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Recently, there has been increasing interest in using deep learningtechniques for various seismic interpretation tasks. However, unlike shallowmachine learning models, deep learning models are often far more complex andcan have hundreds of millions of free parameters. This not only means thatlarge amounts of computational resources are needed to train these models, butmore critically, they require vast amounts of labeled training data as well. Inthis work, we show how automatically-generated weak labels can be effectivelyused to overcome this problem and train powerful deep learning models forlabeling seismic structures in large seismic volumes. To achieve this, weautomatically generate thousands of weak labels and use them to train adeconvolutional network for labeling fault, salt dome, and chaotic regionswithin the Netherlands F3 block. Furthermore, we show how modifying the lossfunction to take into account the weak training labels helps reduce falsepositives in the labeling results. The benefit of this work is that it enablesthe effective training and deployment of deep learning models to variousseismic interpretation tasks without requiring any manual labeling effort. Weshow excellent results on the Netherlands F3 block, and show how our modeloutperforms other baseline models.

Learning to Label Seismic Structures with Deconvolution Networks and Weak Labels