Detecting local earthquakes via fiber-optic cables in telecommunication conduits under Stanford University campus using deep learning

With fiber-optic seismic acquisition development, continuous dense seismic monitoring is becoming increasingly more accessible. Repurposing fiber cables in telecommunication conduits makes it possible to run seismic studies at low cost, even in locations where traditional seismometers are not easily installed, such as urban areas. However, due to the large volume of continuous streaming data, data collected in such a manner will go to waste unless we significantly automate the processing workflow. We train a convolutional neural network (CNN) for earthquake detection using 3000 events from a publicly available catalog and data acquired over three years by fiber cables in telecommunication conduits under the Stanford University campus. We performed a hyperparameter search both on the network architecture itself (e.g., number of layers, number of parameters) and on its training parameters, showing that CNNs with a small number of layers are sufficient for performing this detection task with high accuracy. We introduce a novel method for combining the deep learning results on fiber-optic and seismometer data to improve detection accuracy, dramatically reducing the false detection rate that is often a problem when processing large time-scale noisy continuous data. Consequently, we demonstrate that enhancing two sparse seismometer stations with an urban fiber system allows for the reliable detection of small earthquakes despite a low signal-to-noise ratio. We scale this processing method over three years of continuous data and show that this system reliably detects local small-amplitude earthquakes down to magnitudes as low as 0.5, leading to the discovery of previously uncataloged events.