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Automatic Facies Classification And Horizon Tracking In 3D Seismic DataNormal access

Authors: A.J. Bugge, J.E. Lie and S. Clark
Event name: First EAGE/PESGB Workshop Machine Learning
Session: Case Studies I
Publication date: 30 November 2018
DOI: 10.3997/2214-4609.201803010
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 617Kb )
Price: € 20

We present an automatic method that first classify seismic facies and then interpret seismic horizons through four steps; local binary pattern segmentation, unsupervised clustering, supervised classification and dynamic time warping. Our approach avoids the need to manually label data, reducing the need for specialist geological knowledge. We test our method on a structurally complex seismic cube acquired in the SW Barents Sea, targeting rotated Mesozoic fault blocks.

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