{"_buckets": {"deposit": "aeb928e3-25b3-4f17-98c6-f7e3f580f91c"}, "_deposit": {"id": "4708", "owners": [1], "pid": {"revision_id": 0, "type": "depid", "value": "4708"}, "status": "published"}, "_oai": {"id": "oai:nied-repo.bosai.go.jp:00004708", "sets": []}, "author_link": [], "item_10001_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2018-01-09", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "282", "bibliographicPageStart": "278", "bibliographicVolumeNumber": "2018-", "bibliographic_titles": [{"bibliographic_title": "Proceedings - 2017 International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2017", "bibliographic_titleLang": "en"}]}]}, "item_10001_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Predicting earthquake activity is desirable because it can save lives and reduce economic losses. However, constructing a predictive model is difficult because of the complexity of earthquake activity. Thus, we adopted a statistical approach for extracting seismicity features. We extracted features of global seismicity from an earthquake data catalog using principal component analysis to reveal the spatial linkages and time dependence of earthquake activity. For principal component analysis, we defined earthquake occurrence rate and regarded its time series as samples and regional labels as the dimensionality. We demonstrate that this method accurately identified past earthquake activity and revealed correlations between remote locations and time dependence of seismicity features. These results will help the construction of a predictive earthquake activity model.", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10001_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "Institute of Electrical and Electronics Engineers Inc.", "subitem_publisher_language": "en"}]}, "item_10001_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "10.1109/ICCAIRO.2017.71"}}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Akihisa Okada", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Mitsuhiro Toriumi", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Yoshiyuki Kaneda", "creatorNameLang": "en"}]}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_title": "Feature Extraction of Global Seismicity by Principal Component Analysis", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Feature Extraction of Global Seismicity by Principal Component Analysis", "subitem_title_language": "en"}]}, "item_type_id": "40001", "owner": "1", "path": ["1670839190650"], "permalink_uri": "https://nied-repo.bosai.go.jp/records/4708", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2023-03-30"}, "publish_date": "2023-03-30", "publish_status": "0", "recid": "4708", "relation": {}, "relation_version_is_last": true, "title": ["Feature Extraction of Global Seismicity by Principal Component Analysis"], "weko_shared_id": -1}