The seismic traffic footprint: Tracking trains, aircraft, and cars seismically

Abstract

Although naturally occurring vibrations have proven useful to probe the subsurface, the vibrations caused by traffic have not been explored much. Such data, however, are less sensitive to weather and low visibility compared to some common out‐of‐road traffic sensing systems. We study traffic‐generated seismic noise measured by an array of 5200 geophones that covered a 7 × 10 km area in Long Beach (California, USA) with a receiver spacing of 100 m. This allows us to look into urban vibrations below the resolution of a typical city block. The spatiotemporal structure of the anthropogenic seismic noise intensity reveals the Blue Line Metro train activity, departing and landing aircraft in Long Beach Airport and their acceleration, and gives clues about traffic movement along the I‐405 highway at night. As low‐cost, stand‐alone seismic sensors are becoming more common, these findings indicate that seismic data may be useful for traffic monitoring.

Real‐time detection, location, and characterization of rockslides using broadband regional seismic networks

“…However, the spectral fingerprints of seismic signals induced by mass-wasting phenomena can be also investigated to better discriminate landslides from small local earthquakes and/or other anthropogenic events [Latorre et al, 2014;Riahi and Gerstoft, 2015]. However, the spectral fingerprints of seismic signals induced by mass-wasting phenomena can be also investigated to better discriminate landslides from small local earthquakes and/or other anthropogenic events [Latorre et al, 2014;Riahi and Gerstoft, 2015].…”

“…We verified that the ratio between M L and M D is a fast and reliable criterion to discriminate rockslides from local earthquakes in the European Alps. However, the spectral fingerprints of seismic signals induced by mass-wasting phenomena can be also investigated to better discriminate landslides from small local earthquakes and/or other anthropogenic events [Latorre et al, 2014;Riahi and Gerstoft, 2015]. These observations can be used to implement further benchmarks to our algorithm, thus improving its detection capability.…”

Do Low‐Cost Seismographs Perform Well Enough for Your Network? An Overview of Laboratory Tests and Field Observations of the OSOP Raspberry Shake 4D

“…The low cost of nodes coupled with the ability to install the instruments quickly has led to large numbers of sensors (> 100) being deployed in spatially dense (< 200 m station spacing) networks (e.g., Schmandt and Clayton, 2013;Ben-Zion et al, 2015;Sweet et al, 2018). In turn, this has enabled high-resolution imaging of the shallow subsurface (e.g., Lin et al, 2013), reduced detection thresholds of local seismicity (e.g., Hansen and Schmandt, 2015), and improved characterization of naturally occurring and anthropogenic high-frequency (> 1 Hz) seismic noise sources (e.g., Riahi and Gerstoft, 2015;Schmandt et al, 2017).…”

Passive processing of active nodal seismic data: Estimation of V&lt;sub&gt;P&lt;/sub&gt; / V&lt;sub&gt;S&lt;/sub&gt;-ratios to characterize structure and hydrology of an alpine valley infill

“…CC BY 4.0 License. (McNamara and Bulland, 2004;Riahi and Gerstoft, 2015). The location and timing of specific events within this noise spectrum might be known with some degree of uncertainty (e.g.…”