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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFRL22
Repositorysid.inpe.br/sibgrapi/2017/08.21.23.15
Last Update2017:08.21.23.15.00 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.21.23.15.01
Metadata Last Update2022:06.14.00.09.00 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.52
Citation KeyCruzCaSaPeLeCl:2017:ImAcAu
TitleImproving accuracy of automatic fracture detection in borehole images with deep learning and GPUs
FormatOn-line
Year2017
Access Date2024, Apr. 27
Number of Files1
Size2049 KiB
2. Context
Author1 Cruz, Rommel Anatoli Quintanilla
2 Cacau, Diego Carriço
3 Santos, Renato Moraes dos
4 Pereira, Evandro Jose Ribeiro
5 Leta, Fabiana
6 Clua, Esteban
Affiliation1 Universidade Federal Fluminense
2 Universidade Federal Fluminense
3 LMDC - Universidade Federal Fluminense
4 LMDC - Universidade Federal Fluminense
5 LMDC - Universidade Federal Fluminense
6 Universidade Federal Fluminense
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressrquintanilla@id.uff.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-21 23:15:01 :: rquintanilla@id.uff.br -> administrator ::
2022-06-14 00:09:00 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsDeep learning
Automatic fracture detection
Pattern recognition
AbstractThe logging and further analysis of borehole images is a major step in the interpretation of geological events. Natural fractures and beddings are features whose identification is commonly performed using acoustic and electrical borehole imaging tools. Such identification is a tedious task and is made visually by geologists, who must be experts on classification. The correct identification of planar features, represented as sinusoids into an image projection, depends on the quality of the images. Due to the distortions and noises of the images, known as artifacts, the automatic features detection is not trivial through conventional image processing methods. Since the identification process has to ensure that the marked events are true with minimal inconsistencies, we propose a pioneering approach to improving the quality of the results by applying deep neural networks to confirm or exclude candidate features extracted by a regular Hough transform. This is the first approach in literature to improve the quality of geological auto-detected marks by applying deep learning techniques for borehole images where our implementation is able to exclude most of the false positive marks.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Improving accuracy of...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Improving accuracy of...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFRL22
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFRL22
Languageen
Target FilePID4960365.pdf
User Grouprquintanilla@id.uff.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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