Probabilistic seismic hazard zonation to investigate the effect of soil condition on earthquake acceleration propagation, Case study Hamadan City, Iran

Probabilistic seismic hazard zonation to investigate the effect of soil condition on earthquake acceleration propagation, Case study Hamadan City, Iran | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Probabilistic seismic hazard zonation to investigate the effect of soil condition on earthquake acceleration propagation, Case study Hamadan City, Iran Hadi Aboutalebi, Mohamad Mohamadi Dehcheshmeh, Gholamreza Ghodrati Amiri, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7261086/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The shear wave velocity (Vs) is a key factor in influencing ground motion intensity and amplifying seismic waves. The city of Hamedan, due to its geological position atop alluvial deposits, exhibits a wide variation in Vs values. This heterogeneity may result in considerable structural damage during major seismic events. This study, which aims to evaluate site-specific effects on ground motion, developed a shear wave velocity map using existing subsurface geotechnical investigations. A probabilistic approach was employed for seismic hazard assessment. The results show that lower-Vs zones exhibit elevated seismic accelerations. Disaggregation analysis identifies the Nahavand and Morvarid faults as major contributors to seismic hazard in Hamedan, particularly under long return periods. The influence of distant seismic sources is evident and should be considered in the seismic design process of tall buildings. Shear wave velocity earthquake hazard analysis seismic hazard disaggregation Hamedan distant seismic sources Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1-Introduction Seismic events have repeatedly caused significant damage in the city of Hamedan throughout history. Notable instances include the Asadabad–Hamedan earthquake of 956 AD and the events that struck Hamedan in 1087 and 1191 AD, among others. Seismic hazard assessment plays a crucial role in risk reduction and urban planning, particularly in tectonically active regions such as Iran. Local site conditions particularly soil type and geotechnical characteristics significantly affect the amplitude and frequency content of seismic waves. This phenomenon, known as site amplification, can result in severe damage even in locations far from the earthquake epicenter (Petersen et al., 2024 ). Therefore, incorporating site-specific soil conditions into seismic hazard zoning is essential for accurate ground motion modeling. Traditional approaches to seismic hazard assessment often rely on deterministic methods that neglect uncertainties related to seismic source parameters, wave propagation paths, and site response. In contrast, Probabilistic Seismic Hazard Analysis (PSHA) offers a robust framework that accounts for these uncertainties, providing more realistic ground motion estimates for engineering applications (Rezaeian et al., 2024). Hamedan, located in western Iran, lies in a seismically active region influenced by several major faults, including the Nahavand and Morvarid faults. Urban expansion in Hamedan has occurred over a geologically heterogeneous subsurface composed of alluvial deposits and older lithologic formations, making the area a compelling case study for examining the influence of local soil conditions on seismic hazard. However, no site-specific PSHA map incorporating detailed geotechnical parameters has yet been developed for the city. The aim of this study is to develop a probabilistic seismic hazard zoning (PSHZ) map for Hamedan, with particular focus on the spatial variation of peak ground acceleration (PGA) as influenced by soil conditions. The methodology involves the compilation of seismotectonic data, selection of appropriate ground motion prediction equations (GMPEs), and integration of site amplification factors derived from regional geotechnical and geophysical datasets. The outcomes of this work are expected to inform seismic design code updates, urban development strategies, and regional risk mitigation planning. Over the past few decades, Probabilistic Seismic Hazard Assessment (PSHA) has become a cornerstone method in earthquake engineering and disaster risk management. A wide array of international research has confirmed that local soil conditions are among the most critical determinants of ground motion response, significantly affecting both the intensity and spatial distribution of seismic acceleration. In particular, studies conducted in the United States (Petersen et al., 2024 ), Japan (Takemura et al., 1991 ), and Turkey (Şeşetyan et al., 2018 ) have demonstrated the profound influence of spatial variability in dynamic soil properties especially shear wave velocity (Vs30) on peak ground acceleration (PGA) and spectral response. At the regional level, and notably in Iran situated along the seismically active Alpine–Himalayan belt the need for seismic hazard assessment is even more pronounced. Recent investigations across western Iranian cities have emphasized the importance of incorporating geotechnical characteristics and local soil variability. For instance, Zafarani et al. (Zafarani et al., 2020 ) reported significant amplification effects in soft to medium-stiff soils in western Iran. Habibi et al. (Habibi et al., 2023 ) demonstrated that the use of appropriate soil classifications, such as those based on NEHRP standards, and accurate input parameters like Vs30, substantially improves hazard estimation by reducing epistemic uncertainties. Abbaszadeh et al. (Abbaszadeh et al., 2011 ) showed that including nonlinear soil response in seismic modeling enhances prediction accuracy. Similarly, Abdollahi et al. (Abdollahi et al., 2023 ) emphasized the amplifying role of shallow surficial soils through extensive geotechnical and seismic data analysis in the Tehran region. Nejad et al. (Nejad et al., 2018 ) proposed microzonation maps for parts of western Iran based on shear wave velocity profiling, providing valuable guidance for engineering applications. Ghobadi and Fereidooni (Ghobadi and Fereidooni, 2012 ) specifically addressed seismic hazard in Hamedan, offering spatial PGA distribution maps derived from integrated geological and statistical modeling. Collectively, these studies highlight that coupling PSHA with local soil parameterization forms a pivotal strategy for improving seismic hazard forecasts and strengthening the scientific foundation for earthquake-resilient urban planning in cities such as Hamedan. However, a critical review of existing literature reveals a frequent underutilization of empirical in-situ data such as downhole testing and standard penetration testing (SPT) in estimating shear wave velocity (Vs). Instead, many hazard assessments rely on empirical correlations, surface geophysical techniques, or low-resolution datasets, which may be acceptable for preliminary studies but are insufficient for high-precision engineering applications. Furthermore, previous seismic hazard studies across Iranian cities have rarely established robust links between subsurface geotechnical layering and seismic input parameters. Detailed shear wave velocity maps derived from field-based measurements have seldom been used to generate spatially resolved PGA maps. As a result, most existing studies operate at a coarse spatial scale, limiting their utility for localized seismic hazard differentiation. In this study, a high-resolution map of near-surface shear wave velocity (Vs30) was generated for Hamedan using a combination of downhole and SPT test data collected from 133 borehole locations. These site-specific measurements were directly integrated into the PSHA framework to analyze the spatial correlation between Vs30 and PGA across the urban extent. For cases where SPT data were available, empirical conversion relationships were used to estimate shear wave velocity. Additionally, geological maps and global Vs30 models (e.g., (Allen and Wald, 2007 )) were employed to supplement areas lacking direct measurements. This integrated, data-driven methodology enables a more refined and spatially sensitive analysis of seismic hazard. The level of resolution and accuracy achieved in this study represents a substantial advancement in PSHA for the region. The developed Vs30 dataset serves not only as a reliable input for ground motion modeling but also as a foundation for engineered seismic microzonation, performance-based seismic design, and urban-scale risk mitigation planning. 2-Geology and seismotectonics of the region 2-1-Geology The geological characteristics of the Hamedan region, located at approximately 48°E longitude and 34°N latitude, were compiled through an extensive review of existing literature, geological mapping, and remote sensing data analysis. The study area lies within the Sanandaj–Sirjan structural zone one of the most tectonically active crustal regions in Iran. This zone forms a northwest–southeast trending linear belt in western Iran and preserves both Mesozoic metamorphic assemblages and evidence of Cenozoic Alpine orogenic processes (Ghobadi and Fereidooni, 2012 ). From a geomorphological perspective, the Hamedan area is characterized by a dichotomous landscape. The western and southwestern parts of the city exhibit rugged, mountainous terrain, while the eastern and northeastern sections are dominated by relatively flat topography and extensive Quaternary alluvial plains (see Fig. 1 ). Geologically, the region encompasses one of the most prominent and geologically significant granitic plutonic bodies within the Iranian crust the Alvand pluton. This extensive granite massif stretches from the northern and eastern outskirts of Hamedan toward Tuyserkan in the southeast and Asadabad in the northwest. Covering an area of approximately 400 km², it is recognized as the largest exposed plutonic rock complex in Iran. The Alvand pluton is primarily composed of porphyroid monzogranite–granodiorite, mesocratic granite, and holocrystalline granite. Quaternary alluvial deposits, which overlie these older lithologic units, are predominantly distributed along the northern and eastern margins of Hamedan. Figure 1 presents the geological map of the study area and its regional context within Iran. Considering the subsequent emphasis on shear wave velocity (Vs), geological formations exhibiting significant spatial variation in Vs values were identified, delineated, and categorized accordingly. 2-2-Tectonic and Seismotectonic In general, seismic hazard assessment for a specific site or region necessitates the identification of all potential seismic sources and the evaluation of their capacity to generate strong ground motion in the future (Haerifard et al., 2018 ). The analysis of active faults and tectonic lineaments plays a critical role in delineating the regional seismotectonic framework (Baftipour et al., 2022 ; Jarahi et al., 2016 ). Seismic sources are typically classified into three categories: point sources, line sources, and areal sources. In this study, seismic sources were modeled as line sources, based on geological and seismotectonic data within a 100-kilometer radius centered on Hamedan. The primary tectonic structures and fault systems in the region include a range of both major and minor faults, namely the Nahavand, Tafresh, Morvarid, Avaj, Kooshk-e-Nosrat, High Zagros, Parandak, Khorrud, Indes, Hasanabad, Soltaniyeh, and Ipak faults (Berberian, 2014 ; Berberian and Yeats, 1999 ; Haerifard et al., 2018 ; Honarvar et al., 2014 ; Jarahi, 2016 ; Jarahi et al., 2022 ; Namdar et al., 2025a ; Namdar et al., 2025b ; Namdar et al., 2025c )(see Fig. 2 ). An evaluation of historical (855–1900 AD) and instrumental (1900–2025 AD) seismicity in the Hamedan region indicates the occurrence of 132 earthquakes with magnitudes ≥ 4.0. Of these, 81 events had magnitudes ≥ 5.5. Notable historical earthquakes include the 976 AH event (Mw 5.3), which impacted both Hamedan and Asadabad; the 1087 AH earthquake (Mw 5.9), which led to the collapse of two towers in Hamedan; and the 1191 AH event, which caused only minor damage (Ambraseys and Melville, 1982 ). Additionally, the 1936 (1315 SH) Golpayegan earthquake, located east of Hamedan, resulted in the complete destruction of 20 villages, while the more recent 2051 (1430 SH) event (Mw 5.9) significantly affected the city (Ambraseys & Melville, 1982 ). Between 1900 and 2025, a total of 132 seismic events with magnitudes ≥ 4.0 were recorded within the 100-km radius zone around Hamedan. A spatial clustering of epicenters is evident to the north, south, and southwest of the city correlating with the active segments of the Avaj and Nahavand faults. Figure 2 presents both the spatial and temporal distributions of historical and instrumental seismicity in the region. Earthquake frequency exhibits an increasing trend over time, particularly in the transition from 885–1900 to 1900–2025, with the 2000s showing the highest decadal frequency (19 events). As is commonly observed, earthquake frequency decreases with increasing magnitude. Specifically, the highest number of events (22) was recorded in the magnitude range of 4.1–4.5, while the lowest frequency (2 events) occurred in the 7.6–7.7 range. 3. Methodology To estimate the seismicity parameters for the Hamedan region, the Gutenberg–Richter (G–R) recurrence model was employed. This model remains one of the most widely adopted frameworks in seismic hazard analysis, enabling the empirical quantification of the frequency–magnitude relationship for earthquakes. The G–R equation is expressed as: logN(M) = a − bM\log N(M) = a - bMlogN(M) = a − bM where N(M)N(M)N(M) represents the cumulative number of earthquakes with magnitudes greater than MMM, and aaa and bbb are constants derived from regional seismic data. To assess local site effects, geotechnical data were compiled and analyzed, including key parameters such as shear wave velocity (Vs) and stratigraphic classifications. These inputs were utilized in Geographic Information Systems (GIS) to produce spatially distributed maps of soil properties, Vs values, and other relevant geotechnical features. In this study, a Probabilistic Seismic Hazard Analysis (PSHA) was conducted for the city of Hamedan and its surrounding areas. The analysis was designed to estimate key seismic hazard parameters, including the Maximum Credible Earthquake (MCE) and the Peak Ground Acceleration (PGA) expected to impact the region. These parameters were determined using a suite of Ground Motion Prediction Equations (GMPEs) sourced from recent literature. The MCE is defined as the largest earthquake that can plausibly occur along a known fault segment, based on current or inferred tectonic activity. This assessment does not explicitly account for recurrence intervals or long-term fault behavior. Seismic hazard modeling was performed using the EZ-FRISK software package, which is widely employed for PSHA and ground motion modeling. The software incorporates multiple approaches to hazard estimation, including the Gutenberg–Richter and Kijko methods, and utilizes both geological and seismological datasets to forecast seismic activity (Ez-FRisk, 2018 ). To quantify the contribution of each seismic source, the Magnitude–Distance Deaggregation (MDD) technique (McGuire, 2004 ) was applied. This method disaggregates total seismic hazard by magnitude and distance bins, providing a breakdown of source contributions to PGA. The results obtained for each fault segment were aggregated to construct a comprehensive regional hazard profile (Bazzurro and Allin Cornell, 1999 ; Harmsen et al., 1999 ; McGuire, 1995 ). This methodology is especially suited for urban-scale hazard evaluations and is instrumental in identifying zones of elevated or reduced seismic risk. 4-Result 4-1-Seismicity Parameters For the identified vulnerable seismic sources, a threshold magnitude of ≥ 4.0 was adopted. As shown in Fig. 3 , a total of 12 fault segments have experienced earthquakes exceeding this threshold. The shortest distances from the centroid of each fault to the center of Hamedan city were measured using seismotectonic maps, and the results are presented in Table 1 . The focal depths of the earthquakes were assumed to be approximately 15 km. This assumption follows recommendations by Berberian (Berberian, 1983 ; Berberian, 1994 ) and Maggi et al. (Maggi et al., 2000), who reported that destructive earthquakes in Iran typically occur at depths shallower than 30 km, with an average of around 15 km. The empirical equations used to estimate the Maximum Credible Earthquake (MCE) for the Hamedan region are listed in Table 1 . These include models developed by Nowroozi (Nowroozi, 1985 ), Ambraseys and Melville (Ambraseys and Melville, 1982 ), and Wells and Coppersmith (Wells and Coppersmith, 1994 ). Maximum magnitudes were calculated based on these empirical relationships, particularly using the formulations by Ambraseys and Melville ( 1982 ), which have been widely applied across the Iranian plateau. Table 1 Maximum magnitudes generated by faults in the study area based on empirical relationships. No. Fault Name Fault Length (km) Max. Magnitude 1 2 3 4 Avg. 1 Nahavand 100 7.3 7.3 7.3 7.2 7.3 2 Tafresh 38 7.4 7.5 7.4 7.4 7.4 3 Morvarid 45 7.3 7.4 7.4 7.3 7.3 4 Avaj 109 7.1 7 7.1 7 7.1 5 Kushk-e-Nosrat > 200 6.9 6.7 6.8 6.8 6.8 6 HZF 119 7.2 7.2 7.2 7.2 7.2 7 Parandak 100 7.4 7.5 7.5 7.4 7.4 8 Khar Rud 112 6.9 6.8 6.9 6.9 6.9 9 Indes 100 7.3 7.3 7.3 7.3 7.3 10 Hasan Abad 85 7.2 7.2 7.3 7.2 7.2 11 Soltaniyeh 80 7.2 7.2 7.2 7.2 7.2 12 Ipak 106 7.3 7.3 7.4 7.3 7.3 Magnitude calculated based on: (1) (Mohajer-Ashjai and Nowroozi, 1978 ), (2) Ambraseys, N. N. & Melville, C. P. ( 1982 ), (3) Nowroozi, A. A. ( 1985 ) and (4) Wells, D. L. & Coppersmith, K. J. ( 1994 ) equations. Intensity calculated based on: Ambraseys, N. N. & Melville, C. P. ( 1982 ). Fault length: Values of actual lengths may be larger than those shown in this table. Collective lengths of subparallel branches and segments are not considered. “>” Is used for long faults (more than 100 Km in length) whose exact length is not established. For faults longer than 200 km, only 100 km of fault length has been considered. The total length of fault is used for faults up to 20 km long. For faults between 20 to 200 km long, respectively, 100–50% of the total lengths has been considered. In the Gutenberg–Richter model, the seismicity parameter a represents the overall level of seismic activity (i.e., the expected number of events for M = 0), while the b-value, typically close to 1, is a tectonic indicator reflecting the relative occurrence of large versus small earthquakes. For the study area, the dataset was partitioned into two subsets: historical records (855–1900) and instrumental records (1900–2025), to develop the Gutenberg–Richter relationship (Gutenberg and Richter, 1950 ; Gutenberg and Richter, 1956 ). Figure 4 displays the temporal distribution of both historical and instrumental earthquake records, along with the corresponding magnitude–frequency distribution, where M denotes the earthquake magnitude. The graph plots the logarithmic cumulative number of events per year for magnitudes M > 5.5, following the method outlined by Krinitzsky (Krinitzsky, 1995 ). A least-squares regression line was fitted across the entire magnitude range, resulting in the following equation: log N = 4.51 − 0.58M Based on this regression, the derived seismicity parameters are: a = 4.51 b = 0.58 with a high correlation coefficient of R = 0.98 (see Fig. 5 ). 4.2. Shear Wave Velocity To assess the spatial distribution of shear wave velocity (Vs) within the study area, a comprehensive dataset was compiled from existing geotechnical investigations. All available projects conducted in the region for Vs measurement were considered, including downhole tests and Standard Penetration Tests (SPTs). The collected data were subsequently filtered, quality-controlled, and georeferenced using ArcGIS to generate a spatial distribution map of Vs. In total, 133 downhole tests and 23 SPTs were conducted across the study area, with their geographic locations shown in Fig. 6 . Empirical correlations established by Ishihara (Ishihara, 1993 ) and Molnar et al. (Molnar et al., 2017 ) were employed to convert SPT values into equivalent shear wave velocities. To compensate for spatial data gaps in direct Vs measurements, two supplementary datasets were utilized: the geological map of Hamedan and the global Vs30 model developed by Allen and Wald (Allen and Wald, 2007 ). In the geological map, lithologic units older than the Quaternary period (> 2.5 million years) were classified as bedrock. Quaternary units were further differentiated based on age and lithological composition. Vs values obtained from site-specific geotechnical investigations (downhole and borehole data) were then assigned to the corresponding geologic units. In the global Vs30 dataset, topographic slope is used as a proxy for Vs, under the assumption that steeper slopes correspond to higher compaction and thus higher shear wave velocities, whereas flatter terrain indicates looser, more compressible soils with lower Vs. All input datasets including downhole test results, SPT-derived estimates, geological unit classifications, and the global Vs30 model were integrated using a hierarchical weighting approach to produce the final composite Vs map (Fig. 6 ). Riverbed areas exhibited the lowest Vs values, while mountainous regions displayed the highest. Within the urban area of Hamedan, Vs ranged from approximately 180 m/s in low-lying zones to over 1800 m/s in the southern highlands. This final Vs map was used as a primary input in ground motion prediction equations in the subsequent seismic hazard analyses. 4.3. Attenuation Equations The selection of suitable ground motion prediction equations (GMPEs) is a critical element in any seismic hazard assessment. In this study, the GMPEs developed under the NGA-West program by the Pacific Earthquake Engineering Research Center (PEER), originally intended for western United States tectonic conditions, were adopted due to their demonstrated compatibility with the tectonic setting of the study area (Shoja–Taheri et al., 2010 ). Accordingly, a set of Next Generation Attenuation (NGA) relationships was used for statistical analyses. These included models developed by Abrahamson et al. (Abrahamson et al., 2014 ), Boore et al. (Boore et al., 2014 ), Campbell and Bozorgnia (Campbell and Bozorgnia, 2014 ), and Chiou and Youngs (Chiou and Youngs, 2014 ). 4.4. Probabilistic Seismic Hazard Assessment (PSHA) Based on standard design-level exceedance probabilities or their corresponding return periods the following seismic design levels were considered: Design Basis Level (DBL): 475-year return period (20% probability of exceedance in 100 years), Maximum Design Level (MDL): 975-year return period (10% in 100 years), Maximum Considered Level (MCL): 2475-year return period (2% in 100 years). For each return period, peak horizontal ground acceleration (PGA) values were estimated at the 84th percentile confidence level. Conventional seismic hazard zoning studies frequently assume a uniform shear wave velocity typically 760 m/s throughout the study area. Under such an assumption, ground motion contour maps exhibit smooth gradients that diminish with increasing distance from seismic sources. In contrast, the present study conducted a comparative assessment between the traditional uniform-Vs approach and one incorporating the spatially resolved Vs distribution map developed in Section 4.2 . To implement this comparison, a grid comprising approximately 700 points was defined across the study area. Higher point densities were assigned to regions with high variability in shear wave velocity, particularly in the alluvial plains. Seismic ground motion calculations were then performed for each grid point. In the first scenario, a constant Vs of 760 m/s representative of reference rock conditions was applied across the entire region. Ground motion simulations were conducted using EZ-FRISK software, employing NGA-based attenuation models. The second scenario integrated the spatially variable Vs map. For both cases, seismic hazard maps were generated for horizontal ground acceleration at a spectral acceleration period of SA = 0.2 seconds, corresponding to return periods of 475, 975, and 2475 years (Fig. 7 ). Overall, the results demonstrate that ground motion attenuation generally follows an expected decline with increasing distance from seismic sources, except in areas where shear wave velocity (Vs) exhibits significant reductions. For the 475-year return period, the estimated peak ground acceleration (PGA) in Hamedan ranged from 0.15 to 0.20 g under the assumption of a uniform Vs. When incorporating the spatially variable Vs map, this range increased to 0.25 to 0.30 g, representing a 33% increase. A similar amplification trend was observed for longer return periods: 975-year return period: PGA increased from 0.20–0.25 g to 0.35–0.40 g, indicating a 60% increase. 2475-year return period: PGA increased from 0.25–0.30 g to 0.45–0.50 g, yielding a 70% increase. These results underscore the pivotal role of spatial shear wave velocity variability in influencing ground motion estimations and highlight the necessity of incorporating site-specific geotechnical conditions into probabilistic seismic hazard assessments. 4.5. Magnitude–Distance Deaggregation (MDD) As previously discussed, the influence of distant seismic sources becomes more pronounced at longer return periods. To quantify this effect, it is essential to disaggregate the total ground motion hazard by seismic source, magnitude, and distance. The most effective approach for this purpose is the magnitude–distance–epsilon (M–R–ε) deaggregation method, which enables identification of dominant sources and comparative evaluation of their respective contributions. Accordingly, deaggregation plots were generated for return periods of 475, 975, and 2475 years (Fig. 8 ). Since ground motion contributions from sources within 50 km are negligible, the plots begin at this threshold, confirming the absence of near-fault effects in the hazard estimates. As illustrated, two principal seismic sources dominate the ground acceleration at the site: Nahavand Fault – approximate magnitude Mw 7.0, located ~ 60 km from Hamedan Morvarid Fault – approximate magnitude Mw 6.8, located ~ 102 km from Hamedan Both faults are recognized branches of the active Zagros Fault System. Figure 9 examines the relationship between epsilon (ε) and the probability of occurrence. As the return period increases, epsilon values exhibit a clear upward trend approximately 18% for the 975-year period and about 24% for the 2475-year period. The figure also presents the relative contribution of individual seismic sources to total hazard. Among them, the Nahavand Fault dominates, contributing 84%, 86%, and 89% of the total seismic hazard for return periods of 475, 975, and 2475 years, respectively. Additionally, epsilon values for each source are plotted as a function of distance from Hamedan. Across all return periods, epsilon increases with distance. A consistent increase in epsilon is also observed with increasing return periods. 4. Discussion The study area encompasses the city of Hamedan and its adjacent regions. Given the presence of vital socio-economic infrastructure, detailed analysis of seismicity parameters and probabilistic seismic hazard is of paramount importance. In this study, a probabilistic spectral acceleration zoning and seismic hazard disaggregation analysis was conducted. After completing a regional geological assessment, the tectonic framework and seismicity of the area were evaluated. A seismotectonic model was then developed to better define the seismic behavior of the region. A shear wave velocity (Vs) map for the upper 30 meters of the subsurface was generated using existing geotechnical test data. Ground motion probabilistic analysis was conducted using two approaches: In the first approach aligned with conventional national practices a constant Vs of 760 m/s was assumed across the study area. In the second approach, the detailed Vs map was directly incorporated into the analysis framework. Seismic hazard disaggregation allowed for the identification of the dominant hazard scenario in terms of earthquake magnitude, causative fault, and distance to the city of Hamedan. Seismicity parameters were computed at the center of Hamedan and assigned to each seismic source appropriately, considering its tectonic context and seismic potential. Based on this analysis, and accounting for the exceedance probabilities (or equivalent return periods) corresponding to DBL, MDL, and MCL design levels, the maximum horizontal ground acceleration at the 84th percentile was estimated across the study area. In the initial analysis phase, the assumption of a uniform Vs value (760 m/s) was applied. Ground motion calculations were carried out in EZ-FRISK using four NGA ground motion prediction models, and seismic hazard maps were developed for spectral acceleration at a period of 0.2 seconds. 5. Conclusion Ground acceleration generally decreases with increasing distance from seismic sources except in cases where shear wave velocity (Vs) exhibits significant reductions. When a uniform Vs value is assumed across the region, this attenuation pattern remains relatively smooth and predictable. However, the integration of a spatially variable Vs map into the analysis reveals markedly different behavior. Specifically, ground acceleration values show notable amplification in zones characterized by low Vs values, such as alluvial plains, riverbeds, and areas underlain by soft sediments. The regions surrounding the Alvasjerd, Abshineh, and Bahari rivers demonstrate clear evidence of seismic wave amplification due to local site effects. Incorporating the spatially detailed Vs map into seismic hazard analysis leads to substantial increases in ground motion estimates. This increase becomes more pronounced for longer seismic return periods. These findings underscore not only the direct influence of low Vs zones on seismic amplification, but also the enhanced contribution of distant seismic sources particularly the Nahavand Fault (average distance ~ 60 km) to seismic hazard at extended return intervals. Magnitude–distance–epsilon (M–R–ε) deaggregation plots for return periods of 475, 975, and 2475 years confirm that seismic hazard in the Hamedan region is dominated by two principal sources: The Nahavand Fault, with an estimated moment magnitude (Mw) of approximately 7.0 at a distance of ~ 60 km The Morvarid Fault, with an Mw of approximately 6.8 at a distance of ~ 102 km Despite their relatively long distances from the urban core, these faults are capable of generating significant ground motions during large-magnitude events, with potential implications for the performance of mid- to high-rise structures. Additionally, the analysis shows that as the return period increases, epsilon values also increase, indicating greater uncertainty and the growing influence of distant sources in long-term hazard projections. Author Contributions: Hadi Aboutalebi and Mohammad Mehdi Deh Cheshmeh: formal analysis, fieldwork, methodology, investigation, and writing the first draft of the manuscript; Gholamreza Ghodrati Amiri and Seyed Ali Razavian Amraei: fieldwork, conceptualization and design of the study, review, and editing. All authors have read and agreed to the published version of the manuscript. All authors contributed to the research and preparation of the manuscript and approved the submitted version. Declarations Funding: This research received no external funding . Institutional Review Board Statement: Not applicable . Informed Consent Statement: Not applicable . Data Availability Statement: Microseismicity data from https://www.bhrc.ac.ir/en . Acknowledgments: This article is extracted from the PhD thesis of the first author.The authors extend their gratitude to Hamedan Municipality for Downhole data. Conflicts of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest . Author Contribution Hadi Aboutalebi: Conducted the geotechnical data collection, shear wave velocity modeling, and contributed to seismic hazard analysis. Drafted the initial manuscript and prepared visual materials (maps and charts).Mohamad Mohamadi Dehcheshmeh: Conceived and designed the overall research framework. Performed the probabilistic seismic hazard analysis and disaggregation modeling. Reviewed and edited the manuscript. Corresponding author.Gholamreza Ghodrati Amiri: Provided technical supervision and critical feedback on seismic hazard methodology. Validated the analytical models and revised the final manuscript for theoretical accuracy.Seyed Ali Razavian Amrei: Advised on structural seismic implications and regional geological interpretations. 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Berberian, M. and Yeats, R.S., 2016. Tehran: An Earthquake Time Bomb; In Tectonic Evolution, Collision, and Seismicity of Southwest Asia: In Honor of Manuel Berberian’s Forty-Five Years of Research Contributions. The Geological Society of America, 1(Special Paper 525): 84. Boore, D., Stewart, J., Seyhan, E. and Atkinson, G., 2014. NGA-West2 equations for predicting PGA, PGV, and 5% damped PSA for shallow crustal earthquakes. NGA-West2 Equations for Predicting PGA, PGV, and 5% Damped PSA for Shallow Crustal Earthquakes, 30: 1057-1085. Campbell, K.W. and Bozorgnia, Y., 2014. NGA-West2 Ground Motion Model for the Average Horizontal Components of PGA, PGV, and 5% Damped Linear Acceleration Response Spectra. Earthquake Spectra, 30(3): 1087-1115. Chiou, B.S.-J. and Youngs, R.R., 2014. Update of the Chiou and Youngs NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra. Earthquake Spectra, 30(3): 1117-1153. Ez-FRisk, 2018. Software for Earthquake Ground Motion Estimation. Risk Engineering INC, Boulder, Colorado. Ghobadi, M.H. and Fereidooni, D., 2012. Seismic hazard assessment of the city of Hamedan and its vicinity, west of Iran. Natural Hazards, 63. Gutenberg, B. and Richter, C.F., 1950. Seismicity of the Earth and associated phenomena. MAUSAM, 1: 174-176. Gutenberg, B. and Richter, C.F., 1956. Earthquake Magnitude, Intensity, Energy and Acceleration. Bull. Seism. Soc. Am., 46: 105-145. Habibi, R., Pourkermani, M., Ghorashi, M., Almasian, M. and Jarahi, H., 2023. The Effects of Quaternary Sediments on Earthquake Acceleration. Himalayan Geology, 44: 71-80. Haerifard, S., Jarahi, H., Pourkermani, M. and Almasian, M., 2018. Seismic Hazard Assessment at Esfaraen‒Bojnurd Railway, North‒East of Iran. Geotectonics, 52(1): 151-156. Harmsen, S., Perkins, D. and Frankel, A., 1999. Deaggregation of probabilistic ground motions in the central and eastern United States. 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Deterministic versus probabilistic seismic hazard analysis for critical structures. Engineering geology, 40(1-2): 1-7. Maggi, A., A., J.J., D., M. and C., P.K., 2000. Earthquake focal depths effective elastic thickness ، and the strength of the continental lithosphere. Geology 28: 495-498. McGuire, R.K., 1995. Probabilistic seismic hazard analysis and design earthquakes: closing the loop, Geotechnical and Environmental Geophysics. B. Seismol. Soc. Am., pp. 1275–1284. McGuire, R.K., 2004. Seismic Hazard and Risk Analysis. Earthquake Engineering Research Institute. Mohajer-Ashjai, A. and Nowroozi, A.A., 1978. Observed and probable intensity zoning of Iran. Tectonophysics, 49(3): 149-160. Molnar, S., Onwuemeka, J. and Adhikari, S., 2017. Rapid Post-Earthquake Microtremor Measurements for Site Amplification and Shear Wave Velocity Profiling in Kathmandu, Nepal. Earthquake Spectra, 33. Namdar, D., Jarahi, H. and Maghami Moghim, G., 2025a. Introduction to Hecatompylos Lake in Damghan, 17th Conference of the Iranian Paleontological Society, Hormozgan University, pp. 8. Namdar, D., Jarahi, H. and Maghami Moghim, G., 2025b. Paleo Mega Lake of Rey, An Introduction to Water Level-Volume Changes Over Time from a Morphological Perspective, 7th International Conference of Biology and Earth Science, Hamadan, pp. 1-9. Namdar, D., Jarahi, H. and Maghami Moghim, G., 2025c. Paleo Mega Lake of Rey, North Yazd Paleoshoreline Sedimentology, 9th Symposoum of Sedimentological Society of Iran, Tabas, pp. 1-10. Nejad, M.M., Momeni, M.S. and Manahiloh, K.N., 2018. Shear wave velocity and soil type microzonation using neural networks and geographic information system. Soil Dynamics and Earthquake Engineering, 104: 54-63. Nowroozi, A.A., 1985. Empirical Relations between Magnitudes and Fault Parameters for Earthquakes in Iran. Bull. Seism. Soc. Am., 75(5): 1327-1338. Petersen, M.D., Shumway, A.M., Powers, P.M., Field, E.H., Moschetti, M.P., Jaiswal, K.S., Milner, K.R., Rezaeian, S., Frankel, A.D., Llenos, A.L., Michael, A.J., Altekruse, J.M., Ahdi, S.K., Withers, K.B., Mueller, C.S., Zeng, Y., Chase, R.E., Salditch, L.M., Luco, N., Rukstales, K.S., Herrick, J.A., Girot, D.L., Aagaard, B.T., Bender, A.M., Blanpied, M.L., Briggs, R.W., Boyd, O.S., Clayton, B.S., DuRoss, C.B., Evans, E.L., Haeussler, P.J., Hatem, A.E., Haynie, K.L., Hearn, E.H., Johnson, K.M., Kortum, Z.A., Kwong, N.S., Makdisi, A.J., Mason, H.B., McNamara, D.E., McPhillips, D.F., Okubo, P.G., Page, M.T., Pollitz, F.F., Rubinstein, J.L., Shaw, B.E., Shen, Z.-K., Shiro, B.R., Smith, J.A., Stephenson, W.J., Thompson, E.M., Thompson Jobe, J.A., Wirth, E.A. and Witter, R.C., 2024. The 2023 US 50-State National Seismic Hazard Model: Overview and implications. Earthquake Spectra, 40(1): 5-88. Rezaeian, S., Powers, P.M., Altekruse, J., Ahdi, S.K., Petersen, M.D., Shumway, A.M., Frankel, A.D., Wirth, E.A., Smith, J.A., Moschetti, M.P., Withers, K.B. and Herrick, J.A., 2024. The 2023 US National Seismic Hazard Model: Subduction ground-motion models. Earthquake Spectra, 40(3): 1739-1786. Şeşetyan, K., Danciu, L., Demircioğlu Tümsa, M.B., Giardini, D., Erdik, M., Akkar, S., Gülen, L., Zare, M., Adamia, S., Ansari, A., Arakelyan, A., Askan, A., Avanesyan, M., Babayan, H., Chelidze, T., Durgaryan, R., Elias, A., Hamzehloo, H., Hessami, K., Kalafat, D., Kale, Ö., Karakhanyan, A., Khan, M.A., Mammadli, T., Al-Qaryouti, M., Sayab, M., Tsereteli, N., Utkucu, M., Varazanashvili, O., Waseem, M., Yalçın, H. and Yılmaz, M.T., 2018. The 2014 seismic hazard model of the Middle East: overview and results. Bulletin of Earthquake Engineering, 16(8): 3535-3566. Sheikholeslami, M.R., Javadi, H.R., Assadi Sarshar, M., Agha Hoseini, A., Kouhpeyma, M. and Vahdati Daneshmend, B., 2014. Iran Faults Encyclopedia, 1. Research Institue of Earth Science, Geological Survey & Mineral Exploration of Iran, Tehran, Iran, 600 pp. Shoja–Taheri, J., Nnaserieh, S. and Hadi, G., 2010. A Test of the Applicability of NGA Models to the Strong Ground-Motion Data in the Iranian Plateau. Journal of Earthquake Engineering, 14: 278–292. Takemura, M., Kato, K., Ikeura, T. and Shima, E., 1991. Site amplification of S-waves from strong motion records in special relation to surface geology. Journal of Physics of the Earth, 39(3): 537-552. Wells, D.L. and Coppersmith, K.J., 1994. New Empirical Relationships among Magnitude, Rupture Length, Rupture Width, Rupture Area and Surface Displacement. Bulletin of the Seismological Society of America, 84(4): 974-1002. Zafarani, H., Jafarian, Y., Eskandarinejad, A., Lashgari, A., Soghrat, M.R., Sharafi, H. and Afraz-e Haji-Saraei, M., 2020. Seismic hazard analysis and local site effect of the 2017 M w 7.3 Sarpol-e Zahab, Iran, earthquake. Natural Hazards, 103(2): 1783-1805. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7261086","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502925817,"identity":"120626f1-5435-42e3-9e2e-c8aa90ff3cce","order_by":0,"name":"Hadi Aboutalebi","email":"","orcid":"","institution":"ShK.C, Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Hadi","middleName":"","lastName":"Aboutalebi","suffix":""},{"id":502925818,"identity":"d85edd08-14c4-4fe6-ba15-46fadda0c88a","order_by":1,"name":"Mohamad Mohamadi Dehcheshmeh","email":"data:image/png;base64,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","orcid":"","institution":"ShK.C, Islamic Azad University","correspondingAuthor":true,"prefix":"","firstName":"Mohamad","middleName":"Mohamadi","lastName":"Dehcheshmeh","suffix":""},{"id":502925819,"identity":"3e2a0551-0add-43a4-a5f2-8408824204dd","order_by":2,"name":"Gholamreza Ghodrati Amiri","email":"","orcid":"","institution":"Natural Disasters Prevention Research Center, School of Civil Engineering, Iran University of Science and Technology,","correspondingAuthor":false,"prefix":"","firstName":"Gholamreza","middleName":"Ghodrati","lastName":"Amiri","suffix":""},{"id":502925820,"identity":"99521664-f456-4fae-a254-a64d6f69663a","order_by":3,"name":"Seyed Ali Razavian Amiri","email":"","orcid":"","institution":"Payam Noor University","correspondingAuthor":false,"prefix":"","firstName":"Seyed","middleName":"Ali Razavian","lastName":"Amiri","suffix":""}],"badges":[],"createdAt":"2025-07-31 10:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7261086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7261086/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90300946,"identity":"70be1c46-043e-49f3-b488-5dec65a9c3cc","added_by":"auto","created_at":"2025-09-01 08:55:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":796995,"visible":true,"origin":"","legend":"\u003cp\u003eGeological map of Quaternary units in the Hamedan region. Dark gray areas are classified as bedrock.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7261086/v1/b0b294943302bcd79116bc3f.png"},{"id":90300943,"identity":"fd3b2f9c-6ead-4dbf-93d5-3e406d23bca8","added_by":"auto","created_at":"2025-09-01 08:55:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":989099,"visible":true,"origin":"","legend":"\u003cp\u003eSeismotectonic map of the Hamedan region. Significant earthquakes have occurred in both the northeastern and southwestern sectors. Fault trace data are derived from Sheikholeslami et al. (Sheikholeslami et al., 2014); historical earthquake records from Ambraseys and Melville (1982); seismogenic zones from Berberian and Yeats (Berberian and Yeats, 2016); and the 12.5-meter resolution Digital Elevation Model (DEM) was obtained from ALOS PALSAR satellite imagery.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7261086/v1/f0416fd6a1464861afe3a7fa.png"},{"id":90300965,"identity":"8c4ca4b7-2cf6-4179-bc25-02515f28ef54","added_by":"auto","created_at":"2025-09-01 08:55:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":216469,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal distribution of maximum magnitudes of historical earthquakes within a 150 km radius around the city of Hamedan.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7261086/v1/873540c161870683fc3abde1.png"},{"id":90300961,"identity":"5ef1c58e-545d-458c-b514-cc7d38dae391","added_by":"auto","created_at":"2025-09-01 08:55:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11177,"visible":true,"origin":"","legend":"\u003cp\u003eMagnitude–frequency relationship of earthquakes within the study area.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7261086/v1/05212303565c94328fd0ebbd.png"},{"id":90301969,"identity":"886a57c1-6c9f-457c-8e0a-12d18cb8a1e8","added_by":"auto","created_at":"2025-09-01 09:03:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":19361,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between earthquake magnitude (M) and the logarithmic cumulative frequency of events in the study area.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7261086/v1/00efce45aef46a9b43b9a25c.png"},{"id":90300959,"identity":"79cccb17-7cf5-46f6-88b0-6cf4622c40b7","added_by":"auto","created_at":"2025-09-01 08:55:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":488571,"visible":true,"origin":"","legend":"\u003cp\u003eFinal shear wave velocity (Vs) map of the study area.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7261086/v1/1fda5e056c7aed84650a9f39.png"},{"id":90300945,"identity":"e2d31df0-5cb2-4ff7-bfc3-3d4f8fc98b2b","added_by":"auto","created_at":"2025-09-01 08:55:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1135722,"visible":true,"origin":"","legend":"\u003cp\u003eSeismic hazard zoning maps at 0.2-second spectral acceleration. From top to bottom: return periods of 475, 975, and 2475 years, respectively. Left column: uniform shear wave velocity assumption (760 m/s); right column: incorporating the spatially variable Vs map.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7261086/v1/8b36630c262d22674b33eebd.png"},{"id":90301968,"identity":"2dfdf544-5d99-41d7-baab-fbf4291b81cb","added_by":"auto","created_at":"2025-09-01 09:03:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":443145,"visible":true,"origin":"","legend":"\u003cp\u003eEarthquake hazard deaggregation by magnitude–distance. From top to bottom: return periods of 475, 975, and 2475 years. The contributing faults remain the same across all plots.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7261086/v1/a7d19737fbf329799a939d22.png"},{"id":90300948,"identity":"7a3bfc91-7be0-4342-af9a-1762d43abd9e","added_by":"auto","created_at":"2025-09-01 08:55:49","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":109253,"visible":true,"origin":"","legend":"\u003cp\u003eFrom top to bottom: (1) probability of earthquake occurrence versus epsilon (ε); (2) contribution of each seismic source to total hazard; (3) epsilon values of individual seismic sources for return periods of 475, 975, and 2475 years.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7261086/v1/ddefaea4cf40d021eda6463a.png"},{"id":92426126,"identity":"4160f2d3-1983-4451-844c-1eff5022e328","added_by":"auto","created_at":"2025-09-29 15:17:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5074866,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7261086/v1/d438fc62-68eb-44ec-8ce9-0a4ce9a392dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Probabilistic seismic hazard zonation to investigate the effect of soil condition on earthquake acceleration propagation, Case study Hamadan City, Iran","fulltext":[{"header":"1-Introduction","content":"\u003cp\u003eSeismic events have repeatedly caused significant damage in the city of Hamedan throughout history. Notable instances include the Asadabad\u0026ndash;Hamedan earthquake of 956 AD and the events that struck Hamedan in 1087 and 1191 AD, among others. Seismic hazard assessment plays a crucial role in risk reduction and urban planning, particularly in tectonically active regions such as Iran. Local site conditions particularly soil type and geotechnical characteristics significantly affect the amplitude and frequency content of seismic waves. This phenomenon, known as site amplification, can result in severe damage even in locations far from the earthquake epicenter (Petersen et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, incorporating site-specific soil conditions into seismic hazard zoning is essential for accurate ground motion modeling. Traditional approaches to seismic hazard assessment often rely on deterministic methods that neglect uncertainties related to seismic source parameters, wave propagation paths, and site response. In contrast, Probabilistic Seismic Hazard Analysis (PSHA) offers a robust framework that accounts for these uncertainties, providing more realistic ground motion estimates for engineering applications (Rezaeian et al., 2024). Hamedan, located in western Iran, lies in a seismically active region influenced by several major faults, including the Nahavand and Morvarid faults. Urban expansion in Hamedan has occurred over a geologically heterogeneous subsurface composed of alluvial deposits and older lithologic formations, making the area a compelling case study for examining the influence of local soil conditions on seismic hazard. However, no site-specific PSHA map incorporating detailed geotechnical parameters has yet been developed for the city. The aim of this study is to develop a probabilistic seismic hazard zoning (PSHZ) map for Hamedan, with particular focus on the spatial variation of peak ground acceleration (PGA) as influenced by soil conditions. The methodology involves the compilation of seismotectonic data, selection of appropriate ground motion prediction equations (GMPEs), and integration of site amplification factors derived from regional geotechnical and geophysical datasets. The outcomes of this work are expected to inform seismic design code updates, urban development strategies, and regional risk mitigation planning.\u003c/p\u003e\u003cp\u003eOver the past few decades, Probabilistic Seismic Hazard Assessment (PSHA) has become a cornerstone method in earthquake engineering and disaster risk management. A wide array of international research has confirmed that local soil conditions are among the most critical determinants of ground motion response, significantly affecting both the intensity and spatial distribution of seismic acceleration. In particular, studies conducted in the United States (Petersen et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Japan (Takemura et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), and Turkey (Şeşetyan et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) have demonstrated the profound influence of spatial variability in dynamic soil properties especially shear wave velocity (Vs30) on peak ground acceleration (PGA) and spectral response.\u003c/p\u003e\u003cp\u003eAt the regional level, and notably in Iran situated along the seismically active Alpine\u0026ndash;Himalayan belt the need for seismic hazard assessment is even more pronounced. Recent investigations across western Iranian cities have emphasized the importance of incorporating geotechnical characteristics and local soil variability. For instance, Zafarani et al. (Zafarani et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported significant amplification effects in soft to medium-stiff soils in western Iran. Habibi et al. (Habibi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrated that the use of appropriate soil classifications, such as those based on NEHRP standards, and accurate input parameters like Vs30, substantially improves hazard estimation by reducing epistemic uncertainties. Abbaszadeh et al. (Abbaszadeh et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) showed that including nonlinear soil response in seismic modeling enhances prediction accuracy. Similarly, Abdollahi et al. (Abdollahi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasized the amplifying role of shallow surficial soils through extensive geotechnical and seismic data analysis in the Tehran region. Nejad et al. (Nejad et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) proposed microzonation maps for parts of western Iran based on shear wave velocity profiling, providing valuable guidance for engineering applications. Ghobadi and Fereidooni (Ghobadi and Fereidooni, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) specifically addressed seismic hazard in Hamedan, offering spatial PGA distribution maps derived from integrated geological and statistical modeling. Collectively, these studies highlight that coupling PSHA with local soil parameterization forms a pivotal strategy for improving seismic hazard forecasts and strengthening the scientific foundation for earthquake-resilient urban planning in cities such as Hamedan. However, a critical review of existing literature reveals a frequent underutilization of empirical in-situ data such as downhole testing and standard penetration testing (SPT) in estimating shear wave velocity (Vs). Instead, many hazard assessments rely on empirical correlations, surface geophysical techniques, or low-resolution datasets, which may be acceptable for preliminary studies but are insufficient for high-precision engineering applications.\u003c/p\u003e\u003cp\u003eFurthermore, previous seismic hazard studies across Iranian cities have rarely established robust links between subsurface geotechnical layering and seismic input parameters. Detailed shear wave velocity maps derived from field-based measurements have seldom been used to generate spatially resolved PGA maps. As a result, most existing studies operate at a coarse spatial scale, limiting their utility for localized seismic hazard differentiation. In this study, a high-resolution map of near-surface shear wave velocity (Vs30) was generated for Hamedan using a combination of downhole and SPT test data collected from 133 borehole locations. These site-specific measurements were directly integrated into the PSHA framework to analyze the spatial correlation between Vs30 and PGA across the urban extent. For cases where SPT data were available, empirical conversion relationships were used to estimate shear wave velocity. Additionally, geological maps and global Vs30 models (e.g., (Allen and Wald, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)) were employed to supplement areas lacking direct measurements. This integrated, data-driven methodology enables a more refined and spatially sensitive analysis of seismic hazard. The level of resolution and accuracy achieved in this study represents a substantial advancement in PSHA for the region. The developed Vs30 dataset serves not only as a reliable input for ground motion modeling but also as a foundation for engineered seismic microzonation, performance-based seismic design, and urban-scale risk mitigation planning.\u003c/p\u003e"},{"header":"2-Geology and seismotectonics of the region","content":"\n\u003ch3\u003e2-1-Geology\u003c/h3\u003e\n\u003cp\u003eThe geological characteristics of the Hamedan region, located at approximately 48\u0026deg;E longitude and 34\u0026deg;N latitude, were compiled through an extensive review of existing literature, geological mapping, and remote sensing data analysis. The study area lies within the Sanandaj\u0026ndash;Sirjan structural zone one of the most tectonically active crustal regions in Iran. This zone forms a northwest\u0026ndash;southeast trending linear belt in western Iran and preserves both Mesozoic metamorphic assemblages and evidence of Cenozoic Alpine orogenic processes (Ghobadi and Fereidooni, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). From a geomorphological perspective, the Hamedan area is characterized by a dichotomous landscape. The western and southwestern parts of the city exhibit rugged, mountainous terrain, while the eastern and northeastern sections are dominated by relatively flat topography and extensive Quaternary alluvial plains (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Geologically, the region encompasses one of the most prominent and geologically significant granitic plutonic bodies within the Iranian crust the Alvand pluton. This extensive granite massif stretches from the northern and eastern outskirts of Hamedan toward Tuyserkan in the southeast and Asadabad in the northwest. Covering an area of approximately 400 km\u0026sup2;, it is recognized as the largest exposed plutonic rock complex in Iran. The Alvand pluton is primarily composed of porphyroid monzogranite\u0026ndash;granodiorite, mesocratic granite, and holocrystalline granite. Quaternary alluvial deposits, which overlie these older lithologic units, are predominantly distributed along the northern and eastern margins of Hamedan. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the geological map of the study area and its regional context within Iran. Considering the subsequent emphasis on shear wave velocity (Vs), geological formations exhibiting significant spatial variation in Vs values were identified, delineated, and categorized accordingly.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e2-2-Tectonic and Seismotectonic\u003c/h3\u003e\n\u003cp\u003eIn general, seismic hazard assessment for a specific site or region necessitates the identification of all potential seismic sources and the evaluation of their capacity to generate strong ground motion in the future (Haerifard et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The analysis of active faults and tectonic lineaments plays a critical role in delineating the regional seismotectonic framework (Baftipour et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jarahi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Seismic sources are typically classified into three categories: point sources, line sources, and areal sources. In this study, seismic sources were modeled as line sources, based on geological and seismotectonic data within a 100-kilometer radius centered on Hamedan. The primary tectonic structures and fault systems in the region include a range of both major and minor faults, namely the Nahavand, Tafresh, Morvarid, Avaj, Kooshk-e-Nosrat, High Zagros, Parandak, Khorrud, Indes, Hasanabad, Soltaniyeh, and Ipak faults (Berberian, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Berberian and Yeats, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Haerifard et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Honarvar et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jarahi, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jarahi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Namdar et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e; Namdar et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; Namdar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025c\u003c/span\u003e)(see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). An evaluation of historical (855\u0026ndash;1900 AD) and instrumental (1900\u0026ndash;2025 AD) seismicity in the Hamedan region indicates the occurrence of 132 earthquakes with magnitudes\u0026thinsp;\u0026ge;\u0026thinsp;4.0. Of these, 81 events had magnitudes\u0026thinsp;\u0026ge;\u0026thinsp;5.5. Notable historical earthquakes include the 976 AH event (Mw 5.3), which impacted both Hamedan and Asadabad; the 1087 AH earthquake (Mw 5.9), which led to the collapse of two towers in Hamedan; and the 1191 AH event, which caused only minor damage (Ambraseys and Melville, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Additionally, the 1936 (1315 SH) Golpayegan earthquake, located east of Hamedan, resulted in the complete destruction of 20 villages, while the more recent 2051 (1430 SH) event (Mw 5.9) significantly affected the city (Ambraseys \u0026amp; Melville, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1982\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBetween 1900 and 2025, a total of 132 seismic events with magnitudes\u0026thinsp;\u0026ge;\u0026thinsp;4.0 were recorded within the 100-km radius zone around Hamedan. A spatial clustering of epicenters is evident to the north, south, and southwest of the city correlating with the active segments of the Avaj and Nahavand faults. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents both the spatial and temporal distributions of historical and instrumental seismicity in the region. Earthquake frequency exhibits an increasing trend over time, particularly in the transition from 885\u0026ndash;1900 to 1900\u0026ndash;2025, with the 2000s showing the highest decadal frequency (19 events). As is commonly observed, earthquake frequency decreases with increasing magnitude. Specifically, the highest number of events (22) was recorded in the magnitude range of 4.1\u0026ndash;4.5, while the lowest frequency (2 events) occurred in the 7.6\u0026ndash;7.7 range.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eTo estimate the seismicity parameters for the Hamedan region, the Gutenberg\u0026ndash;Richter (G\u0026ndash;R) recurrence model was employed. This model remains one of the most widely adopted frameworks in seismic hazard analysis, enabling the empirical quantification of the frequency\u0026ndash;magnitude relationship for earthquakes. The G\u0026ndash;R equation is expressed as:\u003c/p\u003e\u003cp\u003elogN(M)\u0026thinsp;=\u0026thinsp;a\u0026thinsp;\u0026minus;\u0026thinsp;bM\\log N(M)\u0026thinsp;=\u0026thinsp;a - bMlogN(M)\u0026thinsp;=\u0026thinsp;a\u0026thinsp;\u0026minus;\u0026thinsp;bM\u003c/p\u003e\u003cp\u003ewhere N(M)N(M)N(M) represents the cumulative number of earthquakes with magnitudes greater than MMM, and aaa and bbb are constants derived from regional seismic data.\u003c/p\u003e\u003cp\u003eTo assess local site effects, geotechnical data were compiled and analyzed, including key parameters such as shear wave velocity (Vs) and stratigraphic classifications. These inputs were utilized in Geographic Information Systems (GIS) to produce spatially distributed maps of soil properties, Vs values, and other relevant geotechnical features. In this study, a Probabilistic Seismic Hazard Analysis (PSHA) was conducted for the city of Hamedan and its surrounding areas. The analysis was designed to estimate key seismic hazard parameters, including the Maximum Credible Earthquake (MCE) and the Peak Ground Acceleration (PGA) expected to impact the region. These parameters were determined using a suite of Ground Motion Prediction Equations (GMPEs) sourced from recent literature. The MCE is defined as the largest earthquake that can plausibly occur along a known fault segment, based on current or inferred tectonic activity. This assessment does not explicitly account for recurrence intervals or long-term fault behavior. Seismic hazard modeling was performed using the EZ-FRISK software package, which is widely employed for PSHA and ground motion modeling. The software incorporates multiple approaches to hazard estimation, including the Gutenberg\u0026ndash;Richter and Kijko methods, and utilizes both geological and seismological datasets to forecast seismic activity (Ez-FRisk, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To quantify the contribution of each seismic source, the Magnitude\u0026ndash;Distance Deaggregation (MDD) technique (McGuire, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) was applied. This method disaggregates total seismic hazard by magnitude and distance bins, providing a breakdown of source contributions to PGA. The results obtained for each fault segment were aggregated to construct a comprehensive regional hazard profile (Bazzurro and Allin Cornell, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Harmsen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; McGuire, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). This methodology is especially suited for urban-scale hazard evaluations and is instrumental in identifying zones of elevated or reduced seismic risk.\u003c/p\u003e"},{"header":"4-Result","content":"\n\u003ch3\u003e4-1-Seismicity Parameters\u003c/h3\u003e\n\u003cp\u003eFor the identified vulnerable seismic sources, a threshold magnitude of \u0026ge;\u0026thinsp;4.0 was adopted. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a total of 12 fault segments have experienced earthquakes exceeding this threshold. The shortest distances from the centroid of each fault to the center of Hamedan city were measured using seismotectonic maps, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The focal depths of the earthquakes were assumed to be approximately 15 km. This assumption follows recommendations by Berberian (Berberian, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Berberian, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) and Maggi et al. (Maggi et al., 2000), who reported that destructive earthquakes in Iran typically occur at depths shallower than 30 km, with an average of around 15 km. The empirical equations used to estimate the Maximum Credible Earthquake (MCE) for the Hamedan region are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These include models developed by Nowroozi (Nowroozi, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), Ambraseys and Melville (Ambraseys and Melville, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), and Wells and Coppersmith (Wells and Coppersmith, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Maximum magnitudes were calculated based on these empirical relationships, particularly using the formulations by Ambraseys and Melville (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), which have been widely applied across the Iranian plateau.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMaximum magnitudes generated by faults in the study area based on empirical relationships.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFault Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFault Length (km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\u003cp\u003eMax. Magnitude\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAvg.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNahavand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTafresh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMorvarid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAvaj\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKushk-e-Nosrat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHZF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParandak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKhar Rud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHasan Abad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoltaniyeh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIpak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eMagnitude calculated based on: (1) (Mohajer-Ashjai and Nowroozi, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), (2) Ambraseys, N. N. \u0026amp; Melville, C. P. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), (3) Nowroozi, A. A. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) and (4) Wells, D. L. \u0026amp; Coppersmith, K. J. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) equations. Intensity calculated based on: Ambraseys, N. N. \u0026amp; Melville, C. P. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1982\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eFault length: Values of actual lengths may be larger than those shown in this table. Collective lengths of subparallel branches and segments are not considered. \u0026ldquo;\u0026gt;\u0026rdquo; Is used for long faults (more than 100 Km in length) whose exact length is not established. For faults longer than 200 km, only 100 km of fault length has been considered. The total length of fault is used for faults up to 20 km long. For faults between 20 to 200 km long, respectively, 100\u0026ndash;50% of the total lengths has been considered.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the Gutenberg\u0026ndash;Richter model, the seismicity parameter a represents the overall level of seismic activity (i.e., the expected number of events for M\u0026thinsp;=\u0026thinsp;0), while the b-value, typically close to 1, is a tectonic indicator reflecting the relative occurrence of large versus small earthquakes. For the study area, the dataset was partitioned into two subsets: historical records (855\u0026ndash;1900) and instrumental records (1900\u0026ndash;2025), to develop the Gutenberg\u0026ndash;Richter relationship (Gutenberg and Richter, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1950\u003c/span\u003e; Gutenberg and Richter, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1956\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the temporal distribution of both historical and instrumental earthquake records, along with the corresponding magnitude\u0026ndash;frequency distribution, where M denotes the earthquake magnitude. The graph plots the logarithmic cumulative number of events per year for magnitudes M\u0026thinsp;\u0026gt;\u0026thinsp;5.5, following the method outlined by Krinitzsky (Krinitzsky, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA least-squares regression line was fitted across the entire magnitude range, resulting in the following equation:\u003c/p\u003e\u003cp\u003elog N\u0026thinsp;=\u0026thinsp;4.51\u0026thinsp;\u0026minus;\u0026thinsp;0.58M\u003c/p\u003e\u003cp\u003eBased on this regression, the derived seismicity parameters are:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ea\u0026thinsp;=\u0026thinsp;4.51\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eb\u0026thinsp;=\u0026thinsp;0.58\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003ewith a high correlation coefficient of R\u0026thinsp;=\u0026thinsp;0.98 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Shear Wave Velocity\u003c/h2\u003e\u003cp\u003eTo assess the spatial distribution of shear wave velocity (Vs) within the study area, a comprehensive dataset was compiled from existing geotechnical investigations. All available projects conducted in the region for Vs measurement were considered, including downhole tests and Standard Penetration Tests (SPTs). The collected data were subsequently filtered, quality-controlled, and georeferenced using ArcGIS to generate a spatial distribution map of Vs. In total, 133 downhole tests and 23 SPTs were conducted across the study area, with their geographic locations shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Empirical correlations established by Ishihara (Ishihara, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and Molnar et al. (Molnar et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) were employed to convert SPT values into equivalent shear wave velocities. To compensate for spatial data gaps in direct Vs measurements, two supplementary datasets were utilized: the geological map of Hamedan and the global Vs30 model developed by Allen and Wald (Allen and Wald, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In the geological map, lithologic units older than the Quaternary period (\u0026gt;\u0026thinsp;2.5\u0026nbsp;million years) were classified as bedrock. Quaternary units were further differentiated based on age and lithological composition. Vs values obtained from site-specific geotechnical investigations (downhole and borehole data) were then assigned to the corresponding geologic units. In the global Vs30 dataset, topographic slope is used as a proxy for Vs, under the assumption that steeper slopes correspond to higher compaction and thus higher shear wave velocities, whereas flatter terrain indicates looser, more compressible soils with lower Vs. All input datasets including downhole test results, SPT-derived estimates, geological unit classifications, and the global Vs30 model were integrated using a hierarchical weighting approach to produce the final composite Vs map (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Riverbed areas exhibited the lowest Vs values, while mountainous regions displayed the highest. Within the urban area of Hamedan, Vs ranged from approximately 180 m/s in low-lying zones to over 1800 m/s in the southern highlands. This final Vs map was used as a primary input in ground motion prediction equations in the subsequent seismic hazard analyses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Attenuation Equations\u003c/h2\u003e\u003cp\u003eThe selection of suitable ground motion prediction equations (GMPEs) is a critical element in any seismic hazard assessment. In this study, the GMPEs developed under the NGA-West program by the Pacific Earthquake Engineering Research Center (PEER), originally intended for western United States tectonic conditions, were adopted due to their demonstrated compatibility with the tectonic setting of the study area (Shoja\u0026ndash;Taheri et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Accordingly, a set of Next Generation Attenuation (NGA) relationships was used for statistical analyses. These included models developed by Abrahamson et al. (Abrahamson et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Boore et al. (Boore et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Campbell and Bozorgnia (Campbell and Bozorgnia, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and Chiou and Youngs (Chiou and Youngs, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Probabilistic Seismic Hazard Assessment (PSHA)\u003c/h2\u003e\u003cp\u003eBased on standard design-level exceedance probabilities or their corresponding return periods the following seismic design levels were considered:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eDesign Basis Level (DBL): 475-year return period (20% probability of exceedance in 100 years),\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMaximum Design Level (MDL): 975-year return period (10% in 100 years),\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMaximum Considered Level (MCL): 2475-year return period (2% in 100 years).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eFor each return period, peak horizontal ground acceleration (PGA) values were estimated at the 84th percentile confidence level.\u003c/p\u003e\u003cp\u003eConventional seismic hazard zoning studies frequently assume a uniform shear wave velocity typically 760 m/s throughout the study area. Under such an assumption, ground motion contour maps exhibit smooth gradients that diminish with increasing distance from seismic sources. In contrast, the present study conducted a comparative assessment between the traditional uniform-Vs approach and one incorporating the spatially resolved Vs distribution map developed in Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e. To implement this comparison, a grid comprising approximately 700 points was defined across the study area. Higher point densities were assigned to regions with high variability in shear wave velocity, particularly in the alluvial plains. Seismic ground motion calculations were then performed for each grid point. In the first scenario, a constant Vs of 760 m/s representative of reference rock conditions was applied across the entire region. Ground motion simulations were conducted using EZ-FRISK software, employing NGA-based attenuation models. The second scenario integrated the spatially variable Vs map. For both cases, seismic hazard maps were generated for horizontal ground acceleration at a spectral acceleration period of SA\u0026thinsp;=\u0026thinsp;0.2 seconds, corresponding to return periods of 475, 975, and 2475 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Overall, the results demonstrate that ground motion attenuation generally follows an expected decline with increasing distance from seismic sources, except in areas where shear wave velocity (Vs) exhibits significant reductions. For the 475-year return period, the estimated peak ground acceleration (PGA) in Hamedan ranged from 0.15 to 0.20 g under the assumption of a uniform Vs. When incorporating the spatially variable Vs map, this range increased to 0.25 to 0.30 g, representing a 33% increase.\u003c/p\u003e\u003cp\u003eA similar amplification trend was observed for longer return periods:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e975-year return period: PGA increased from 0.20\u0026ndash;0.25 g to 0.35\u0026ndash;0.40 g, indicating a 60% increase.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e2475-year return period: PGA increased from 0.25\u0026ndash;0.30 g to 0.45\u0026ndash;0.50 g, yielding a 70% increase.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese results underscore the pivotal role of spatial shear wave velocity variability in influencing ground motion estimations and highlight the necessity of incorporating site-specific geotechnical conditions into probabilistic seismic hazard assessments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Magnitude\u0026ndash;Distance Deaggregation (MDD)\u003c/h2\u003e\u003cp\u003eAs previously discussed, the influence of distant seismic sources becomes more pronounced at longer return periods. To quantify this effect, it is essential to disaggregate the total ground motion hazard by seismic source, magnitude, and distance. The most effective approach for this purpose is the magnitude\u0026ndash;distance\u0026ndash;epsilon (M\u0026ndash;R\u0026ndash;ε) deaggregation method, which enables identification of dominant sources and comparative evaluation of their respective contributions. Accordingly, deaggregation plots were generated for return periods of 475, 975, and 2475 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Since ground motion contributions from sources within 50 km are negligible, the plots begin at this threshold, confirming the absence of near-fault effects in the hazard estimates. As illustrated, two principal seismic sources dominate the ground acceleration at the site:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eNahavand Fault \u0026ndash; approximate magnitude Mw 7.0, located\u0026thinsp;~\u0026thinsp;60 km from Hamedan\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMorvarid Fault \u0026ndash; approximate magnitude Mw 6.8, located\u0026thinsp;~\u0026thinsp;102 km from Hamedan\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eBoth faults are recognized branches of the active Zagros Fault System.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e examines the relationship between epsilon (ε) and the probability of occurrence. As the return period increases, epsilon values exhibit a clear upward trend approximately 18% for the 975-year period and about 24% for the 2475-year period. The figure also presents the relative contribution of individual seismic sources to total hazard. Among them, the Nahavand Fault dominates, contributing 84%, 86%, and 89% of the total seismic hazard for return periods of 475, 975, and 2475 years, respectively. Additionally, epsilon values for each source are plotted as a function of distance from Hamedan. Across all return periods, epsilon increases with distance. A consistent increase in epsilon is also observed with increasing return periods.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe study area encompasses the city of Hamedan and its adjacent regions. Given the presence of vital socio-economic infrastructure, detailed analysis of seismicity parameters and probabilistic seismic hazard is of paramount importance. In this study, a probabilistic spectral acceleration zoning and seismic hazard disaggregation analysis was conducted. After completing a regional geological assessment, the tectonic framework and seismicity of the area were evaluated. A seismotectonic model was then developed to better define the seismic behavior of the region. A shear wave velocity (Vs) map for the upper 30 meters of the subsurface was generated using existing geotechnical test data.\u003c/p\u003e\u003cp\u003eGround motion probabilistic analysis was conducted using two approaches:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIn the first approach aligned with conventional national practices a constant Vs of 760 m/s was assumed across the study area.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIn the second approach, the detailed Vs map was directly incorporated into the analysis framework.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eSeismic hazard disaggregation allowed for the identification of the dominant hazard scenario in terms of earthquake magnitude, causative fault, and distance to the city of Hamedan. Seismicity parameters were computed at the center of Hamedan and assigned to each seismic source appropriately, considering its tectonic context and seismic potential. Based on this analysis, and accounting for the exceedance probabilities (or equivalent return periods) corresponding to DBL, MDL, and MCL design levels, the maximum horizontal ground acceleration at the 84th percentile was estimated across the study area. In the initial analysis phase, the assumption of a uniform Vs value (760 m/s) was applied. Ground motion calculations were carried out in EZ-FRISK using four NGA ground motion prediction models, and seismic hazard maps were developed for spectral acceleration at a period of 0.2 seconds.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eGround acceleration generally decreases with increasing distance from seismic sources except in cases where shear wave velocity (Vs) exhibits significant reductions. When a uniform Vs value is assumed across the region, this attenuation pattern remains relatively smooth and predictable. However, the integration of a spatially variable Vs map into the analysis reveals markedly different behavior. Specifically, ground acceleration values show notable amplification in zones characterized by low Vs values, such as alluvial plains, riverbeds, and areas underlain by soft sediments. The regions surrounding the Alvasjerd, Abshineh, and Bahari rivers demonstrate clear evidence of seismic wave amplification due to local site effects. Incorporating the spatially detailed Vs map into seismic hazard analysis leads to substantial increases in ground motion estimates. This increase becomes more pronounced for longer seismic return periods. These findings underscore not only the direct influence of low Vs zones on seismic amplification, but also the enhanced contribution of distant seismic sources particularly the Nahavand Fault (average distance\u0026thinsp;~\u0026thinsp;60 km) to seismic hazard at extended return intervals. Magnitude\u0026ndash;distance\u0026ndash;epsilon (M\u0026ndash;R\u0026ndash;ε) deaggregation plots for return periods of 475, 975, and 2475 years confirm that seismic hazard in the Hamedan region is dominated by two principal sources:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe Nahavand Fault, with an estimated moment magnitude (Mw) of approximately 7.0 at a distance of ~\u0026thinsp;60 km\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe Morvarid Fault, with an Mw of approximately 6.8 at a distance of ~\u0026thinsp;102 km\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eDespite their relatively long distances from the urban core, these faults are capable of generating significant ground motions during large-magnitude events, with potential implications for the performance of mid- to high-rise structures. Additionally, the analysis shows that as the return period increases, epsilon values also increase, indicating greater uncertainty and the growing influence of distant sources in long-term hazard projections. Author Contributions: Hadi Aboutalebi and Mohammad Mehdi Deh Cheshmeh: formal analysis, fieldwork, methodology, investigation, and writing the first draft of the manuscript; Gholamreza Ghodrati Amiri and Seyed Ali Razavian Amraei: fieldwork, conceptualization and design of the study, review, and editing. All authors have read and agreed to the published version of the manuscript. All authors contributed to the research and preparation of the manuscript and approved the submitted version.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no external funding\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e Not applicable\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Not applicable\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e Microseismicity data from https://www.bhrc.ac.ir/en .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e This article is extracted from the PhD thesis of the first author.The authors extend their gratitude to Hamedan Municipality for Downhole data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eHadi Aboutalebi: Conducted the geotechnical data collection, shear wave velocity modeling, and contributed to seismic hazard analysis. Drafted the initial manuscript and prepared visual materials (maps and charts).Mohamad Mohamadi Dehcheshmeh: Conceived and designed the overall research framework. Performed the probabilistic seismic hazard analysis and disaggregation modeling. Reviewed and edited the manuscript. Corresponding author.Gholamreza Ghodrati Amiri: Provided technical supervision and critical feedback on seismic hazard methodology. Validated the analytical models and revised the final manuscript for theoretical accuracy.Seyed Ali Razavian Amrei: Advised on structural seismic implications and regional geological interpretations. Participated in manuscript review and final approval of the version to be submitted.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbaszadeh, A., Esfandiyari, B. and Hamzeloo, H., 2011. Evaluation of a nonlinear seismic geotechnical site response analysis method subjected to earthquake vibrations (case study: Kerman Province, Iran). 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New Empirical Relationships among Magnitude, Rupture Length, Rupture Width, Rupture Area and Surface Displacement. Bulletin of the Seismological Society of America, 84(4): 974-1002.\u003c/li\u003e\n\u003cli\u003eZafarani, H., Jafarian, Y., Eskandarinejad, A., Lashgari, A., Soghrat, M.R., Sharafi, H. and Afraz-e Haji-Saraei, M., 2020. Seismic hazard analysis and local site effect of the 2017 M w 7.3 Sarpol-e Zahab, Iran, earthquake. Natural Hazards, 103(2): 1783-1805.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Shear wave velocity, earthquake hazard analysis, seismic hazard disaggregation, Hamedan, distant seismic sources","lastPublishedDoi":"10.21203/rs.3.rs-7261086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7261086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe shear wave velocity (Vs) is a key factor in influencing ground motion intensity and amplifying seismic waves. The city of Hamedan, due to its geological position atop alluvial deposits, exhibits a wide variation in Vs values. This heterogeneity may result in considerable structural damage during major seismic events. This study, which aims to evaluate site-specific effects on ground motion, developed a shear wave velocity map using existing subsurface geotechnical investigations. A probabilistic approach was employed for seismic hazard assessment. The results show that lower-Vs zones exhibit elevated seismic accelerations. Disaggregation analysis identifies the Nahavand and Morvarid faults as major contributors to seismic hazard in Hamedan, particularly under long return periods. The influence of distant seismic sources is evident and should be considered in the seismic design process of tall buildings.\u003c/p\u003e","manuscriptTitle":"Probabilistic seismic hazard zonation to investigate the effect of soil condition on earthquake acceleration propagation, Case study Hamadan City, Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 08:55:44","doi":"10.21203/rs.3.rs-7261086/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"40d2aa07-fefc-4fb1-ab3a-9eebd5702088","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T15:08:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 08:55:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7261086","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7261086","identity":"rs-7261086","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}