Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyCruzCaSaPeLeCl:2017:ImAcAu
TitleImproving accuracy of automatic fracture detection in borehole images with deep learning and GPUs
Access Date2021, Jan. 26
Number of Files1
Size2049 KiB
Context area
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
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
DateOct. 17-20, 2017
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-21 23:15:01 :: -> administrator ::
2020-02-19 02:01:37 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
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.
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Next Higher Units8JMKD3MGPAW/3PJT9LS
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