Del Gaudio V.; Muscillo S.; Wasowski J., 2014, What we can learn about slope response to earthquakes from ambient noise analysis_ An overview, Engineering geology 182 (2014): 182–200. doi_10.1016/j.enggeo.2014.05.010,
URL: http://www.cnr.it/prodotto/i/307533

Earthquake induced slope failures are responsible for a significant amount of life loss and damage, and their effective mitigation requires further advancements in our comprehension of slope behaviour under seismic shaking. One source of uncertainty in seismic landslide susceptibility assessment is the phenomenon of enhanced amplification of ground motion along down slope directions. This implies a strength demand beyond that estimated by standard slope stability analysis. An extensive accelerometer monitoring of slope dynamic response in areas exposed to seismic landslide hazard is unfeasible. An alternative approach can take advantage of recent development of reconnaissance techniques based on the analysis of ambient noise recorded by portable instruments. The most popular technique, known as Nakamura or HVNR method, consists in analysing H/V spectral ratios between Horizontal and Vertical components of Noise Recording, and allows the recognition of site resonance frequencies. The application of HVNR to complex site conditions typical of marginally stable slopes is often difficult and requires the development of "ad hoc" procedures both for acquisition and analysis of noise recording. Tests in different geologic and geomorphic settings show that an analysis of azimuthal variation of spectral ratios can reveal the presence and orientation of directional resonance, whereas the recognition of main resonance frequencies requires a proper selection of signals to be analysed. Efforts to evaluate amplification factors currently rely on numerical simulations, which in turn require S-wave velocity of slope materials. Ambient noise analysis in terms of velocity models can contribute through the inversion of H/V spectral ratios and surface wave velocity dispersion curves derived from the processing of multiple simultaneous noise recordings. However these applications require a correct identification of the nature of surface waves present in the noise recording. © 2014 Elsevier B.V. All rights reserved.

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