{"id":101538,"date":"2019-02-12T20:15:27","date_gmt":"2019-02-12T20:15:27","guid":{"rendered":"http:\/\/ahay.org\/blog\/?p=101538"},"modified":"2019-05-03T22:28:32","modified_gmt":"2019-05-03T22:28:32","slug":"emd-seislet","status":"publish","type":"post","link":"https:\/\/ahay.org\/blog\/2019\/02\/12\/emd-seislet\/","title":{"rendered":"EMD-Seislet"},"content":{"rendered":"<p>A new paper is added to the <a href=\"\/wiki\/Reproducible_Documents\">collection of reproducible documents<\/a>: <a href=\"\/RSF\/book\/tccs\/eseis\/paper_html\/\">EMD-seislet transform<\/a><\/p>\n<p><img decoding=\"async\" src=\"\/RSF\/book\/tccs\/eseis\/sparse\/Fig\/sparse.png\" alt=\"\" title=\"\" \/> <img decoding=\"async\" src=\"\/RSF\/book\/tccs\/eseis\/field\/Fig\/real-0.png\" alt=\"\" title=\"\" \/> <img decoding=\"async\" src=\"\/RSF\/book\/tccs\/eseis\/field\/Fig\/real-eseist-0.png\" alt=\"\" title=\"\" \/><\/p>\n<blockquote>\n<p>The seislet transform uses a prediction operator which is connected to the local slope or frequency of seismic events. In this paper, we propose combining the 1D non-stationary seislet transform with empirical mode decomposition (EMD) in the $f-x$ domain. We use the EMD to decompose data into smoothly variable frequency components for the following 1D seislet transform. The resultant representation shows remarkable sparsity. We introduce the detailed algorithm and use a field example to demonstrate the application of the new seislet transform for sparsity-promoting seismic data processing. <\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>A new paper is added to the collection of reproducible documents: EMD-seislet transform The seislet transform uses a prediction operator which is connected to the local slope or frequency of seismic events. In this paper, we propose combining the 1D non-stationary seislet transform with empirical mode decomposition (EMD) in the $f-x$ domain. We use the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","activitypub_content_warning":"","activitypub_content_visibility":"local","activitypub_max_image_attachments":4,"activitypub_interaction_policy_quote":"","footnotes":""},"categories":[5],"tags":[],"class_list":["post-101538","post","type-post","status-publish","format-standard","hentry","category-documentation"],"_links":{"self":[{"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/posts\/101538","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/comments?post=101538"}],"version-history":[{"count":3,"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/posts\/101538\/revisions"}],"predecessor-version":[{"id":101549,"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/posts\/101538\/revisions\/101549"}],"wp:attachment":[{"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/media?parent=101538"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/categories?post=101538"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/tags?post=101538"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}