{"_buckets": {"deposit": "8f7cf23f-2eb0-4ec7-8297-8f5fa997b55d"}, "_deposit": {"created_by": 7, "id": "6823", "owners": [7], "pid": {"revision_id": 0, "type": "depid", "value": "6823"}, "status": "published"}, "_oai": {"id": "oai:nied-repo.bosai.go.jp:00006823", "sets": []}, "author_link": [], "item_10001_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2024-10-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "5", "bibliographicPageEnd": "771", "bibliographicPageStart": "760", "bibliographicVolumeNumber": "19", "bibliographic_titles": [{"bibliographic_title": "Journal of Disaster Research", "bibliographic_titleLang": "en"}]}]}, "item_10001_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "S-net is a seafloor observation network for earthquakes and tsunamis around the Japan Trench, comprising 150 observatories with seismometers and pressure gauges. The region has been known to experience massive earthquakes, and several magnitude 6 and 7 class earthquakes have occurred after the network was established in 2016. This study constructed ground motion prediction equations (GMPEs) for horizontal peak ground accelerations (PGAs) and peak ground velocities (PGVs) using the S-net data and revealed that the GMPEs can be used to predict the PGAs and PGVs at the land stations where measured S-wave velocities are available. We used a relatively short time window of the S-net records from the viewpoint of earthquake early warning but included S waves. Data from earthquakes of magnitudes between Mw 5.5 and Mw 7.4 were used. The construction of the GMPEs was achieved in two steps. First, regression analysis was conducted for each event data, and mean site residual was obtained over the available records at each S-net site. Second, the data were adjusted by the mean site residuals, and stratified regression analysis, which decouples the source and path factors, was performed. Finally, we applied the GMPEs to predict PGAs and PGVs at the KiK-net sites on land. We determined that the residuals at the KiK-net sites were systematically biased with Vs30 (average S-wave velocity in the upper 30 m). We obtained correction factors for the bias and demonstrated that the PGAs and PGVs at the KiK-net sites could be predicted reasonably well.", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10001_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "Fuji Technology Press Ltd.", "subitem_publisher_language": "en"}]}, "item_10001_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "10.20965/jdr.2024.p0760"}}]}, "item_10001_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1883-8030", "subitem_source_identifier_type": "EISSN"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Yadab P. Dhakal", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Hisahiko Kubo", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Takashi Kunugi", "creatorNameLang": "en"}]}]}, "item_title": "Prediction Equations for Peak-Ground Accelerations and Velocities in Northeast Japan Using the S-net Data", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Prediction Equations for Peak-Ground Accelerations and Velocities in Northeast Japan Using the S-net Data", "subitem_title_language": "en"}]}, "item_type_id": "40001", "owner": "7", "path": ["1670839190650"], "permalink_uri": "https://nied-repo.bosai.go.jp/records/6823", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2025-04-14"}, "publish_date": "2025-04-14", "publish_status": "0", "recid": "6823", "relation": {}, "relation_version_is_last": true, "title": ["Prediction Equations for Peak-Ground Accelerations and Velocities in Northeast Japan Using the S-net Data"], "weko_shared_id": -1}