Search Results (26)

Recent destructive earthquakes have clearly demonstrated that damage distribution in many cities developed on thick alluvial deposits is strongly controlled by local soil amplification and site response effects. Soil conditions therefore play a critical role in determining the characteristics of ground motion and the seismic performance of structures during earthquakes. This study presents the first microzonation-oriented site response assessment for the rapidly urbanizing city of Afyonkarahisar, which is characterized by thick alluvial deposits and a shallow groundwater table. A database consisting of 124 boreholes was compiled to characterize the subsurface stratigraphy of the study area. Shear-wave velocity profiles were verified using both SPT-based correlations and MASW measurements to ensure reliable input parameters for dynamic analyses. One-dimensional equivalent linear and nonlinear site response analyses were performed using the DeepSoil program, employing eleven earthquake ground motion records scaled according to the Turkish Building Earthquake Code. The results indicate that for Earthquake Level-1 (EL-1; 2% probability of exceedance in 50 years) ground motions, nonlinear analyses produce lower amplification factors (1.00–1.62), whereas equivalent linear analyses tend to predict higher amplification values, reaching up to 4.52, owing to their simplified treatment of soil nonlinearity. Under Earthquake Level-2 (EL-2; 10% probability of exceedance in 50 years) motions, both methods yield comparable amplification values ranging from 1.18 to 1.72. GIS-based amplification maps reveal significant spatial variability within the study area and identify zones where local soil conditions may substantially increase seismic demand. The findings suggest that nonlinear site response analysis is more appropriate for representing soil behavior under strong ground motions (EL-1), while both approaches provide comparable results for moderate ground motions (EL-2). Comparisons with Eurocode 8 and NEHRP site classifications further confirm the broader applicability of the results. Overall, this study provides a practical framework for reliable site response assessment that supports earthquake-resistant design and microzonation studies in seismically active regions characterized by complex alluvial environments. Full article

This study offers a new approach to probabilistic earthquake hazard assessment (PEHA) in the densely populated regions of Southern Sumatra and West Java, Indonesia. While much attention is given to powerful, offshore megathrust earthquakes, this research focuses on a different yet equally dangerous threat: shallow, moderate-magnitude earthquakes (4.5 ≤ M_w ≤ 6.5) that occur on land. These events, often caused by unmapped faults, pose a significant risk due to their proximity to major cities and infrastructure. To develop a more reliable model, a best-fit earthquake rate model was estimated using declustered shallow earthquake events as a reference. This model enhances existing methods by offering a more precise depiction of where these shallow, damaging earthquakes are likely to occur. We accomplished this by analyzing a comprehensive probability of exceedance (PoE) of earthquakes with magnitudes up to 6.5 and depths up to 50 km that occurred between 1963 and 2022, mapping and modeling both the known active faults and the historical seismic activity in the region, and using advanced statistical methods to create a highly reliable, integrated seismicity rate model. The final product, the Integrated Most Reliable Spatial Seismicity Rate Model (Model_IMRSSR), is proposed as a useful tool for government authorities and urban planners. It can be used to create detailed seismic hazard maps that highlight areas of highest risk, especially those with unmapped faults. By guiding development away from these high-risk zones and identifying specific locations for physical reinforcement, this research provides a framework for sustainable investment. The proactive use of these findings can lead to more resilient communities and a significant reduction in potential damage and loss of life from future earthquakes. Full article

Post-disaster recovery is a multi-year, multi-dimensional process, yet most remote-sensing assessments rely on single indicators and are hard to apply in data-sparse regions—limiting their value for sustainable, evidence-based reconstruction. We develop a Google Earth Engine (GEE)-based multi-sensor Recovery-Activity Index (RAI), built entirely from free satellite data, and apply it to the 2023 Al Haouz earthquake (Mw 6.8) in the High Atlas, Morocco. The index is framed explicitly as an observed recovery-activity monitoring proxy, not a direct measure of welfare or resilience capacity. Monthly VIIRS nighttime-light (NTL) anomalies, Dynamic World built-up probability, and precipitation-corrected Sentinel-2 NDVI were extracted for a 30 km rural core zone (January 2022–May 2026), deseasonalized, standardized, and integrated. NTL anomalies rose after the earthquake (post-event mean +18%) and appeared to precede built-up anomalies by about two months; because monthly series are short and autocorrelated, we tested this lead with block-bootstrap and block-permutation methods and report it as a reproducible but modest early-activity lead (r = 0.65, p = 0.02; p = 0.14 after correction) that is not an artefact of optical data gaps. NDVI was governed mainly by precipitation (R² = 0.61) with negligible earthquake-attributable change, so vegetation signals do not confound the index. The integrated RAI peaked in December 2024 and proved robust to indicator weighting (pairwise r ≥ 0.97), baseline choice (r = 0.88), and spatial domain (<9% variation), with a genuinely multi-sensor peak (NTL 62%, built-up 43%). Province-level analysis revealed an uneven recovery hierarchy (Chichaoua > Al Haouz > Taroudannt) driven by differences in physical-rebuilding signal rather than baseline luminosity. Running in minutes server-side at no cost, the RAI offers data- and resource-limited administrations a scalable, reproducible tool to flag where reconstruction activity lags and to prioritize targeted ground verification—supporting more equitable, sustainability-oriented recovery governance—rather than serving as a stand-alone, validated recovery measure. Full article

An earthquake lethality model was employed to assess the casualty distribution in Yunnan, Guizhou, and Sichuan provinces, taking into account the ground motion acceleration with different 50-year exceedance probabilities. When the probability is 63%, fatalities are predominantly concentrated in central and south-western Yunnan, as well as central, southern, and western Sichuan. At a 10% probability, the peaks of the casualties are observed in southern, eastern, and central Sichuan. In Yunnan (excluding the northwest and southeast regions), the casualty density exhibits unevenness, whereas Guizhou experiences relatively low casualties (except in the eastern and western mountainous areas). Xichang incurs the most substantial losses, followed by Lancang. Xundian, Songming, and Dongchuan demonstrate a high propensity for fatalities, and the risk is relatively high in the vicinity of the Longjiang and Nujiang faults. If a destructive earthquake occurs near these areas within the next 50 years, the probability of a Level-I emergency response exceeds 10%. When the ground motion acceleration doubles (especially when the exceedance probability drops to 2% in 50 year and 0.1% in a year), the predicted number of casualties remains relatively stable. However, the grid of the casualty population exhibits a higher degree of spatial concentration of casualties, and the disaster-affected area expands. There exists no linear correlation between earthquake-induced fatalities and the ground motion level. When the 50-year exceedance probability decreases from 63% to 10%, the casualty rate may increase by several dozen times. Full article

Natural hazards and their negative impacts on assets are increasing because of a variety of causes, including climate change, population expansion, and urbanization. Moreover, several areas are susceptible to multiple hazards that interact spatially and/or temporally, necessitating a multi-hazard assessment to adequately mitigate their effects. The goal of this study is to investigate the direct monetary losses produced by the simultaneous interaction of two independent hazards in Lisbon’s city centre, i.e., earthquake and pluvial flood. Seismic hazard has been assessed in terms of macro-seismic intensity, while flood scenario allows for the prediction of water depth for different return periods through a hydrologic-hydraulic model in HEC-RAS software. The seismic and flood vulnerability of the urban investigated compound was evaluated through MCDM methodology—specifically, AHP and TOPSIS methods. A framework for multi-hazard analysis was subsequently developed, explicitly accounting for the interaction between the two hazards and their joint occurrence probabilities based on historical data from the case study area. The results demonstrate that multi-hazard losses are 108 M€ for a 2-year return period and 232 M€ for a 475/500-year scenario, emphasizing that floods contribute more across all return periods in the research area; however, for longer return periods, the earthquake contribution increases significantly. Full article

Reinforced concrete (RC) buildings in coastal seismic regions are exposed to coupled deterioration processes driven by chloride-induced corrosion and earthquake loading. This interaction is particularly critical along the Mexican Pacific coast, where persistent marine exposure coincides with high seismic hazard. Nevertheless, current models lack a consistent multi-physics framework that integrates chloride transport, electrochemical heterogeneity (including galvanic interactions), and seismic structural response. This study quantifies the influence of corrosion on seismic collapse probability by explicitly modeling the coupled mechanisms of moisture transport, chloride ingress, and electrochemical potential distribution in RC members. A three-dimensional mechanistic framework is adopted to capture the spatial variability in corrosion, including galvanic interactions between passive and active reinforcement regions. A representative scenario is examined in which a corner column remains in continuous contact with seawater, promoting localized chloride accumulation and sustained corrosion activity. The resulting nonuniform section loss is incorporated into nonlinear structural models subjected to mainshock–aftershock sequences. The results show that corrosion-induced heterogeneity, amplified by galvanic coupling between passive and active zones, accelerates strength and stiffness degradation. Compared to conventional uniform corrosion assumptions, this effect leads to a significant increase in early collapse probability, with values increasing from near-zero levels to approximately $0.6$–$0.9$ at moderate seismic intensity levels. These findings emphasize the need to account for coupled transport and electrochemical processes, as well as localized exposure conditions, in the seismic assessment of RC structures in aggressive coastal environments. Full article

Earthquake-induced landslides in active orogens such as the Nepal Himalaya pose severe threats to lives, infrastructure, and post-disaster recovery. While machine learning (ML) and deep learning (DL) approaches to coseismic landslide susceptibility mapping have advanced considerably, spaceborne interferometric synthetic aperture radar (InSAR) products, particularly line-of-sight (LOS) displacement and coherence-based damage proxy maps (DPMs), remain underutilized in event-based frameworks. This study develops and evaluates a multi-factor coseismic landslide probability model that integrates InSAR-derived deformation metrics with geomorphic and hydrologic predictors to support rapid post-earthquake hazard assessment. Using the 25 April 2015 Mw 7.8 Gorkha earthquake as a case study, LOS displacement was derived from ALOS-2 PALSAR-2 ScanSAR interferometry, and the normalized channel steepness index (K_sn) was computed from a digital elevation model. Fourteen conditioning factors were used to train five architectures: Random Forest (RF), XGBoost, CNN, U-Net, and DeepLabV3. Spatial autocorrelation was mitigated using a leave-one-basin-out three-fold spatial cross-validation strategy, with models evaluated on a patch-based domain comprising 655,360 pixels at a positive-class prevalence of 6.35%, establishing a no-skill AUC-PR baseline of 0.0635. InSAR integration consistently improved model performance under high class imbalance, increasing AUC-PR across all models by 7.8% to 17.3%. Random Forest achieved the highest AUC-PR (0.7940, nearly 12.5 times the baseline) and CSI (0.3027), providing the best balance between landslide recall (88.09%) and non-landslide specificity (88.68%) with the lowest false alarm rate (11.32%). XGBoost attained the highest AUC-ROC (0.9501) but exhibited lower recall (83.73%) and poorer calibration (Brier = 0.1397). Among DL models, DeepLabV3 produced the best-calibrated probabilities (Brier = 0.0693) and the highest CSI (0.2307), while U-Net offered the most balanced DL performance and CNN achieved the highest recall (92.40%) at the expense of elevated false alarms. Permutation feature importance identified K_sn as the dominant predictor, highlighting the strong tectono-geomorphic control on coseismic landslide occurrence. These results demonstrate that integrating InSAR-derived products substantially enhances landslide hazard assessment and supports more reliable rapid response in the Nepal Himalaya. Full article

Liquefaction is a critical mechanism amplifying earthquake-induced damage, necessitating systematic hazard assessment through spatially distributed mapping. This study presents a nationwide liquefaction hazard assessment framework for South Korea, integrating site classification, liquefaction potential index (LPI) computation, and probabilistic damage evaluation. Sites across the Korean Peninsula were stratified into five geotechnical categories (S1–S5) based on soil characteristics. LPI values were computed incorporating site-specific amplification coefficients for nine bedrock acceleration levels corresponding to seismic recurrence intervals of 500, 1000, 2400, and 4800 years per Korean seismic design specifications. Subsurface characterization utilized standard penetration test (SPT) data from 121,821 boreholes, with an R-based analytical program enabling statistical processing and spatial visualization. Damage probability assessment employed Iwasaki’s LPI severity classification across site categories. Results indicate that at 0.10 g peak ground acceleration (500-year event), four regions exhibit severe liquefaction susceptibility. This geographic footprint expands to seven regions at 0.14 g (1000-year event) and eight regions at 0.18 g. For the 2400-year design basis earthquake (0.22 g), all eight identified high-risk zones reach critical thresholds simultaneously. Site-specific analysis reveals stark contrasts in vulnerability: S2 sites demonstrate 99% very low to low damage probability, whereas S3, S4, and S5 sites face 33%, 51%, and 99% severe damage risk, respectively. This study establishes a scalable, evidence-based framework enabling efficient large-scale liquefaction hazard assessment for governmental risk management applications. Full article

The coefficient of variation α is a critical parameter in the Brownian Passage Time (BPT) model, used to quantify the variability of large earthquake recurrence intervals. In this paper, a new estimation method is proposed for α based on paleoseismic sequences across multiple faults within a given tectonic region. By integrating Monte Carlo simulations with a Bayesian framework, the method assesses the probability distribution of α without assuming that the sample average recurrence interval equals the true mean μ, thereby avoiding epistemic bias. To validate the method, 1,000,000 simulations were conducted in two study areas of differing spatial scales. In the Western Qilian Mountains-Hexi Corridor, the posterior mean of α is 0.36 (without dating uncertainty) and 0.34 (with uncertainty). Expanding the analysis to 29 faults across western China, the estimated α increases to 0.39 (without dating uncertainty) and 0.36 (with uncertainty), with substantially reduced uncertainty bounds. The results reveal that increasing the number of paleoseismic sequences significantly reduces the uncertainty in estimating α, while considering dating uncertainty has only a minor impact. The methodology provides a robust framework for deriving region-specific recurrence variability parameters and proves particularly valuable for tectonically active regions where individual fault records are sparse but collectively form comprehensive datasets across multiple fault systems. Full article

Earthquake hazards, such as strong ground motion, liquefaction, and landslides, pose significant threats to structures built on seismically vulnerable, loose, and saturated sandy soils. Therefore, a structural failure evaluation method that accounts for site-specific seismic responses is essential for developing effective and appropriate earthquake hazard mitigation strategies. In this study, a real-time assessment framework for structural seismic susceptibility is developed. To evaluate structural susceptibility to earthquakes, seismic fragility functions are employed as thresholds for structural failure and are linked to a geotechnical spatial grid that incorporates correlation equations for seismic load determination. The real-time assessment consists of the following procedures. First, the geotechnical spatial grid is constructed based on the geostatistical method to estimate the site-specific site response to be correlated with the earthquake hazard potential. Second, the peak ground accelerations are determined from seismic load correlation and assigned to the geotechnical spatial grid. Third, the damage grade of structure is determined by calculating the failure probabilities of defined damage levels and integrating the geotechnical spatial grids for the target structure in real time. The proposed assessment was simulated at Incheon Port, South Korea, using both an actual earthquake event (the 2017 Pohang Earthquake) and a hypothetical earthquake scenario. Full article

Characterized by frequent earthquakes and a dense population, Yunnan Province, China, faces significant seismic hazards and is a hot place for earthquake forecasting research. In a previous study, we evaluated the performance of the b value for 5-year seismic forecasting during 2000–2019 and made a forward prediction of M ≥ 5.0 earthquakes in 2020–2024. In this study, with the forecast period having passed, we first revisit the results and assess the forward prediction performance. Then, the background seismicity rate, which may also offer valuable long-term forecasting information, is incorporated into earthquake prediction for Yunnan Province. To assess the effectiveness of the prediction, the Molchan Error Diagram (MED), Probability Gain (PG), and Probability Difference (PD) are employed. Using a 25-year catalog, the spatial b value and background seismicity rate across five temporal windows are calculated, and 86 M ≥ 5.0 earthquakes as prediction samples are examined. The predictive performance of the background seismicity rate and b value is comprehensively tested and shown to be useful for 5-year forecasting in Yunnan. The performance of the b value exhibits a positive correlation with the predicted earthquake magnitude. The synergistic effect of combining these two predictors is also revealed. Finally, using the threshold corresponding to the maximum PD, we integrate the forecast information of background seismicity rates and the b value. A forward prediction is derived for the period from January 2025 to December 2029. This study can be helpful for disaster preparedness and risk management in Yunnan Province, China. Full article

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies. Full article

Rockfalls are incidents of nature that take place when rocks or boulders break from a steep slope and fall to the ground. They can pose considerable threats to buildings placed in high-risk zones. Despite the fact that the impact of a rockfall on a building can cause structural and non-structural damage, few studies have been undertaken to investigate the danger associated with this event. Most of these studies indicated that the risk resulting from rockfall hazards is hard to forecast and assess. A comprehensive quantitative risk assessment approach for rockfalls on buildings is developed and described in this paper and applied for the Mtein village in Mount Lebanon. This method employs a 3D model to simulate the rockfall trajectories using a combination of digital elevation data, field surveys, and orthorectified aerial photographs. The spatial and temporal probability of rockfalls were evaluated using the analysis of historical data in two triggering-factor scenarios: earthquake and precipitation. The findings show that, during the period of 1472 years between the years 551 (the first observed large earthquake in Lebanon) and the current year of the study (2023), the temporal probability will potentially be equal to 0.002 and 0.105 in the cases of earthquake- and rainfall-triggered rockfalls, respectively, while the maximal damage values are expected to be 232 USD and 10,511 USD per year, respectively. The end result is a final map presenting the risk values assigned to each building that could be damaged by rockfalls. Full article

The spatial distribution of vegetation in a basin has a far-reaching influence on the potential for sediment separation and transport capacity. However, many landslides induced by strong earthquakes have greatly changed the existing pattern, which further increases the probability of debris flow in a basin during heavy rainfall and has a significant impact on the stability of the basin. Thus, this study selected the debris flow basin in the Qipan catchment of the Wenchuan earthquake area as the research object. Multisource and high-precision remote sensing images were used to analyze the land use changes in the basin, and the index of connectivity (IC) was introduced to analyze the evolution of sediment transport capacity. An ecosystem stability assessment method suitable for post-earthquake debris flow basins was proposed. Through quantitative assessment of the ecosystem stability of the basin after the Wenchuan earthquake in 2008 and the two debris flow events after the earthquake, the dynamic relationship between the debris-flow-prone area and the ecosystem stability of the basin was revealed. The results showed that the stability of the ecosystem in the Qipan catchment increased annually, indicating a stable and substable state. The spatial distribution characteristics were lower in the north and south and greater in the middle. By comparing the evaluation results with the actual terrain change trend, the accuracy and feasibility of the evaluation method are verified. The results of this study provide a scientific basis for the formulation of regional disaster prevention strategies and help to accelerate the improvement of regional stability in debris-flow-prone areas. Full article

The utilization of the Monte Carlo method in conjunction with probabilistic seismic hazard analysis (PSHA) constitutes a compelling avenue for exploration. This approach presents itself as an efficient and adaptable alternative to conventional PSHA, particularly when confronted with intricate factors such as parameter uncertainties and diverse earthquake source models. Leveraging the Monte Carlo method and drawing from the widely adopted Cornell-type seismicity model in engineering seismology and disaster mitigation, as well as a seismicity model capturing temporal, spatial, and magnitude inhomogeneity, we have derived a formula for the probability of earthquake intensity occurrence and the mean rate of intensity occurrence over a specified time period. This effort has culminated in the development of a MATLAB-based program named MCPSHA. To assess the model’s efficacy, we selected Baoji City, Shaanxi Province, China, as our research site. Our investigation delves into the disparity between occurrence probability and extreme probability (a surrogate commonly employed for occurrence probability) in the Baoji region over the next 50 years. The findings reveal that the Western region of Baoji exhibits a heightened hazard level, as depicted in the maps, which illustrate a 10% probability of exceedance within a 50-year timeframe. The probability of earthquake occurrence under various intensities (VI, VII, and VIII) over 50 years follows a declining trend from west to east. Furthermore, the likelihood of seismic intensity exceeding VI, VII, and VIII indicates the lowest exceeding probability in the northeast and the highest in the northwest. Notably, for intensities VI-VII, the difference between occurrence probability and extreme probability approaches twice, gradually diminishing with increasing intensity. This study underscores the MCPSHA model’s efficacy in providing robust technical support for mitigating earthquake risk and enhancing the precision of earthquake insurance premium rate calculations. Full article