Basin effect quantitation of four deep basins in Japan to explain the gap between empirical and numerical basin‐depth effect models

Abstract

To incorporate basin effects into seismic hazard assessment of urban areas and infrastructure, basin-depth effect models that are the dependence of basin amplification factor on basin depth have been derived via numerical and empirical approaches. However, commonly observed quantitative and qualitative differences between numerical and empirical models remain unresolved. In particular, empirical models tend to predict smaller basin amplification factors. In this study, a modified empirical approach from Choi et al. was applied to the derivation of basin-depth effect models for four deep basins in Japan using a database consisting of 71 M J > 6 (M w > 5.7) earthquakes and 13,562 records from strong-motion seismograph networks (K-NET and KiK-net). The obtained basin-depth models vary among basins and crustal-subduction earthquake pairs, which confirms the recent trend of accounting for regional-seismotectonic basin amplification in seismic hazard analysis. The basin amplification observed for crustal earthquakes is generally larger than that for subduction earthquakes.Moreover, the differences between the numerical and empirical models can be explained by changing the options of basin or outside-basin stations for computing the inter-event residuals. These results bridge a knowledge gap in seismic hazard analysis between ground-motion prediction equations and those using large-scale seismic wave simulations, which are now the main tools for regions lacking seismic records.

Multi‐input integrative neural network for soil seismic response modeling at KiK‐net downhole array sites

“…With the densification of seismic observation networks, such as Kyoshin Network (K-NET) and Kiban Kyoshin Network (KiK-net) in Japan, Garner Valley Downhole Array (GVDA) and Wildlife Liquefaction Array (WLA) in the United States, and Lotung Downhole Array in Taiwan, China, a substantially increased amount of data is available for soil seismic response research. [19][20][21] The availability of abundant recorded seismic data enables the application of data-driven methods in earthquake engineering. In contrast to physics-based methods, data-driven methods can be implemented without any assumptions or prior knowledge.…”