Quality of Green's Functions Improved by Automatic Detection and Removal of Coherent Anthropogenic Noise

We analyze the impact of identifying and removing coherent anthropogenic noise on synthetic Green's functions extracted from ambient noise recorded on a dense linear distributed acoustic sensing (DAS) array. Low-cost, low-impact urban seismic surveys are possible with DAS, which uses dynamic strain sensing to record seismic waves incident to a buried fiber optic cable. However, interferometry and tomography of ambient noise data recorded in urban areas include coherent noise from near-field infrastructure such as cars and trains passing the array, in some cases causing artifacts in estimated Green's functions and potentially incorrect surface wave velocities. Based on our comparison of several methods, we propose an automated, real-time data processing workflow to detect and reduce the impact of these events on data from a dense array in an urban environment. We utilize a recursive STA/LTA (short-term average/long-term average) algorithm on each channel to identify sharp amplitude changes typically associated with an event arrival. In order to distinguish between optical noise and physical events, an event is cataloged only if STA/LTA is triggered on enough channels across the array in a short time window. For each event in the catalog, a conventional semblance analysis is performed across a straight segment of the array to determine whether the event has a coherent velocity signature. Events that demonstrate a semblance peak at low apparent velocities (5-50 m/s) are assumed to represent coherent transportation-related noise and are down-weighted in the time domain before cross-correlation. We show the impact of removing such noise on estimated Green's functions from ambient noise data recorded in Richmond, CA in December 2014. This method has been developed for use on a continuous time-lapse ambient noise survey collected with DAS near Fairbanks, AK, and an upcoming ambient noise survey on the Stanford University campus using DAS with a re-purposed telecommunications fiber optic cable.