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Abstract:
We construct and examine the prototype of a deep learning-based ground-motion model
(GMM) that is both fully data driven and nonergodic. We formulate ground-motion modeling
as an image processing task, in which a specific type of neural network, the U-Net,
relates continuous, horizontal maps of earthquake predictive parameters to sparse observations
of a ground-motion intensity measure (IM). The processing of map-shaped data
allows the natural incorporation of absolute earthquake source and observation site coordinates,
and is, therefore, well suited to include site-, source-, and path-specific amplification
effects in a nonergodic GMM. Data-driven interpolation of the IM between
observation points is an inherent feature of the U-Net and requires no a priori assumptions.
We evaluate our model using both a synthetic dataset and a subset of observations
from the KiK-net strong motion network in the Kanto basin in Japan. We find that the
U-Net model is capable of learning the magnitude–distance scaling, as well as site-,
source-, and path-specific amplification effects from a strong motion dataset. The interpolation
scheme is evaluated using a fivefold cross validation and is found to provide on
average unbiased predictions. The magnitude–distance scaling as well as the site amplification
of response spectral acceleration at a period of 1 s obtained for the Kanto basin are
comparable to previous regional studies.
