  Deep Learning approaches for real, large, and complex scientific data sets
can be very challenging to design. In this work, we present a complete search
for a finely-tuned and efficiently scaled deep learning classifier to identify
usable energy from seismic data acquired using Distributed Acoustic Sensing
(DAS). While using only a subset of labeled images during training, we were
able to identify suitable models that can be accurately generalized to unknown
signal patterns. We show that by using 16 times more GPUs, we can increase the
training speed by more than two orders of magnitude on a 50,000-image data set.
