{"created":"2023-03-30T09:30:39.631134+00:00","id":3391,"links":{},"metadata":{"_buckets":{"deposit":"8dcc24b9-6cca-4a29-9238-e334e43e945b"},"_deposit":{"id":"3391","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"3391"},"status":"published"},"_oai":{"id":"oai:nied-repo.bosai.go.jp:00003391","sets":[]},"author_link":[],"item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2021-08-01","bibliographicIssueDateType":"Issued"},"bibliographic_titles":[{"bibliographic_title":"Bulletin of the Seismological Society of America","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"ABSTRACT\n\n 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.","subitem_description_language":"en","subitem_description_type":"Other"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Seismological Society of America ({SSA})","subitem_publisher_language":"en"}]},"item_10001_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1785/0120200339"}}]},"item_10001_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1943-3573","subitem_source_identifier_type":"EISSN"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tomohisa Okazaki","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Nobuyuki Morikawa","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Asako Iwaki","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Hiroyuki Fujiwara","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Tomoharu Iwata","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Naonori Ueda","creatorNameLang":"en"}]}]},"item_title":"Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records","subitem_title_language":"en"}]},"item_type_id":"40001","owner":"1","path":["1670839190650"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-03-30"},"publish_date":"2023-03-30","publish_status":"0","recid":"3391","relation_version_is_last":true,"title":["Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-06-08T01:37:16.442808+00:00"}