Site classification of locations of installed sensors in the Kumaon Region of the Himalayas using empirical approaches

Abstract

Seismic site classification not only is crucial for seismic hazard assessment but also influences the reliability of ground motion data. The present study classifies 81 locations where Uttarakhand State Earthquake Early Warning System (UEEWS) seismic sensors are installed in the Kumaon region. The ground motion records of earthquakes occurring between 2019 and 2023 have been used as the dataset for this work. A winnowing approach has been applied to select good records from the dataset, and then, spectral acceleration (SA) and pseudo-spectral acceleration (PSA) have been derived for all the records. The horizontal-to-vertical spectral ratio (HVSR) curves have been created using SA and PSA. Four methods with the eight classification approaches have been applied to classify the sites. The first method uses the predominant period obtained from the average HVSR curve of the site and classifies it according to the standard schemes. In the second method, three approaches estimate the site classification index (SCI) by correlating the site’s HVSR curve with standard HVSR curves. In the third method, time-averaged shear wave velocity (V_s30) from the depth of 30 m to the surface of the earth, is estimated using two different empirical models, while in the fourth method, PSA is normalized by peak ground acceleration (PGA). The results from all the approaches have been thoroughly examined and the final classification has been made by comparing them with the standard curves. Out of 81 sites, 31, 23, 1, 1, 6, 2, and 17 have been classified as classes I, II, III, IV, V, VI, and VII, respectively. The description of site categories has been explained in the subsequent sections. It has also been illustrated that the earthquake’s magnitude, epicentral distance, and depth do not affect the predominant period of the sites. The classification of sites plays a crucial role in advancing seismic hazard investigations of the Uttarakhand region, as strong ground motion records are the primary input along with the site’s conditions. This study will be valuable in helping to mitigate potential earthquake damages in the future.