Automatic noise exploration in urban areas
Citations Over TimeTop 13% of 2017 papers
Abstract
Near-surface imaging with ambient noise has grown into an increasingly common tool over the past decade thanks to the virtual source function method. However, if non-ideal noise sources are present, experts must manually analyze the noise to look for any issues they suspect, then design filters to remove these non-ideal noises. Up until now, the deployment and maintenance of the receiver array was the primary cost, but this is changing with advancements in Distributed Acoustic Sensing (DAS), an emerging technology that repurposes a fiber optic cable as a series of strain sensors. On the Stanford campus we have shown that we can record seismic waves with fiber optic cables sitting loosely in existing telecommunications conduits. As we look forward at the possibility of easily plugging into unused fibers in telecom bundles on-demand, it is clear that manual selection of non-ideal noise sources is the next bottleneck. Herein we show a variety of methods, mixing traditional signal processing and machine learning, to automatically assist geophysicists in analyzing the ambient noise recorded and selecting non-ideal noises. We demonstrate that we can identify di erent types of noise using clustering algorithms and that template matching can be used for detecting specific events. Presentation Date: Tuesday, September 26, 2017 Start Time: 2:40 PM Location: Exhibit Hall C, E-P Station 2 Presentation Type: EPOSTER
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