Spurious velocity changes caused by temporal variations in ambient noise frequency content

Ambient seismic noise cross-correlations are now being used to detect temporal variations of seismic velocity, which are typically on the order of 0.1 per cent. At this small level, temporal variations in the properties of noise sources can cause apparent velocity changes. For example, the spatial distribution and frequency content of ambient noise have seasonal variations due to the seasonal hemispherical shift of storms. Here, we show that if the stretching method is used to measure time-shifts, then the temporal variability of noise frequency content causes apparent velocity changes due to the changes in both amplitude and phase spectra caused by waveform stretching. With realistic seasonal variations of frequency content in the Los Angeles Basin, our numerical tests produce about 0.05 per cent apparent velocity change, comparable to what Meier et al. observed in the Los Angeles Basin. We find that the apparent velocity change from waveform stretching depends on time windows and station-pair distances, and hence it is important to test a range of these parameters to diagnose the stretching bias. Better understanding of spatiotemporal noise source properties is critical for more accurate and reliable passive monitoring.

[1]  Roel Snieder,et al.  Monitoring rapid temporal change in a volcano with coda wave interferometry , 2005 .

[2]  U. Meier,et al.  Detecting seasonal variations in seismic velocities within Los Angeles basin from correlations of ambient seismic noise , 2008 .

[3]  Bin Wang,et al.  Continuous subsurface velocity measurement with coda wave interferometry , 2008 .

[4]  John E. Vidale,et al.  Damage to the shallow Landers fault from the nearby Hector Mine earthquake , 2003, Nature.

[5]  Xiaodong Song,et al.  Temporal changes of surface wave velocity associated with major Sumatra earthquakes from ambient noise correlation , 2008, Proceedings of the National Academy of Sciences.

[6]  Thomas M. Daley,et al.  Preseismic velocity changes observed from active source monitoring at the Parkfield SAFOD drill site , 2008, Nature.

[7]  Richard C. Aster,et al.  Multidecadal Climate-induced Variability in Microseisms , 2008 .

[8]  Yingjie Yang,et al.  Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements , 2007 .

[9]  William L. Ellsworth,et al.  Monitoring velocity variations in the crust using earthquake doublets: An application to the Calaveras Fault, California , 1984 .

[10]  Morgan P. Moschetti,et al.  An explicit relationship between time‐domain noise correlation and spatial autocorrelation (SPAC) results , 2010 .

[11]  D. Rivet,et al.  Seismic evidence of nonlinear crustal deformation during a large slow slip event in Mexico , 2011 .

[12]  V. Tsai A model for seasonal changes in GPS positions and seismic wave speeds due to thermoelastic and hydrologic variations , 2011 .

[13]  Xu,et al.  Evidence of shallow fault zone strengthening after the 1992 M7.5 landers, california, earthquake , 1998, Science.

[14]  Toshifumi Matsuoka,et al.  Monitoring seismic velocity change caused by the 2011 Tohoku‐oki earthquake using ambient noise records , 2012 .

[15]  Zhongwen Zhan,et al.  Earthquake Centroid Locations Using Calibration from Ambient Seismic Noise , 2010 .

[16]  Y. Ben‐Zion,et al.  Temporal Changes of Shallow Seismic Velocity Around the Karadere-Düzce Branch of the North Anatolian Fault and Strong Ground Motion , 2006 .

[17]  L. Faenza,et al.  Variations of crustal elastic properties during the 2009 L'Aquila earthquake inferred from cross‐correlations of ambient seismic noise , 2011 .

[18]  P. Silver,et al.  Migration of seismic scatterers associated with the 1993 Parkfield aseismic transient event , 2003, Nature.

[19]  Daniel E. McNamara,et al.  Ambient Noise Levels in the Continental United States , 2004 .

[20]  Michel Campillo,et al.  A study of the seismic noise from its long-range correlation properties , 2006 .

[21]  G. Beroza,et al.  Seismic velocity reductions caused by the 2003 Tokachi‐Oki earthquake , 2007 .

[22]  Michel Campillo,et al.  Towards forecasting volcanic eruptions using seismic noise , 2007, 0706.1935.

[23]  Florent Brenguier,et al.  Assessment of resolution and accuracy of the Moving Window Cross Spectral technique for monitoring crustal temporal variations using ambient seismic noise , 2011 .

[24]  Y. Ben‐Zion,et al.  Spatiotemporal variations of crustal anisotropy from similar events in aftershocks of the 1999 M7.4 İzmit and M7.1 Düzce, Turkey, earthquake sequences , 2005 .

[25]  Michel Deguy Clayton , 2022, Po&sie.

[26]  F. Brenguier,et al.  Postseismic Relaxation Along the San Andreas Fault at Parkfield from Continuous Seismological Observations , 2008, Science.

[27]  Michel Campillo,et al.  Real time monitoring of relative velocity changes using ambient seismic noise at the Piton de la Fournaise volcano (La Réunion) from January 2006 to June 2007 , 2009 .

[28]  Roel Snieder,et al.  Coda Wave Interferometry for Estimating Nonlinear Behavior in Seismic Velocity , 2002, Science.

[29]  Christoph Sens-Schönfelder,et al.  Passive image interferometry and seasonal variations of seismic velocities at Merapi Volcano, Indonesia , 2006 .

[30]  Philippe Roux,et al.  Stability of monitoring weak changes in multiply scattering media with ambient noise correlation: laboratory experiments. , 2009, The Journal of the Acoustical Society of America.