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The use of strongly biased data generally leads to large distortions in a trained machine learning model. We face this problem when constructing a predictor for earthquake-generated ground-motion intensity with machine learning. The machine learning predictor constructed in this study has an underestimation problem for strong motions, although the data fit on relatively weak ground motions is good. This underestimation problem is caused by the strong bias in available ground-motion records; there are few records of strong motions in the dataset. Therefore, we propose a hybrid approach of machine learning and conventional ground-motion prediction equation. This study demonstrates that this hybrid approach machine learning technology and physical model reduces the underestimation of strong motions and leads to better prediction than either of the individual approaches applied alone.

In earthquake hazard assessment studies, the focus is usually on horizontal ground motion. However, records from the 14 June 2008 Iwate-Miyagi earthquake in Japan, a crustal event with a moment magnitude of 6.9, revealed an unprecedented vertical surface acceleration of nearly four times gravity, more than twice its horizontal counterpart. The vertical acceleration was distinctly asymmetric; the waveform envelope was about 1.6 times as large in the upward direction as in the downward direction, which is not explained by existing models of the soil response. We present a simple model of a mass bouncing on a trampoline to account for this asymmetry and the large vertical amplitude. The finding of a hitherto-unknown mode of strong ground motion may prompt major progress in near-source shaking assessments.