High-frequency Rayleigh-wave tomography using traffic noise from Long Beach, California

Abstract

Using a dense seismic array in Long Beach, California, we have investigated the effectiveness of using traffic noise for passive subsurface imaging. Spectral analysis revealed that traffic-induced vibrations dominate the ambient seismic noise field at frequencies between 3 and 15 Hz. Using the ambient-noise crosscorrelation technique at these frequencies, we have extracted fundamental- and first-order-mode Rayleigh waves generated by Interstate 405 and local roads. We picked group traveltimes associated with the fundamental mode and used them in a straight-ray tomography procedure to produce group velocity maps at 3.0 and 3.5 Hz. The velocity trends in our results corresponded to shallow depths and coincided well with lithologies outlined in a geologic map of the survey area. The most prominent features resolved in our velocity maps were the low velocities to the north corresponding to less-consolidated materials, high velocities to the south corresponding to more-consolidated materials, a low-velocity zone corresponding to artificial fill in Alamitos Bay, and a low-velocity linear feature in the Newport-Inglewood Fault Zone. Our resulting near-surface velocities can be useful for identifying regions that are susceptible to serious damage during earthquake-related shaking.

Using a Time‐Based Subarray Method to Extract and Invert Noise‐Derived Body Waves at Long Beach, California

“…The tomographic model shows velocity variations that are, in general, consistent with the surface wave results of Lin et al (). As expected, the most prominent feature within our model is the Newport‐Inglewood fault zone, which shows up as a high velocity anomaly (probably as a result of strain focusing; e.g., Papaleo et al, ) that emerges at depths of around 500 m. Although the average velocity structure of this feature has been imaged in previous tomography studies (e.g., Chang et al, ; Lin et al, ), the resolution that is now achieved with high‐frequency body waves allow us to illuminate some of its geometric variations. Figure shows a comparison between different cross‐sections of our velocity model cut perpendicular to the main fault trend.…”

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

“…Two large 4WD trucks were used for deployment and transporting the source, and their movements along the profile also generate surface wave energy. Many other studies find traffic noise to be a dominant ambient noise source at local scales (Behm et al, 2014;Riahi and Gerstoft, 2015;Chang et al, 2016), and specifically designed surveys are used for traffic noise imaging in urban areas (Cheng et al, 2016). For our data set, active shooting during the day is also regarded as a major contributor to the ambient 20 seismic wave field.…”

Humming Trains in Seismology: An Opportune Source for Probing the Shallow Crust

“…Studies soon followed on detection and characterization of these signals (Riahi and Gerstoft, 2015;Li et al, 2018;Green et al, 2017;Fuchs et al, 2018;Inbal et al, 2018) as well as source modeling (Lavoué et al, 2020). Earlier studies Nakata et al (2011); Quiros et al (2016); Chang et al (2016) proposed using traffic noise and seismic interferometry for both body-and surface-wave imaging. These studies were, however, limited to highly local sources of background cultural noise and near-surface applications.…”