Earthquakes pose significant threats to urban and rural infrastructure, necessitating detailed seismic hazard assessments. This study analyzes the Ground Motion Parameters of earthquakes, specifically Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV), in Sanandaj, Iran, a city located within the seismically active Zagros zone. Employing deterministic methods, the research integrates historical seismic data, active fault characteristics, and local geotechnical properties to estimate these parameters. The attenuation model by Chandra et al. (1979) was utilized to assess spatial variations in seismic intensity, while Geographic Information Systems (GIS) and the Inverse Distance Weighting (IDW) interpolation method facilitated seismic hazard zonation across an 18×15 km urban area. Results indicate that southwestern Sanandaj faces the highest seismic risk, with PGA values ranging from 0.34g to 0.36g and PGV between 35 and 39 cm/s, reflecting significant potential impacts on infrastructure. The study reveals a heterogeneous seismic hazard distribution, with southern and southwestern zones most vulnerable due to proximity to active faults. These findings exceed the baseline PGA of 0.3g outlined in Iran’s seismic design code (Standard 2800), underscoring the need for localized revisions to enhance urban resilience. The integration of GIS-based mapping highlights its efficacy in visualizing hazard patterns, aiding urban planning and disaster management. Recommendations include revising Standard 2800 with region-specific data and adopting probabilistic methods in future studies to improve accuracy. This research provides a critical foundation for mitigating seismic risks in Sanandaj, offering insights applicable to other seismically active regions in Iran.

Keywords: Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), Seismic Hazard Zonation, Sanandaj



Methodology and Results

Aims

This study aims to evaluate the Ground Motion Parameters of earthquakes—Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV)—in Sanandaj, Iran, to assess seismic hazard distribution and inform urban planning and infrastructure resilience. Located in the seismically active Zagros zone, Sanandaj’s proximity to over 40 active faults within a 70 km radius necessitates precise hazard analysis. The research seeks to: (1) estimate PGA and PGV using deterministic methods, (2) map seismic hazard zones across an 18×15 km urban area using GIS, and (3) compare findings with Iran’s seismic design code (Standard 2800) to propose enhancements for local applicability.

Procedure

The research proceeded in stages: (1) data compilation and fault selection, (2) magnitude estimation for each fault, (3) intensity calculation and attenuation modeling, (4) conversion to PGA and PGV, and (5) GIS-based zonation. Fault lengths were measured from geological maps, and magnitudes were averaged across multiple relationships. Intensity attenuation was calculated for each grid cell, followed by PGA and PGV estimation. GIS mapping visualized spatial trends, validated by low RMSE values from IDW interpolation.

A deterministic approach was adopted to estimate seismic parameters, leveraging historical and instrumental earthquake data, fault characteristics, and geotechnical conditions. Data were sourced from multiple authoritative repositories: historical seismicity from Mirzaei et al. (2002), instrumental records (2000–2020) from Iran’s National Seismological Center, fault maps from the Geological Survey of Iran (2019), and geotechnical data from the National Cartographic Center (2021). Active faults were selected based on criteria including a minimum length of 10 km, evidence of activity within the last 10,000 years, and proximity (<70 km) to Sanandaj, as per Iran’s Dam Construction Committee standards (2016).

Earthquake magnitude (M) was calculated using empirical relationships linking fault length (L) to magnitude, including Wells and Coppersmith (1994: M = 5.08 + 1.16log(L)), Nowroozi (1985: M = 4.86 + 1.32log(L)), and Ashjaei (1981: M = 4.5 + 1.5log(L)). A mean magnitude was derived to reduce uncertainty. Seismic intensity (Io) at the epicenter was estimated using Zare et al. (2009: Io = 1.3Ms + 0.09), followed by attenuation modeling with Chandra et al. (1979: IR = Io + 6.453 − 0.00121R − 4.960log(R+20)), where IR is intensity at a site and R is epicentral distance. PGA and PGV were then derived from intensity using log10(PGA) = 0.33MMI − 1.2 and log10(PGV) = 0.31MMI − 0.7, respectively.

The study area was discretized into a 1×1 km grid (270 cells), and GIS (ArcGIS 10.8) was employed for spatial analysis. Epicentral distances were computed using the Point Distance tool, and the IDW method (RMSE = 0.0248) interpolated point data into continuous hazard maps, chosen for its simplicity and accuracy in local variation modeling.

Results and Discussion

Results reveal a non-uniform seismic hazard distribution in Sanandaj. Southwestern zones exhibit the highest risk, with PGA ranging from 0.34g to 0.36g and PGV from 35 to 39 cm/s, corresponding to an intensity of VIII on the Modified Mercalli Intensity (MMI) scale. These values decline northward (PGA ~0.30g, PGV ~30 cm/s), reflecting increased distance from active faults concentrated in the southwest. The elevated PGA exceeds Standard 2800’s 0.3g baseline, suggesting that current design parameters may underestimate local risk. This discrepancy arises from the study’s detailed fault inventory (40 faults) compared to the code’s broader regional approach.

Comparisons with prior studies show consistency with Panahi and Motasharrei (2013) (PGA = 0.34g) and partial alignment with Zare et al. (2009) (0.25–0.35g), though Yazdani and Khaji (2015) report a lower 0.25g due to fewer seismic sources. The precision of GIS-based IDW interpolation (RMSE = 0.0248) enhances result reliability, highlighting southwestern Sanandaj’s vulnerability, particularly to infrastructure like water networks, where PGV strongly correlates with pipe failure (O’Rourke & Liu, 2012).

Conclusion

This study underscores Sanandaj’s heterogeneous seismic risk, with southwestern areas most threatened due to fault proximity. The findings advocate for revising Standard 2800 with localized data and integrating probabilistic methods in future research to refine hazard estimates. GIS-based zonation proves effective for urban planning, offering a replicable model for other Zagros cities.