The investigation of passive seismic ambient noise is a low-cost and effective strategy for studying the subsurface structure. Microtremors are often collected in a single-station configuration and processed according to Nakamura’s method, also known as the Horizontal-to-Vertical Spectral Ratio (HVSR). This approach allows one to compute spectral ratios curves from which the resonance frequency of the investigated site(s) and to infer information on subsurface lithology; the latter aspect is achieved by inverting the HVSR curve to obtain the local 1D subsurface shear-wave velocity distribution. The workflow for computing HVSR curves is now well established, and several software tools, both for processing and inversion are available. However, while simple in principle the workflow connecting data to subsurface model involves several subjective choices that are often left to the operator’s expertise rather than being guided by a well-defined, rational procedure. Additionally, despite the fact that the HVSR method is based on a simplified one-dimensional, flat subsurface model consisting of two main units; a low-velocity layer stack associated with soft sedimentary materials overlying a high-velocity bedrock; it is not uncommon to encounter data collected in areas characterized by significant topography or, more generally, suboptimal acquisition conditions. While best-practice criteria do exist, e.g. SESAME project, checking for the fulfillment of such criteria may become time consuming, especially for datasets containing numerous measurements over a large area. In addition, while inversion codes have become more powerful and flexible, the limitations of the method have become more evident, especially in terms of uniqueness of the output subsurface model and uncertainty of the estimatie. Building on these considerations, and upon the framework provided by the open-source software OpenHVSR Processing Toolkit and OpenHVSR Inversion, which I have been developing since 2016, I have designed and discuss here a set of new tools aimed at improving the HVSR processing beyond the classical workflow.
Revisiting HVSR Processing and Inversion: limitations, challenges, and new methodological tools
Bignardi
2026-01-01
Abstract
The investigation of passive seismic ambient noise is a low-cost and effective strategy for studying the subsurface structure. Microtremors are often collected in a single-station configuration and processed according to Nakamura’s method, also known as the Horizontal-to-Vertical Spectral Ratio (HVSR). This approach allows one to compute spectral ratios curves from which the resonance frequency of the investigated site(s) and to infer information on subsurface lithology; the latter aspect is achieved by inverting the HVSR curve to obtain the local 1D subsurface shear-wave velocity distribution. The workflow for computing HVSR curves is now well established, and several software tools, both for processing and inversion are available. However, while simple in principle the workflow connecting data to subsurface model involves several subjective choices that are often left to the operator’s expertise rather than being guided by a well-defined, rational procedure. Additionally, despite the fact that the HVSR method is based on a simplified one-dimensional, flat subsurface model consisting of two main units; a low-velocity layer stack associated with soft sedimentary materials overlying a high-velocity bedrock; it is not uncommon to encounter data collected in areas characterized by significant topography or, more generally, suboptimal acquisition conditions. While best-practice criteria do exist, e.g. SESAME project, checking for the fulfillment of such criteria may become time consuming, especially for datasets containing numerous measurements over a large area. In addition, while inversion codes have become more powerful and flexible, the limitations of the method have become more evident, especially in terms of uniqueness of the output subsurface model and uncertainty of the estimatie. Building on these considerations, and upon the framework provided by the open-source software OpenHVSR Processing Toolkit and OpenHVSR Inversion, which I have been developing since 2016, I have designed and discuss here a set of new tools aimed at improving the HVSR processing beyond the classical workflow.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
