Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records

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  • Tomohisa Okazaki
    RIKEN Center for Advanced Intelligence Project, Seika, Kyoto, Japan
  • Nobuyuki Morikawa
    National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Ibaraki, Japan
  • Asako Iwaki
    National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Ibaraki, Japan
  • Hiroyuki Fujiwara
    National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Ibaraki, Japan
  • Tomoharu Iwata
    NTT Communication Science Laboratories, Seika, Kyoto, Japan
  • Naonori Ueda
    RIKEN Center for Advanced Intelligence Project, Seika, Kyoto, Japan

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<jats:title>ABSTRACT</jats:title><jats:p>Choosing the method for inputting site conditions is critical in reducing the uncertainty of empirical ground-motion models (GMMs). We apply a neural network (NN) to construct a GMM of peak ground acceleration that extracts site properties from ground-motion data instead of referring to ground condition variables given for each site. A key structure of the model is one-hot representations of the site ID, that is, specifying the collection site of each ground-motion record by preparing input variables corresponding to all observation sites. This representation makes the best use of the flexibility of NN to obtain site-specific properties while avoiding overfitting at sites where a small number of strong motions have been recorded. The proposed model exhibits accurate and robust estimations among several compared models in different aspects, including data-poor sites and strong motions from large earthquakes. This model is expected to derive a single-station sigma that evaluates the residual uncertainty under the specification of estimation sites. The proposed NN structure of one-hot representations would serve as a standard ingredient for constructing site-specific GMMs in general regions.</jats:p>

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