{"id":4571,"date":"2011-07-04T12:12:24","date_gmt":"2011-07-04T12:12:24","guid":{"rendered":"http:\/\/hgpu.org\/?p=4571"},"modified":"2011-07-04T12:12:24","modified_gmt":"2011-07-04T12:12:24","slug":"seismic-volume-visualization-for-horizon-extraction","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4571","title":{"rendered":"Seismic volume visualization for horizon extraction"},"content":{"rendered":"<p>Seismic horizons indicate change in rock properties and are central in geoscience interpretation. Traditional interpretation systems involve time consuming and repetitive manual volumetric seeding for horizon growing. We present a novel system for rapidly interpreting and visualizing seismic volumetric data. First we extract horizon surface-parts by preprocessing the seismic data. Then during interaction the user can assemble in realtime the horizon parts into horizons. Traditional interpretation systems use gradient-based illumination models in the rendering of the seismic volume and polygon rendering of horizon surfaces. We employ realtime gradient-free forward-scattering in the rendering of seismic volumes yielding results similar to high-quality global illumination. We use an implicit surface representation of horizons allowing for a seamless integration of horizon rendering and volume rendering. We present a collection of novel techniques constituting an interpretation and visualization system highly tailored to seismic data interpretation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Seismic horizons indicate change in rock properties and are central in geoscience interpretation. Traditional interpretation systems involve time consuming and repetitive manual volumetric seeding for horizon growing. We present a novel system for rapidly interpreting and visualizing seismic volumetric data. First we extract horizon surface-parts by preprocessing the seismic data. Then during interaction the user [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,192,3],"tags":[1782,14,1798,20,373,297,144,134],"class_list":["post-4571","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-geoscience","category-paper","tag-computer-science","tag-cuda","tag-geoscience","tag-nvidia","tag-nvidia-geforce-gtx-275","tag-real-time-graphics","tag-rendering","tag-visualization"],"views":2036,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4571","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4571"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4571\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4571"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4571"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4571"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}