database. The median age of patients with axSpA was 41 (IQR:14) (female 43, male 40) years, 73.6% were male, and the disease duration was 8 (IQR:7) years. The median follow-up duration for patients in the post-earthquake period was 16 months. Fifty-five patients (34.6%) had discontinued their treatment due to the earthquake and remained without treatment for a median of 75 (IQR:58) days. When patients who discontinued treatment were compared with those who continued, the mean Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) scores after the earthquake were 5.6 and 3.3, respectively, and the difference was statistically significant (p = 0.01). The duration of treatment discontinuation showed a significant positive correlation with post-earthquake BASDAI scores (r = 0.603, p = 0.01). When evaluating the effect of treatment discontinuation duration on clinical outcomes, a longer duration of treatment discontinuation was significantly associated with increased disease activation (p = 0.001) and treatment non-response (p = 0.013). This study, highlights the significant impact of treatment discontinuation on disease activity and response in patients with axSpA receiving biologic therapy. AxSpA inherently presents complex challenges in monitoring and treatment, which are exacerbated in disaster settings. Therefore, there is a need for disaster action plans that incorporate a multidisciplinary approach adapted to the management of chronic rheumatic diseases such as axSpA. Key points • Disease control in axSpA patients is critical to prevent complications. •Disasters like earthquakes are difficult to manage chronic diseases such as axSpA. •Emergency action plans need to be established to ensure the effective management of axSpA patients during disaster situations.

The growing complexity of geophysical systems, like earthquakes and volcanic eruptions, requires computational models that can manage enormous, nonlinear and multidimensional datasets in real time. Classical computing methods still yield results but are often not designed to cope with the scales and stochasticity of seismic and volcanic observations, so quantum computing provides a disruptive technology to tackle this issue, enabling geophysical modeling to entirely transform into a capacity to process and analyze complex patterns at massive scales. This study provides an overview of the potentials of various quantum algorithms such as the Variational Quantum Eigensolver (VQE), the Quantum Approximate Optimization Algorithms (QAOA) and quantum-enhanced Monte Carlo simulations to simulate geophysical processes. The results of these models will be of particular relevance to modeling partial differential equations, inverse problems and tasks of uncertainty quantification that describe seismic wave propagation, magma chamber flow and tectonic stress diffusion. We will also discuss how quantum machine learning (QML) models can improve the forecasts of earthquake epicenters, fault detections and eruption forecasts utilizing quantum feature spaces. Further, we will include a discussion of both quantum sensors and edge quantum processors, with attempts for in situ real-time data collection and data processing in hazardous areas.

Abstract The 1949 M w 7.6 Khait earthquake, Tajikistan, was one of the most destructive of the Pamir—Tien Shan region, killing over 7,000 people by building collapse and landsliding. It occurred at the transition between the Pamir and Tien Shan mountain ranges, which converge along the narrow Vashkh river valley. Therefore, it is important in understanding the tectonic and structural development at the junction of these two major mountain ranges, as well as yielding important information regarding hazard and risk to local populations and infrastructure. Although widespread landsliding and intensities of shaking were recorded, no surface ruptures were mapped, and the earthquake epicenter and source are poorly known. To determine the location, magnitude, and focal mechanism of the 1949 earthquake we combine calibrated earthquake relocations with body‐wave amplitude ratios from digitized paper seismograms. Our analysis shows that the earthquake nucleated within the Tien Shan basement with strike slip kinematics. Using high‐resolution digital elevation models and orthophotos derived from high‐resolution satellite imagery, we identify earthquake ruptures within the epicentral zone consistent with NW‐SE right‐lateral faulting. Relocated aftershocks and later seismicity also follow a NW‐SE trend through the Tien Shan north of the Vakhsh river. Mapping tectonically offset moraines dated in previous literature, we find a Vakhsh Fault slip rate of only half the present day strain accumulation rate derived from GNSS. Our results suggest a significant proportion of the regional deformation may occur away from the geomorphologically well expressed Vakhsh and Darvaz faults, and on faults with little prior expression of activity.

On 06 February 2023, two strong earthquakes occurred in Kahramanmaraş, Türkiye. The first earthquake with a moment magnitude (Mw) of 7.7 and an epicenter in Pazarcık, was followed by a second earthquake with a moment magnitude of (Mw) 7.6 and an epicenter in Elbistan. These earthquakes have caused catastrophic life and structural loss in ten cities where more than 15 million people permanently live. Concentrating on the epicenter of the second earthquake, Elbistan, this study investigates the main characteristics of the earthquakes and the failure of the reinforced concrete buildings. Acceleration records from both earthquakes were analyzed and site surveys were conducted to evaluate the caused damages. The high number of completely collapsed buildings highlighted the vulnerability of the general old building stock constructed before 2000. Notably, unlike previous earthquakes in Türkiye, these earthquakes also affected new buildings constructed according to the recent Turkish Earthquake Code. In that respect, seismic damages seen in these buildings are presented and interpreted in this paper. To validate the observed deficiencies, a damaged building whose construction had just been completed but not yet occupied was both surveyed on site and subjected to non-linear performance analyses. The analysis results demonstrated that the damage distribution and locations in the models closely matched the observed damage after the earthquakes. In addition, the analyses also revealed the effect of brick infill walls on the building’s seismic behavior.

An earthquake with a magnitude of 7.5 occurred on September 28th, 2018, at 18:02 in Palu, Central Sulawesi. The Palu-Koro Fault experienced a strike-slip mechanism. The earthquake epicentre is located 72 km north of Palu, with an earthquake depth of 10 km. This earthquake triggered extensive and massive landslides in the Balaroa Village. The goal achieved through this research is to determine the potential for liquefaction in the area affected by the liquefaction of Balaroa using the geoelectric method. This type of research uses a survey method. The contour map shows that the initial slope between the crown and toe is estimated to be about 3°. Based on the grain distribution graph plotted into the Tsuchida curve (1970), it is obtained that the soil in the sample contains predominantly sand and has a grain size that is within the boundaries of the potential liquefaction zone. Analysis of the Factor of Safety shows that the average liquefaction potential occurs at a depth of 6.5 meters. Liquefaction potential is strongly influenced by earthquake acceleration and ground stress. Resistivity data retrieval is carried out using the Wenner configuration method. The results showed that 1) the elevation of the measurement location was in the range of 18 meters to 59 meters, 2) The range of resistivity values at the liquefaction area in Balaroa village was 2 Ωm to 10000 Ωm, and 3) 4 types of soils were obtained at the research site consisting of water-saturated Alluvium layer, Conglomerate rock destruction, Weathered Granite, and Granite chunks, All measurement lines are shatter zones seen in resistivity cross-sectional patterns that do not show stratigraphic laminated sedimentary patterns good, At line-1 of Balaroa, traces of fault depression were found, which was interpreted as still part of the western Palu-Koro fault zone, and in line-3 of Balaroa, an ancient river channel was found that was trending almost West-East.

Research subject. The seismic process in the area of the East Anatolian Fault (EAF), where two catastrophic earthquakes (doublet) occurred in February 2023.Aim. Identification of specific features of the seismic process, which might indicate a connection (or a lack thereof) between the two earthquakes from the doublet, as well as the presence of any predictive signs before seismic events (medium- or short-term).Materials and methods. An analysis of seismic activity graphs constructed by summing periods between earthquakes based on a local seismic catalog.Results. Activity graphs were plotted for six regions at the epicenter of the first earthquake. Characteristic periods with a constant velocity of the seismic process and transition points were established. Local activations were detected in 2012 and 2022, which are confined to the epicenters of the doublet and are dominant for the EAF as a whole (for at least 300 km). All activations were timed to the same area slightly east of the epicenter of the February earthquake. It is shown that these activations do not correspond to the standard scheme of operation of a seismic center, i.e., they do not have an unambiguously determined main event and a characteristic aftershock process.Conclusions. According to the distribution of earthquake epicenters, the local activation of 2012 was probably the initial stage in the development of the process, which was continued in 2022. The abrupt process of activity in 2022 ended 40 days prior to the earthquake doublet, and can be considered as a short-term predictive sign. The main characteristics of such a short-term anomaly in seismic activity are a reduction in the duration of each subsequent activation and a decrease in the time between them.

In recent years, the exploration and utilization of geothermal energy have received growing attention as a sustainable alternative to conventional energy sources. Reliable, data-driven identification of geothermal reservoirs, particularly in crystalline basement terrains, is crucial for reducing exploration uncertainties and costs. In such geological settings, magnetic susceptibility, radioactive heat production, and seismic wave characteristics play a vital role in evaluating geothermal energy potential. Building on this foundation, our study integrates in situ and laboratory measurements, collected using advanced sensors from spatially diverse locations, with statistical and unsupervised artificial intelligence (AI) clustering models. This integrated framework improves the effectiveness and reliability of identifying clusters of potential geothermal sites. We applied this methodology to the migmatitic gneisses within the Simav Basin in western Türkiye. Among the statistical and AI-based models evaluated, Density-Based Spatial Clustering of Applications with Noise and Autoencoder-Based Deep Clustering identified the most promising and spatially confined subregions with high geothermal production potential. The potential geothermal sites identified by the AI models align closely with those identified by statistical models and show strong agreement with independent datasets, including existing drilling locations, thermal springs, and the distribution of major earthquake epicenters in the region.

In January 2025, a magnitude 6.8 earthquake struck Dingri County, Shigatse, Tibet, causing severe damage. Rapid and precise extraction of damaged buildings is essential for emergency relief and rebuilding efforts. This study proposes an approach integrating YOLO-E (Real-Time Seeing Anything) and the Segment Anything Model 2 (SAM2) to extract damaged buildings with multi-source remote sensing images, including post-earthquake Gaofen-7 imagery (0.80 m), Beijing-3 imagery (0.30 m), and pre-earthquake Google satellite imagery (0.15 m), over the affected region. In this hybrid approach, YOLO-E functions as the preliminary segmentation module for initial segmentation. It leverages its real-time detection and segmentation capability to locate potential damaged building regions and generate coarse segmentation masks rapidly. Subsequently, SAM2 follows as a refinement step, incorporating shapefile information from pre-disaster sources to apply precise, pixel-level segmentation. The dataset used for training contained labeled examples of damaged buildings, and the model optimization was carried out using stochastic gradient descent (SGD), with cross-entropy and mean squared error as the selected loss functions. Upon evaluation, the model reached a precision of 0.840, a recall of 0.855, an F1-score of 0.847, and an IoU of 0.735. It successfully extracted 492 suspected damaged building patches within a radius of 20 km from the earthquake epicenter, clearly showing the distribution characteristics of damaged buildings concentrated in the earthquake fault zone. In summary, this hybrid YOLO-E and SAM2 approach, leveraging multi-source remote sensing imagery, delivers precise and rapid extraction of damaged buildings with a precision of 0.840, recall of 0.855, and IoU of 0.735, effectively supporting targeted earthquake rescue and post-disaster reconstruction efforts in the Dingri County fault zone.

Abstract. The 7.8 Mw Turkey-Syria earthquake of 2023 caused massive destruction in several cities near the earthquake epicenter. However, there is a potential for significant land subsidence to occur across a broader region. Land subsidence, which can lead to significant infrastructure damage and ground deformation, necessitates detailed investigation. This research uses an advanced machine-learning technique to analyze the spatial distribution of earthquake-induced land subsidence and the extent of surface deformation. Sentinel-1 Synthetic Aperture Radar (SAR) data were processed to detect surface deformation near the epicenter and quantify the affected region's vertical displacement. An extreme learning machine model was developed using nine parameters, including slope, curvature, sediment thickness, soil thickness on slopes, peak ground acceleration, hydrologic soil, Vs30, land cover, and landslide probability. The model accurately predicted land subsidence susceptibility (accuracy of 85%) established correlations with ground deformation and observed vertical displacement. The results demonstrate that the deformation phase value ranges between 2.66 to −2.63, and the vertical displacement analysis suggests that a large portion of areas subsided downward up to 75 cm. Effectiveness of extreme learning machine in rapid land subsidence assessment, providing critical insights for disaster response and urban planning in seismically active areas. This study offers a useful solution for post-earthquake land subsidence analysis and lays the groundwork for integrating artificial intelligence with land subsidence research.

Objective: Postpartum depression can negatively impact both the mother's health and the child's development if treatment is not received. This study's goals are to discover different prenatal stressors and ascertain if the mother's stress during pregnancy is linked to postpartum depression. Materials & Methods: Using a consecutive sampling technique, an analytical cross-sectional study was conducted at postnatal wards, EPI centers and Gynae OPD of women aged 20 to 45 during the postpartum period (one week to six months). The study excluded female participants having a history of chronic illness, psychiatric medication, or a diagnosis of psychiatric disorder. The Edinburgh Post-Depression Scale and self-structured pre-partum stressors were included in an interview-based questionnaire.Results: Of the 313 mothers, 63.3% experienced mild stress, 32.6% experienced moderate anxiety, and 3.8% experienced severe stress. Inflation (82%), husband issues (64%), abuse at the hands of the husband (55%), and housekeeping during pregnancy (52%), were the most frequent sources of stress. Postpartum depression was seven times more likely to occur in those with high stress levels.Conclusion: Prepartum stress was modest for the majority of participants. The main causes of stress were things like inflation, followed by problems with the spouse, abuse at the hands of the husband, concerns about the children's schooling, and the amount of chores that came with being pregnant. Postpartum depression was more common in those with high stress levels.

On the 13th of June 2023, at 13:33:42 IST, an earthquake of magnitude Ml5.1 occurred in the Doda region of Jammu & Kashmir (J&K). The earthquake's epicenter was at coordinates 33.15 N and 75.89 E, with a shallow depth of 10 km. This seismic event unfolded within the geological context of the Himalayan orogeny, which has taken shape as a consequence of the collision between the Indian plate and the Eurasian plate. The Himalayan region, characterized by the intricate dynamics of these tectonic collisions, harbours numerous significant regional and local fault systems. A substantial portion of these fault lines remains active, continually generating seismic activity throughout the Himalayan region and its adjacent areas. Despite causing minor structural damage to a few buildings in the source region, this particular earthquake was well-documented and was accurately located by the Indian National Seismic Network, which is operated by the National Centre for Seismology (NCS), Ministry of Earth Sciences (MoES). Notably, the NCS-MoES maintains three Seismological observatories in Jammu & Kashmir (specifically in Jammu, Srinagar, and Udhampur), along with two additional Seismological observatories in Ladakh (in Hanley and Alchi). In the present study, we have harnessed waveform data collected from a network of 15 Seismological observatories managed by NCS-MoES, strategically positioned across J&K, Ladakh, and Himachal Pradesh. Our research endeavours to estimate the source characteristics including the precise determination of the earthquake's geographical location. Our analysis employed specialized software tools, SEISAN for epicenter localization and ISOLA for fault plane solution. Moreover, these derived seismic parameters served as foundational data for further analyses. Specifically, we leveraged these parameters to quantify the energy released and determine the source radius. These additional insights provided a comprehensive characterization of this earthquake and its implications.

Instrumental and macroseismic data on the Tajikabad earthquake of April 13, 2025 with MS=Mw=5.9, which occurred in the northeast of the Tajik depression, are analyzed. In a number of settlements in the Surkhob River valley, at distances of 7–25 km from the epicenter, the earthquake caused destructive consequences corresponding to 7-point intensity by the SHSI-17 scale (Russian seismic intensity scale, State Standard 57546-2017). The intensity at the epicenter, calculated using the macroseismic field equation with coefficients for the Tajik depression, was I0=8. The earthquake was recorded by nine stationary seismic stations of the Geophysical Service of the National Academy of Sciences of Tajikistan and hundreds stations of various international seismological centers around the world. The earthquake epicenter was found to be confined to the northeastern-trending Yafuch fault, with the orientation and dip of which one of the nodal planes of the focal mechanism coincides. Records of strong ground movements at 44 seismic stations in Central Asia were analyzed. The instrumental intensity at 44 strong motion recording points in Tajikistan and neighboring countries was estimated based on acceleration amplitudes, and in most cases it turned out to be lower than the observed intensity. This may be due to the installation of sensors at bedrock outcrops, which corresponds to category I soils, as opposed to category II and III soils in populated areas. The earthquake was accompanied by an intensive aftershock process: within the week since the main shock, 92 aftershocks with M=2.6–4.7 and many weaker ones were registered and processed. The largest aftershocks were noticeable.

An express analysis of two perceptible earthquakes that occurred in April 2025 in the Khabarovsk region on April 2, 2025 at 05:48 with ML=3.9 and on April 14, 2025 at 11:32 with ML=3.3. The earthquake parameters were determined in real time by the duty shift of the Yuzhno-Sakhalinsk Regional Seismic Center based on the data from the regional network of the Sakhalin Branch GS RAS and were refined in the Department of consolidated seismic data processing of the Sakhalin Branch GS RAS using the data from the stations of both the regional network and the local network of the south of Sakhalin Island, as well as the stations of the network of the Yakut Branch GS RAS. The epicenters of both earthquakes are confined to the Middle Amur and Coastal Primorsko-Priamurskaya zones of the Sikhote-Alin region of Primorye and Priamurye, the seismicity of which is extremely low, the last perceptible earthquakes in these zones occurred 100 and 50 years ago. Focal mechanisms of both earthquakes have been constructed. Macroseismic data have been collected and a map of the manifestations of the earthquake on April 14 in different parts of Komsomolsk-on-Amur with an intensity of 2 to 4 points has been compiled, which clarifies the clearly underestimated assessment of emergency services (1–2 points).

A systematic earthquake prediction is performed regularly at fixed intervals within a preselected seismically homogeneous zone. The result of each prediction iteration is a map highlighting the alarm zones, where the epicenters of target earthquakes are expected. The proposed methodology introduces the following innovations: 1 – A prediction is considered successful if all epicenters of the target earthquakes during the forecast interval fall within the alarm zone. 2 – The methodology optimizes both the probability of successfully detecting earthquake epicenters across a series of forecasts and the success rate of predictions in each individual iteration. 3 – The methodology enables the estimation of the probability of success for the next forecast interval. Examples of the method's application are demonstrated for predicting earthquakes in Kamchatka, California, and the island region of Japan.

ABSTRACT The main aim of the study is to assess the resiliency of water distribution systems (WDSs) to natural hazards like earthquakes for Indian networks using the water network tool for resiliency (WNTR). The methodology used in this study in this study consisted of simulation of earthquake scenarios by assigning hypothetical earthquake parameters and determining the major and minor damages of WDN components and the resiliency. No study has been performed in Indian Scenarios to determine the resiliency of WDSs to natural hazards such as earthquakes using WNTR. Two practical case studies are considered to evaluate the resiliency of WDSs of the National Institute of Technology (NIT) Kurukshetra and Bhuj, India, considering the vulnerability of WDSs to earthquake scenarios. It is observed that the distance to the epicenter and earthquake parameters, magnitude, depth, and epicenter, and network layout play an important role in estimating the severity of damage in both networks. For NIT Network, it is observed that the closer the location is to the earthquake's epicenter, the more the NIT water distribution networks (WDNs) resilience is reduced, to 0.00507 from 0.022648. The Todini resilience index (RI) of Bhuj's WDN was 0.018541 and the post-earthquake RI dropped to 0.011151. This indicates that the network's resilience significantly decreased due to the earthquake's impact. It indicates significant damage and reduces the ability of the network to maintain water pressure and supply, emphasizing the need for a strong infrastructure in earthquake-prone areas.