Estimating S-wave velocity profiles from horizontal-to-vertical spectral ratios based on deep learning

• Published in: Soils and foundations Vol. 64; no. 6; p. 101525
• Main Authors: Hayashi, Koichi, Suzuki, Toru, Inazaki, Tomio, Konishi, Chisato, Suzuki, Haruhiko, Matsuyama, Hisanori
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2024
• Subjects: Horizontal-to-vertical spectral ratio; Inversion; Japan; Machine learning; Microtremor; S-wave velocity; Surface wave; S-wave velocity; Japan; Machine learning; Inversion; Surface wave; Microtremor; Horizontal-to-vertical spectral ratio
• ISSN: 0038-0806
• DOI: 10.1016/j.sandf.2024.101525

S-wave velocity (Vs) profile or time averaged Vs to 30 m depth (VS30) is indispensable information to estimate the local site amplification of ground motion from earthquakes. We use a horizontal-to-vertical spectral ratio (H/V) of seismic ambient noise to estimate the Vs profiles or VS30. The measurement of H/V is easier, compared to active surface wave methods (MASW) or microtremor array measurements (MAM). The inversion of H/V is non-unique and it is impossible to obtain unique Vs profiles. We apply deep learning to estimate the Vs profile from H/V together with other information including site coordinates, deep bedrock depths, and geomorphological classification. The pairs of H/V spectra (input layer) and Vs profiles (output layer) are used as training data. An input layer consists of an observed H/V spectrum, site coordinates, deep bedrock depths, and geomorphological classification, and an output layer is a velocity profile. We applied the method to the South Kanto Plain, Japan. We measured MASW, MAM and H/V at approximately 2300 sites. The pairs of H/V spectrum together with their coordinates, geomorphological classification etc. and Vs profile obtained from the inversion of dispersion curve and H/V, compose the training data. A trained neural network predicts Vs profiles from the observed H/V spectra with other information. Predicted Vs profiles and their VS30 are reasonably consistent with true Vs profiles and their VS30. The results implied that the deep learning could estimate Vs profile from H/V together with other information.