%0 Conference Proceedings
%T Improving accuracy of automatic fracture detection in borehole images with deep learning and GPUs
%D 2017
%A Cruz, Rommel Anatoli Quintanilla,
%A Cacau, Diego Carriço,
%A Santos, Renato Moraes dos,
%A Pereira, Evandro Jose Ribeiro,
%A Leta, Fabiana,
%A Clua, Esteban,
%@affiliation Universidade Federal Fluminense
%@affiliation Universidade Federal Fluminense
%@affiliation LMDC - Universidade Federal Fluminense
%@affiliation LMDC - Universidade Federal Fluminense
%@affiliation LMDC - Universidade Federal Fluminense
%@affiliation Universidade Federal Fluminense
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ
%8 Oct. 17-20, 2017
%S Proceedings
%I IEEE Computer Society
%J Los Alamitos
%K Deep learning, Automatic fracture detection, Pattern recognition.
%X 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.
%@language en
%3 PID4960365.pdf