## 1 Introduction  
Vs30, defined as the average seismic shear-wave velocity from the surface to a depth of 30 m, is a fundamental parameter used in seismic hazard analysis. Vs30 measurements obtained at strong motion stations worldwide serve as a basis for developing ground motion prediction equations [1]. These equations incorporate site classifications based on Vs30, such as those outlined in NSCP 2015 [2], to account for site amplification in modeling peak ground motions. Various strategies exist for mapping Vs30, ranging from empirical correlations to direct geophysical measurements. A widely used proxy-based method is the topographic slope approach proposed by [PERSON] and [PERSON] (2007) [3], which correlates topographic gradients with shallow shear-wave velocity for rapid Vs30 estimations. However, this method cannot distinguish sharp Vs30 contrasts. Integrating geological or geomorphological data with slope information can improve Vs30 mapping accuracy, particularly in regions with extensive Vs30 measurements for validation [4]. For more detailed Vs30 mapping, local in situ measurements remain crucial. Commonly used invasive techniques for measuring Vs30 include borehole methods (down-hole, cross-hole, up-hole) where empirical correlations between Standard Penetration Resistance (SPT-N) and shear wave velocity (Vs) have been developed [5; 6; 7; 8; 9]. Although highly accurate, borehole methods require site drilling, making them costly and logistically challenging. As practical alternatives, non-invasive geophysical methods for Vs30 measurement such as seismic refraction [10; 11], Multichannel Analysis of Surface Waves (MASW) [12; 13], Spectral Analysis of Surface Waves (SASW) [14], and Refraction Microtremor (ReMi) [15] are widely used. The ReMi method, in particular, employs an array of geophones to record seismic waves and generate Vs profiles. ReMi surveys, which typically last 1-2 h, require a low-gradient area and an array length of 60 to 120 m to obtain reliable Vs30 measurements [16]. While effective, these methods demand significant field effort, particularly for large-scale Vs30 mapping. A key limitation is a reliance on grid-based microzonation [17; 18], which requires at least one measurement point per grid cell. While this approach allows for detailed site characterization, it results in extensive fieldwork, making data acquisition time-consuming and labor-intensive. To address these challenges, alternative techniques such as the inversion of the Horizontal-to-Vertical Spectral Ratio (HVSR) are being explored. Originally proposed by [PERSON] (1989) [19] for site effect research, the HVSR method analyzes ambient seismic noise recorded using a single-station three-component microtremor seismograph to estimate a site's fundamental resonance frequency. [PERSON] and [PERSON] (1998) [20] refined the method by introducing a smoothing function to mitigate sharp fluctuations in HVSR curves. Earlier studies have demonstrated the potential of HVSR inversion for Vs profile generation and Vs30 estimation, requiring only 30 min of recording time by a single operator [21]. Automated inversion methods have been developed to improve efficiency, such as the hybrid simulated annealing algorithm applied by [PERSON] and [PERSON] (2012) [22] in Greater Metro Manila and the neighborhood algorithm used by [PERSON] et al. (2023) [23] for Vs30 estimation in West Sulawesi and South Sulawesi Regions. In this study, HVSR data from ambient noise measurements conducted in the Greater Metro Manila Area (GMMA) and the province of Leyte in the Philippines were inverted using an elitist genetic algorithm (GA) implemented in the software HVSRInv version 1.0 developed by [PERSON] and [PERSON] (2022) [24]. The GA optimizes the inversion process by simultaneously exploring multiple parameter sets and selecting the best-fit model based on specified criteria. Unlike previous studies on HVSR inversion that relied on generic soil models, our approach calibrates inversion parameters using site-specific ReMi data, improving Vs30 accuracy.