Journal of Seismology and Earthquake Engineering
Volume:24 Issue: 1, Winter and Spring 2022

Seismic risk assessment is a necessary study to mitigate future earthquake damage in urban area. Jablah city is an old coastal city in Syria; it had been affected by many historical earthquakes, and it has not been studied seismically yet. The seismic database in National Earthquake Center in Syria was used to calculate seismic hazard maps of the study area. These maps indicate that the values of the ground acceleration are increasing towards the north and southeast of the Jablah city. Field work on soil shows that dominant period values range from 0.34 to 2.5 seconds, and amplification factor values from 1.1 to 3.7. The period of free vibration of the buildings varied from 0.09 to 0.45 seconds for the selected buildings. To assess the current status of the buildings, an evaluation form of vulnerability has been prepared. As a result, the vulnerability coefficient of the studied buildings to seismic forces ranged from 0.3 to 0.6. To obtain seismic risk maps, GIS program was used to process multiple layers such as seismic hazard map, dominant period, amplification factor of soil, free vibration period of buildings and assessment of their current status. The results show that many crowded neighborhoods in Jablah city are located in high seismic risk areas where tall and important buildings are located. This confirms that the high potential of human and economic losses concentrated in the city center.

To investigate the crustal structure of the northeast of Lut block and Eastern Iranian Ranges, we deployed 31 seismic stations along a 230 km profile for a period of approximately 9 months. Using the migration of P-wave receiver functions, we analyzed changes in the Moho depth along this profile. Our results indicate that the boundary between the crust and upper mantle is nearly flat, with a depth of approximately 40 km. Furthermore, we found that the thickness of the crust is relatively high beneath the Lut-Afghan Block boundaries and Bagheran Mountain, where receiver functions suggest the presence of more complex structures including inclined interfaces. Our findings suggest that the collision of Lut-Afghan blocks has not significantly increased the crustal thickness in the northeast Iran compared to the Zagros collision zone in the southwest of Iran. Rather, we propose that the observed shortening in this region is primarily due to the interaction of strike-slip motions, which can accommodate shortening through mechanisms such as rotation and wedge escaping.

NW-SE trending Shotori range has some structural elements such as folds and fractures (including thrust and strike-slip faults), following almost a similar trend. Based on the plotted stereograms, the predominant mechanism of the region’s faults is thrust with a dextral strike slip component which implies overcoming compressive stresses in the region. On the other hand the strike-slip horizontal component of the faults may also be due to the Nayband fault activities on Shotori and Esfandiar thrust zones. The steep dip of these faults may also be due to some normal faults converted to reverse faults as a result of stress phase change from tensional to compressional. Based on planar axis perpendicular compression or stretching axis σ1= 34, 10 and σ3= 296, 38 were obtained for the study area. striogram axial surface folds  dominantly show  NW-SE direction, which is indicates  compression axis which is equal N53, 12.Aeromagnetic data analysis revealed two magnetic lineaments with the same trends as surveyed lineaments in the field. The relatively high magnetic intensity of these lineaments could be due to basement faults in the region. With regards to obtained stereo plots, trend of the folds’ axis are parallel to the regional thrust faults and folds’ contour diagrams apply asymmetric folds with faulting in the area.

Earthquake of November 11, 2017 (Mw=7.3) in Ezgeleh village of Sarpol-e Zahab city in Iran, triggering numerous slope instabilities of various types of rockfalls, rockslides, avalanches and mud flows. The existence of a very high inherent potential and susceptibility of the region, on the one hand, and the occurrence of a strong earthquake as a driving factor, on the other hand, have been the main reasons for the instabilities in the region have been the main reasons for the instabilities in the region. The number of slope instabilities caused by this earthquake was rare compared to earthquakes with similar magnitude and thus requires more detailed investigations. The main objective of this study is to make a landslide hazard zonation map in this region, using Entropy Shannon’s model, and compare the results with the landslides that triggered by the Ezgeleh earthquake. Preparing a map can identify and distinct low-risk and high-risk areas regarding soil and rock slope instabilities. The earthquake showed that such a map can help significantly to the future construction activities in the region and mitigate the loss of lives and properties. The landslide conditioning factors such as Slope Angle, Lithology (geology), Geological Strength Index (GSI), Slope Aspect, Distance to Faults, Pick Ground Acceleration (PGA), Plan Curvature, Distance to Roads, Distance to Rivers, Land Use, Normalized Difference Vegetation Index (NDVI) and Topographic Wetness Index (TWI), were extracted from the spatial database. By using these factors, weights of each factor were analyzed by index of Entropy model and the map of landslide hazard zonation were prepared, using Geographical Information System (GIS). The results showed that more than 31.37% of the surface of the area has a moderate to very high hazard of landslides. From 335 landslides identified, 235 (≈ 70%) locations were used for the landslide susceptibility maps, while the remaining 100 (≈ 30%) cases were used for model validation. Finally, the ROC (receiver operating characteristic) curve for landslide hazard zonation map was drawn and the areas under the curve (AUC) were calculated. The verification results showed that the index of Entropy model (AUC = 84.3%) has a high accuracy that is assumed as very good.