{"id":8610,"date":"2012-12-08T01:09:33","date_gmt":"2012-12-07T23:09:33","guid":{"rendered":"http:\/\/hgpu.org\/?p=8610"},"modified":"2012-12-08T01:09:33","modified_gmt":"2012-12-07T23:09:33","slug":"gpgpu-aided-3d-staggered-grid-finite-difference-seismic-wave-modeling","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8610","title":{"rendered":"GPGPU-Aided 3D Staggered-grid Finite-difference Seismic Wave Modeling"},"content":{"rendered":"<p>Finite difference is a simple, fast and effective numerical method for seismic wave modeling, and has been widely used in forward waveform inversion and reverse time migration. However, intensive calculation of three-dimensional seismic forward modeling has been restricting the industrial application of 3D pre-stack reverse time migration and inversion. Aiming at this problem, in this paper, a parallelized 3D Staggered-grid Finite-difference has been developed using General-purpose computing on the graphics processing unit (GPGPU), namely G-3DFD, since the emergence of graphic processing units (GPU) as an effective alternative to traditional general purpose processors has become increasingly capable in accelerating large-scale scientific computing. We analyze three-dimensional staggered grid finite difference method for the implementation on GPU, making possible the industrial application of 3D pre-stack reverse time migration and inversion. Experiments show that G-3DFD has dramatically improved the runtime performance 88 times on modern GPGPU platforms comparing to the original CPU implementation methods.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Finite difference is a simple, fast and effective numerical method for seismic wave modeling, and has been widely used in forward waveform inversion and reverse time migration. However, intensive calculation of three-dimensional seismic forward modeling has been restricting the industrial application of 3D pre-stack reverse time migration and inversion. Aiming at this problem, in this [&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":[89,303,3],"tags":[14,1801,327,20,1306,241,856],"class_list":["post-8610","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-earth-and-space-sciences","category-paper","tag-cuda","tag-earth-and-space-sciences","tag-finite-difference","tag-nvidia","tag-nvidia-geforce-gtx-680","tag-seismic-modeling","tag-seismology"],"views":4490,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8610","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=8610"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8610\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8610"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8610"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8610"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}