  Moving loads such as cars and trains are very useful sources of seismic
waves, which can be analyzed to retrieve information on the seismic velocity of
subsurface materials using the techniques of ambient noise seismology. This
information is valuable for a variety of applications such as geotechnical
characterization of the near-surface, seismic hazard evaluation, and
groundwater monitoring. However, for such processes to converge quickly, data
segments with appropriate noise energy should be selected. Distributed Acoustic
Sensing (DAS) is a novel sensing technique that enables acquisition of these
data at very high spatial and temporal resolution for tens of kilometers. One
major challenge when utilizing the DAS technology is the large volume of data
that is produced, thereby presenting a significant Big Data challenge to find
regions of useful energy. In this work, we present a highly scalable and
efficient approach to process real, complex DAS data by integrating physics
knowledge acquired during a data exploration phase followed by deep supervised
learning to identify "useful" coherent surface waves generated by anthropogenic
activity, a class of seismic waves that is abundant on these recordings and is
useful for geophysical imaging. Data exploration and training were done on
130~Gigabytes (GB) of DAS measurements. Using parallel computing, we were able
to do inference on an additional 170~GB of data (or the equivalent of 10 days'
worth of recordings) in less than 30 minutes. Our method provides interpretable
patterns describing the interaction of ground-based human activities with the
buried sensors.
