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.

[1] We perform a time-lapse analysis of Rayleigh and Love wave anisotropy above an underground gas storage facility in the Paris Basin. The data were acquired with a three-component seismic array deployed during several days in April and November 2010. Phase velocity and back azimuth of Rayleigh and Love waves are measured in the frequency range 0.2-1.1 Hz using a three-component beamforming algorithm. In both snapshots, higher-surface wave modes start dominating the signal above 0.4 Hz with a concurrent increase in back azimuth ranges. We fit anisotropy parameters to the array detections above 0.4 Hz using a bootstrap approach which also provides estimation uncertainty and enables significance testing. The isotropic phase velocity dispersion for Love and Rayleigh waves match for both snapshots. We also observe a stable fast direction of NNW-SSE for Love and Rayleigh waves which is aligned with the preferred orientation of known shallow (<300 m) and deeper ( 1000 m) fault systems in the area, as well as the maximum horizontal stress orientation. At lower frequencies corresponding to deeper parts of the basin, the anisotropic parameters exhibit higher magnitude in the November data. This may perhaps be caused by the higher pore pressure changes in the gas reservoir in that depth range.Citation: Riahi, N., G. Bokelmann, P. Sala, and E. H. Saenger (2013), Time-lapse analysis of ambient surface wave anisotropy: A three-component array study above an underground gas storage,

No abstract

We develop a model-free technique to identify weak sources within dense sensor arrays using graph clustering. No knowledge about the propagation medium is needed except that signal strengths decay to insignificant levels within a scale that is shorter than the aperture. We then reinterpret the spatial coherence matrix of a wave field as a matrix whose support is a connectivity matrix of a graph with sensors as vertices. In a dense network, well-separated sources induce clusters in this graph. The geographic spread of these clusters can serve to localize the sources. The support of the covariance matrix is estimated from limited-time data using a hypothesis test with a robust phase-only coherence test statistic combined with a physical distance criterion. The latter criterion ensures graph sparsity and thus prevents clusters from forming by chance. We verify the approach and quantify its reliability on a simulated dataset. The method is then applied to data from a dense 5200 element geophone array that blanketed 7 km ×10 km of the city of Long Beach (CA). The analysis exposes a helicopter traversing the array and oil production facilities.