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The horizontal-to-vertical spectral ratio (HVSR) has seen widespread use as a tool for measuring a site's resonant behavior. However, the processing of HVSR with traditional methods can be tedious and time-consuming when the HVSR is complex and exhibits multiple resonances. This work proposes the AutoHVSR algorithm that allows for fully-automated processing of HVSR measurements, including those with zero, one, or multiple clear resonances. The AutoHVSR algorithm accepts microtremor or earthquake recordings and user-defined HVSR processing parameters as input and returns the computed HVSR from each time window or earthquake record, the mean/median HVSR curve, and statistics on each automatically identified HVSR resonance in terms of frequency and amplitude. The AutoHVSR algorithm integrates robust signal processing and computational methods with state-of-the-art machine-learning models trained using a diverse dataset of 1108 HVSR measurements. The AutoHVSR algorithm demonstrates excellent performance by correctly determining the number of HVSR resonances for 1098 of the 1108 HVSR measurements (>99%) and predicting the mean resonant frequency of the correctly identified resonances with a root mean square error (RMSE) of 0.05 Hz. Furthermore, the AutoHVSR algorithm was able to produce these predictions in 13 min (including HVSR processing time) compared to the 30 h required for traditional processing (a speed up of 138). The AutoHVSR algorithm is further demonstrated on a challenging dataset from Canterbury, New Zealand that included many HVSR curves with multiple and/or ambiguous resonances. Despite the challenging nature of the Canterbury dataset, the AutoHVSR algorithm was capable of correctly determining the number of HVSR resonances for 113 of the 129 HVSR measurements (>87%) and predicting the mean resonant frequency of the correctly identified resonances with a RMSE of 0.10 Hz. The AutoHVSR algorithm produced these predictions under 2 min (including HVSR processing time) compared to the 4 h required for traditional processing (a speed up of 120). Finally, while the AutoHVSR algorithm was developed using microtremor measurements where the horizontal components were combined using the geometric mean, it is shown to extend without modification to microtremor HVSR measurements where the two horizontal components are rotated azimuthally and to HVSR measurements from earthquake recordings. The AutoHVSR algorithm has been made publicly available in v0.3.0 of HVSRweb, it can be accessed at https://hvsrweb.designsafe-ci.org/. • The AutoHVSR algorithm is proposed for fully-automating HVSR processing. • AutoHVSR can process HVSR with zero, one, or multiple clear resonances. • The predictions of AutoHVSR are fast, reliable, and reproducible. • AutoHVSR can be applied broadly including azimuthally-rotated and earthquake HVSR. • AutoHVSR has been made publicly accessible through HVSRweb. [ABSTRACT FROM AUTHOR]

This paper presents the development of depth- and V s 30 -dependent parametric velocity and spatial correlation models to characterise shear-wave velocities within the geologic layers of the Canterbury New Zealand sedimentary basin. The models utilise data from 22 shear-wave velocity profiles of up to 2.5 k m depth, derived from surface wave analysis, juxtaposed with models which detail the three-dimensional structure of the geologic formations in the Canterbury sedimentary basin. Parametric velocity equations are presented for Fine Grained Sediments, Gravels, and Tertiary layer groupings. Spatial correlations were developed and applied to generate three-dimensional stochastic velocity perturbations. Dispersion curves for the stochastic models and observed velocity profiles show good agreement over a wide frequency range with the dispersion data underlying the velocity profiles used as input data, indicating that the parametric perturbed velocity profiles replicate the dispersion characteristics of the observed velocity profiles. Collectively, these models enable seismic velocities to be realistically represented for applications such as 3D ground motion and site response simulations. • Parametric functions to prescribe velocities in Canterbury sedimentary deposits. • Geostatistics are used to develop spatial correlations to add velocity perturbations to parametric models. • Velocity modelling for use in ground motion and site response simulations. [ABSTRACT FROM AUTHOR]