{"id":299,"date":"2012-08-01T12:10:12","date_gmt":"2012-08-01T12:10:12","guid":{"rendered":"http:\/\/ahay.org\/blog\/?p=299"},"modified":"2015-09-02T21:28:46","modified_gmt":"2015-09-02T21:28:46","slug":"program-of-the-month-sfpick","status":"publish","type":"post","link":"https:\/\/ahay.org\/blog\/2012\/08\/01\/program-of-the-month-sfpick\/","title":{"rendered":"Program of the month: sfpick"},"content":{"rendered":"<p><a href=\"\/RSF\/sfpick.html\">sfpick<\/a> performs automatic picking from semblance-like panels. <\/p>\n<p>The underlying algorithm is described in <a href=\"\/RSF\/book\/tccs\/avo\/paper_html\/node12.html\">Appendix B<\/a> of the paper <a href=\"\/RSF\/book\/tccs\/avo\/paper_html\/node12.html\">Velocity analysis using AB semblance<\/a> and is inspired by the method of virtual endoscopy in medical imaging. The following example from <a href=\"\/RSF\/book\/tccs\/avo\/avo.html\">tccs\/avo\/avo<\/a> shows an automatic pick through AB semblance. <\/p>\n<p><img decoding=\"async\" src=\"\/RSF\/book\/tccs\/avo\/avo\/Fig\/avoscn.png\" alt=\"\" title=\"\" \/> <\/p>\n<p>If <strong>smooth=n<\/strong>, the program picks an optimal path through the panel but does not try to smooth it. If <strong>smooth=y<\/strong> (the default), the picked path is additionally smoothed using <a href=\"\/RSF\/book\/jsg\/shape\/paper_html\/\">shaping regularization<\/a>. The smoothing radius across different dimensions is controled by <strong>rect1=<\/strong>, <strong>rect2=<\/strong>, etc. parameters. The number of shaping iterations is specified by <strong>niter=<\/strong>. The following example from <a href=\"\/RSF\/book\/sep\/vc2\/beivc.html\">sep\/vc2\/beivc<\/a> shows two different picks for two different values of the <strong>rect1=<\/strong> parameter <\/p>\n<p><img decoding=\"async\" src=\"\/RSF\/book\/sep\/vc2\/beivc\/Fig\/velpick.png\" alt=\"\" title=\"\" \/> <\/p>\n<p>The <strong>vel0=<\/strong> refers to the value the pick initially takes at the surface (the origin). This value is not necessarily preserved, because it can get modified by smoothing. In the following example from <a href=\"\/RSF\/book\/tccs\/timelapse\/timelapse.html\">tccs\/timelapse\/timelapse<\/a>, <strong>vel0=1<\/strong>. <\/p>\n<p><img decoding=\"async\" src=\"\/RSF\/book\/tccs\/timelapse\/timelapse\/Fig\/scan100.png\" alt=\"\" title=\"\" \/> <\/p>\n<p>The <strong>an=<\/strong> parameter controls the anisotropy between the two axes of the semblance panel. Varying this parameter, you can achieve picks that are more rigid or more flexible. The <strong>gate=<\/strong> parameter controls how far (in velocity samples) the pick can swing between two neighboring time samples. Changing its value also affects the flexibility of the picked trend. <\/p>\n<p>For picking from 3-D (rather than 2-D) panels, try <a href=\"\/RSF\/sfpick3.html\">sfpick3<\/a>. <\/p>\n<h3 id=\"10previousprogramsofthemonth\">10 previous programs of the month<\/h3>\n<ul>\n<li><a href=\"\/blog\/2012\/07\/02\/program-of-the-month-sffft3\/\">sffft3<\/a><\/li>\n<li><a href=\"\/blog\/2012\/06\/02\/program-of-the-month-sfdip\/\">sfdip<\/a><\/li>\n<li><a href=\"\/blog\/2012\/05\/01\/program-of-the-month-sfderiv\/\">sfderiv<\/a><\/li>\n<li><a href=\"\/blog\/2012\/04\/01\/program-of-the-month-sfgrey3\/\">sfgrey3<\/a><\/li>\n<li><a href=\"\/blog\/2012\/03\/18\/program-of-the-month-sfspectra\/\">sfspectra<\/a><\/li>\n<li><a href=\"\/blog\/2011\/07\/03\/program-of-the-month-sfnoise\/\">sfnoise<\/a><\/li>\n<li><a href=\"\/blog\/2011\/08\/09\/program-of-the-month-sfgraph\/\">sfgraph<\/a><\/li>\n<li><a href=\"\/blog\/2011\/09\/03\/program-of-the-month-sfclip\/\">sfclip<\/a><\/li>\n<li><a href=\"\/blog\/2011\/10\/01\/program-of-the-month-sfagc\/\">sfagc<\/a><\/li>\n<li><a href=\"\/blog\/2011\/11\/05\/program-of-the-month-sfenvelope\/\">sfenvelope<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>sfpick performs automatic picking from semblance-like panels. The underlying algorithm is described in Appendix B of the paper Velocity analysis using AB semblance and is inspired by the method of virtual endoscopy in medical imaging. The following example from tccs\/avo\/avo shows an automatic pick through AB semblance. If smooth=n, the program picks an optimal path [&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":[3],"tags":[],"class_list":["post-299","post","type-post","status-publish","format-standard","hentry","category-programs"],"_links":{"self":[{"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/posts\/299","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=299"}],"version-history":[{"count":3,"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/posts\/299\/revisions"}],"predecessor-version":[{"id":20104,"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/posts\/299\/revisions\/20104"}],"wp:attachment":[{"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/media?parent=299"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/categories?post=299"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ahay.org\/blog\/wp-json\/wp\/v2\/tags?post=299"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}