Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PFRL22 |
Repository | sid.inpe.br/sibgrapi/2017/08.21.23.15 |
Last Update | 2017:08.21.23.15.00 administrator |
Metadata | sid.inpe.br/sibgrapi/2017/08.21.23.15.01 |
Metadata Last Update | 2020:02.19.02.01.37 administrator |
Citation Key | CruzCaSaPeLeCl:2017:ImAcAu |
Title | Improving accuracy of automatic fracture detection in borehole images with deep learning and GPUs  |
Format | On-line |
Year | 2017 |
Access Date | 2021, Jan. 26 |
Number of Files | 1 |
Size | 2049 KiB |
Context area | |
Author | 1 Cruz, Rommel Anatoli Quintanilla 2 Cacau, Diego Carriço 3 Santos, Renato Moraes dos 4 Pereira, Evandro Jose Ribeiro 5 Leta, Fabiana 6 Clua, Esteban |
Affiliation | 1 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 |
Editor | Torchelsen, 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 Address | rquintanilla@id.uff.br |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ |
Date | Oct. 17-20, 2017 |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2017-08-21 23:15:01 :: rquintanilla@id.uff.br -> administrator :: 2020-02-19 02:01:37 :: administrator -> :: 2017 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | Deep learning, Automatic fracture detection, Pattern recognition. |
Abstract | The 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. |
source Directory Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3PFRL22 |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PFRL22 |
Language | en |
Target File | PID4960365.pdf |
User Group | rquintanilla@id.uff.br |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PJT9LS 8JMKD3MGPAW/3PKCC58 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
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