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Salt Classification Using Deep LearningNormal access

Authors: A.U. Waldeland and A.H.S.S. Solberg
Event name: 79th EAGE Conference and Exhibition 2017
Session: Seismic Interpretation - Analytics and Machine Learning for Interpretation
Publication date: 12 June 2017
DOI: 10.3997/2214-4609.201700918
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 1.42Mb )
Price: € 20

Summary:
Traditional methods for salt classification consist of choosing a set of attributes that are sensitive to the characteristics of salt bodies and training a classification algorithm to discriminate between salt and other geological structures. Convolutional neural networks have the advantage of combining attribute extraction and classification in one network. This allows both the attributes and classification to be trainable for the given application. In this work we show how this technique can be applied to salt classification in seismic datasets. The results shows that training a classifier on one labelled inline slice is sufficient to classify other slices in the same dataset.


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