  Microseismic analysis is a valuable tool for fracture characterization in the
earth's subsurface. As distributed acoustic sensing (DAS) fibers are deployed
at depth inside wells, they hold vast potential for high-resolution
microseismic analysis. However, the accurate detection of microseismic signals
in continuous DAS data is challenging and time-consuming. We design, train, and
deploy a deep learning model to detect microseismic events in DAS data
automatically. We create a curated dataset of nearly 7,000 manually-selected
events and an equal number of background noise examples. We optimize the deep
learning model's network architecture together with its training
hyperparameters by Bayesian optimization. The trained model achieves an
accuracy of 98.6% on our benchmark dataset and even detects low-amplitude
events missed during manual labeling. Our methodology detects more than 100,000
events allowing the reconstruction of spatio-temporal fracture development far
more accurately and efficiently than would have been feasible by traditional
methods.
