{"id":2792,"date":"2024-12-11T21:57:16","date_gmt":"2024-12-12T05:57:16","guid":{"rendered":"https:\/\/nwekenest.com\/?p=2792"},"modified":"2025-07-26T22:10:41","modified_gmt":"2025-07-27T05:10:41","slug":"new-paper-alert-southern-california-basin-and-non-basin-classification-algorithm-for-ground-motion-site-amplification-model-applications","status":"publish","type":"post","link":"https:\/\/nwekenest.com\/index.php\/2024\/12\/11\/new-paper-alert-southern-california-basin-and-non-basin-classification-algorithm-for-ground-motion-site-amplification-model-applications\/","title":{"rendered":"!!!NEW PAPER ALERT!!! Southern California basin and non-basin classification algorithm for ground-motion site amplification model applications"},"content":{"rendered":"<p>This work investigates the means used to characterize site conditions for seismic ground motion hazard modeling, analysis, and assessment. We embark on the path to quantitatively assign basin categories using machine learning algorithms test discover two length scales work well for Southern California. We discover that with just the use of surface texture as a feature parameter we can accurately and adequately classify sites as basin, non-basin, and intermediate categories. The basin classification algorithm code can be found at the following DesignSafe project.<br \/>\n<a class=\"DGvtWMHHFxUFucvarCuTmPvyMuWuwlaQNuYA \" tabindex=\"0\" href=\"https:\/\/lnkd.in\/gkKYZBcg\" target=\"_self\" data-test-app-aware-link=\"\">https:\/\/lnkd.in\/gkKYZBcg<\/a><\/p>\n<p>and the paper can be accessed <a href=\"https:\/\/journals.sagepub.com\/doi\/10.1177\/87552930241293568\">here<\/a>.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-2794 size-large\" src=\"https:\/\/nwekenest.com\/wp-content\/uploads\/2025\/07\/Figure-9-1024x479.jpg\" alt=\"\" width=\"1024\" height=\"479\" srcset=\"https:\/\/nwekenest.com\/wp-content\/uploads\/2025\/07\/Figure-9-1024x479.jpg 1024w, https:\/\/nwekenest.com\/wp-content\/uploads\/2025\/07\/Figure-9-300x140.jpg 300w, https:\/\/nwekenest.com\/wp-content\/uploads\/2025\/07\/Figure-9-768x359.jpg 768w, https:\/\/nwekenest.com\/wp-content\/uploads\/2025\/07\/Figure-9-1536x718.jpg 1536w, https:\/\/nwekenest.com\/wp-content\/uploads\/2025\/07\/Figure-9-2048x957.jpg 2048w, https:\/\/nwekenest.com\/wp-content\/uploads\/2025\/07\/Figure-9-610x285.jpg 610w, https:\/\/nwekenest.com\/wp-content\/uploads\/2025\/07\/Figure-9-600x280.jpg 600w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This work investigates the means used to characterize site conditions for seismic ground motion hazard modeling, analysis, and assessment. We embark on the path to quantitatively assign basin categories using machine learning algorithms test discover two length scales work well for Southern California. We discover that with just the use of surface texture as a &hellip; <a href=\"https:\/\/nwekenest.com\/index.php\/2024\/12\/11\/new-paper-alert-southern-california-basin-and-non-basin-classification-algorithm-for-ground-motion-site-amplification-model-applications\/\" title=\"!!!NEW PAPER ALERT!!! Southern California basin and non-basin classification algorithm for ground-motion site amplification model applications\" class=\"read-more\">Read More<\/a><\/p>\n","protected":false},"author":1,"featured_media":2793,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[86,85],"tags":[],"class_list":["post-2792","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-journal-article","category-publication"],"_links":{"self":[{"href":"https:\/\/nwekenest.com\/index.php\/wp-json\/wp\/v2\/posts\/2792","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nwekenest.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nwekenest.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nwekenest.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/nwekenest.com\/index.php\/wp-json\/wp\/v2\/comments?post=2792"}],"version-history":[{"count":1,"href":"https:\/\/nwekenest.com\/index.php\/wp-json\/wp\/v2\/posts\/2792\/revisions"}],"predecessor-version":[{"id":2795,"href":"https:\/\/nwekenest.com\/index.php\/wp-json\/wp\/v2\/posts\/2792\/revisions\/2795"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nwekenest.com\/index.php\/wp-json\/wp\/v2\/media\/2793"}],"wp:attachment":[{"href":"https:\/\/nwekenest.com\/index.php\/wp-json\/wp\/v2\/media?parent=2792"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nwekenest.com\/index.php\/wp-json\/wp\/v2\/categories?post=2792"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nwekenest.com\/index.php\/wp-json\/wp\/v2\/tags?post=2792"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}