Characterizing Egyptian National Seismic Network station sites using genetic optimization for microtremor data inversion

Abstract

Precise site response characterization is essential for understanding lithostratigraphic subsurface properties, seismic site effects, and soil classification within seismic networks. This study addresses the challenge of limited shear-wave velocity data within the upper 30 meters (\(V_{S30}\)) at 39 stations of the Egyptian National Seismic Network (ENSN) in Northern Egypt. We employ the inversion of Horizontal-to-Vertical Spectral Ratio (HVSR) data from single-station ambient noise using an Elitist Genetic Algorithm (EGA) to estimate the shear-wave velocity profile at each station. This algorithm uses an equivalent linear approach based on the viscoelastic Kelvin-Voigt model to compute the theoretical site response of horizontally stratified soil layers. Inversion results from Multichannel Analysis of Surface Waves (MASW) conducted at five ENSN stations were incorporated to refine the input inversion parameters and control the genetic HVSR inversion outcomes. This approach effectively demonstrates the HVSR method’s ability to detect variations in the shear-wave velocity structure with depth and determine the average shear-wave velocity in the upper 30 meters. The obtained site-specific amplification data contributes to a more detailed understanding of site conditions, enabling precise determination of site classification and characterization factors. This facilitates refined Peak Ground Acceleration (PGA) estimations, thereby substantially enhancing the robustness of future seismic hazard assessments in Egypt.