Abstract

The Japanese KiK-net network comprises about 700 stations spread across the whole territory of Japan. For most of the stations, VP and VS profiles were measured down to the bottom borehole station. Using the vast dataset of earthquake recordings from 1997 to 2020 at a subset of 428 seismic stations, we compute the horizontal-to-vertical spectral ratio of earthquake coda, the S-wave surface-to-borehole spectral ratio, and the equivalent outcropping S-wave amplification function. The de facto equivalence of the horizontal-to-vertical spectral ratio of earthquake coda and ambient vibration is assessed on a homologous Swiss dataset. Based on that, we applied the canonical correlation analysis between amplification information and the horizontal-to-vertical spectral ratio of earthquake coda across all KiK-net sites. The aim of the correlation is to test a strategy to predict local earthquake amplification basing the inference on site condition indicators and single-station ambient vibration recordings. Once the correlation between frequency-dependent amplification factors and amplitudes of horizontal-to-vertical coda spectral ratios is defined, we predict amplification at each site in the selected KiK-net dataset with a leave-one-out cross-validation approach. In particular, for each site, three rounds of predictions are performed, using as prediction target the surface-to-borehole spectral ratio, the equivalent of a standard spectral ratio referred to the local bedrock and to a common Japanese reference rock profile. From our analysis, the most effective prediction is obtained when standard spectral ratios referred to local bedrock and the horizontal-to-vertical spectral ratio of earthquake coda are used, whereas a strong mismatch is obtained when standard spectral ratios are referred to a common reference. We ascribe this effect to the fact that, differently from amplification functions referred to a common reference, horizontal-to-vertical spectral ratios are fully site-dependent and then their peak amplitude is influenced by the local velocity contrast between bedrock and overlying sediments. Therefore, to reduce this discrepancy, we add in the canonical correlation as a site proxy the inferred velocity of the bedrock, which improves the final prediction.

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