{"id":10384,"date":"2016-10-26T10:36:59","date_gmt":"2016-10-26T10:36:59","guid":{"rendered":"http:\/\/revoscience.com\/en\/?p=10384"},"modified":"2016-10-26T10:36:59","modified_gmt":"2016-10-26T10:36:59","slug":"shape-detection-at-fault","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/shape-detection-at-fault\/","title":{"rendered":"Shape detection at fault"},"content":{"rendered":"

Software that automatically recognizes surfaces within complex three-dimensional images can benefit petroleum extraction.<\/strong><\/em><\/p>\n

\n

\"A<\/a>
A new algorithm that generates smooth 3-D images of underground fault locations makes it easier to find petroleum resources. \u00a9 2016 KAUST<\/figcaption><\/figure>\n

The deep cracking faults that lie within the Earth\u2019s crust are significant geologic surfaces for oil exploration and earthquake prediction. A team from KAUST developed an algorithm that smoothly detects faults and other three-dimensional (3-D) surfaces with high computational efficiency even amid noisy and cluttered data sets.<\/span>

Identifying objects in images using geometric curves is critical to many computer vision applications. One method uses fast marching algorithms that track how interfaces evolve with time from an initial seed point. This technique uses efficient computational routines to expand the seed curve step-by-step until mathematical conditions corresponding to a boundary are met\u2014the steep slope of a cliff, for instance.<\/span>

Requiring software users to define probable surface boundaries, however, makes it tricky to use fast marching algorithms for complex 3-D problems.\u00a0<\/span>

<\/p>\n

[pullquote]\u201cOur idea embeds 3-D curves on the surface as ridges of a moving front, and we watch the curves evolve as the front propagates,\u201d noted Sundaramoorthi.[\/pullquote]<\/p>\n

\u201cIt\u2019s a challenge to extract a surface from an image volume when the boundary is non-empty and unknown,\u201d explained Ganesh Sundaramoorthi from the University\u2019s Computer, Electrical and Mathematical Science and Engineering Division. \u201cUntil now, no algorithm could handle this task.\u201d<\/span>

Sundaramoorthi and colleague Marei Algarni recently developed software known as SurfCut to solve these issues. The pair realized that for two-dimensional (2-D) objects, a small curve from a seed point can trace out the surface and automatically stop along the boundary. However, performing the equivalent operations in 3-D required a new approach based on topology, or the mathematical descriptions of features that are preserved under deformation.<\/span>

The new analysis program uses fast marching methods to compute the shortest paths between a seed point and a moving interface. Ridge sets are then computed by retracting the interface until rigid topological features emerge. These features are likely to lie on the surface, and the team\u2019s algorithm extracts them to efficiently determine 3-D surfaces.<\/span>

\u201cOur idea embeds 3-D curves on the surface as ridges of a moving front, and we watch the curves evolve as the front propagates,\u201d noted Sundaramoorthi. \u201cRidge sets are difficult to extract from realistic images, which are discrete and noisy, but our algorithm makes this operation feasible.\u201d<\/span>

To demonstrate the usefulness of SurfCut, the researchers analyzed a series of seismic images and generated new underground 3-D maps with stunning clarity. \u201cUnderstanding complex fault surfaces can be hard, even for expert geologists,\u201d Sundaramoorthi said. \u201cOur technique allows them to see structures that are impossible to view using 2-D slices, and is really robust against data imperfections. This could directly impact the oil industry.\u201d<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

The deep cracking faults that lie within the Earth\u2019s crust are significant geologic surfaces for oil exploration and earthquake prediction. A team from KAUST developed an algorithm that smoothly detects faults and other three-dimensional (3-D) surfaces with high computational efficiency even amid noisy and cluttered data sets.<\/p>\n","protected":false},"author":6,"featured_media":10385,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[43,22,17],"tags":[],"class_list":["post-10384","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-computer-science","category-other","category-research"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",500,375,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1-150x150.jpg",150,150,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1-300x225.jpg",300,225,true],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",500,375,false],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",500,375,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",500,375,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",500,375,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",500,375,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",500,375,false],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",500,375,false],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",500,375,false],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",500,375,false],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",480,360,false],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",87,65,false],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",500,375,false],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",96,72,false],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/10\/3956-1.jpg",150,113,false]},"author_info":{"info":["Amrita Tuladhar"]},"category_info":"Computer Science<\/a> Other<\/a> Research<\/a>","tag_info":"Research","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/10384","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=10384"}],"version-history":[{"count":0,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/10384\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/10385"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=10384"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=10384"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=10384"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}