  Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake
monitoring and subsurface imaging. The recorded seismic signals by DAS have
several distinct characteristics, such as unknown coupling effects, strong
anthropogenic noise, and ultra-dense spatial sampling. These aspects differ
from conventional seismic data recorded by seismic networks, making it
challenging to utilize DAS at present for seismic monitoring. New data analysis
algorithms are needed to extract useful information from DAS data. Previous
studies on conventional seismic data demonstrated that deep learning models
could achieve performance close to human analysts in picking seismic phases.
However, phase picking on DAS data is still a difficult problem due to the lack
of manual labels. Further, the differences in mathematical structure between
these two data formats, i.e., ultra-dense DAS arrays and sparse seismic
networks, make model fine-tuning or transfer learning difficult to implement on
DAS data. In this work, we design a new approach using semi-supervised learning
to solve the phase-picking task on DAS arrays. We use a pre-trained PhaseNet
model as a teacher network to generate noisy labels of P and S arrivals on DAS
data and apply the Gaussian mixture model phase association (GaMMA) method to
refine these noisy labels to build training datasets. We develop a new deep
learning model, PhaseNet-DAS, to process the 2D spatial-temporal data of DAS
arrays and train the model on DAS data. The new deep learning model achieves
high picking accuracy and good earthquake detection performance. We then apply
the model to process continuous data and build earthquake catalogs directly
from DAS recording. Our approach using semi-supervised learning provides a way
to build effective deep learning models for DAS, which have the potential to
improve earthquake monitoring using large-scale fiber networks.

Geophysics