@article{LilienkampvonSpechtWeatherilletal.2022,
  author    = {Lilienkamp, Henning and von Specht, Sebastian and Weatherill, Graeme and Caire, Giuseppe and Cotton, Fabrice},
  title     = {Ground-Motion modeling as an image processing task},
  series = {Bulletin of the Seismological Society of America},
  volume    = {112},
  journal   = {Bulletin of the Seismological Society of America},
  number    = {3},
  publisher = {Seismological Society of America},
  address   = {Albany},
  issn      = {0037-1106},
  doi       = {10.1785/0120220008},
  pages     = {1565 -- 1582},
  year      = {2022},
  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.},
  language  = {en}
}