  Recently, there has been increasing interest in using deep learning
techniques for various seismic interpretation tasks. However, unlike shallow
machine learning models, deep learning models are often far more complex and
can have hundreds of millions of free parameters. This not only means that
large amounts of computational resources are needed to train these models, but
more critically, they require vast amounts of labeled training data as well. In
this work, we show how automatically-generated weak labels can be effectively
used to overcome this problem and train powerful deep learning models for
labeling seismic structures in large seismic volumes. To achieve this, we
automatically generate thousands of weak labels and use them to train a
deconvolutional network for labeling fault, salt dome, and chaotic regions
within the Netherlands F3 block. Furthermore, we show how modifying the loss
function to take into account the weak training labels helps reduce false
positives in the labeling results. The benefit of this work is that it enables
the effective training and deployment of deep learning models to various
seismic interpretation tasks without requiring any manual labeling effort. We
show excellent results on the Netherlands F3 block, and show how our model
outperforms other baseline models.
