{"_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"], "permalink_uri": "https://nied-repo.bosai.go.jp/records/3391", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2023-03-30"}, "publish_date": "2023-03-30", "publish_status": "0", "recid": "3391", "relation": {}, "relation_version_is_last": true, "title": ["Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records"], "weko_shared_id": -1}