Improving temporal resolution in ambient noise monitoring of seismic wave speed

Hadziioannou, Céline; Larose, Éric; Baig, Adam; Roux, Philippe; Campillo, Míchel

doi:10.1029/2011jb008200

Cited by 71 publications

(74 citation statements)

References 40 publications

(55 reference statements)

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“…2). Coda parts of NCFs in which scattered surface wave arrived were used to measure dt to minimize effects of the time-dependent distributions of ambient noise sources (e.g., Stehly et al 2008;Hadziioannou et al 2011). The coda parts were defined from −50 to −20 s and 20 to 50 s to avoid direct surface waves for our dt measurements (Fig.…”

Section: Data and Analysismentioning

confidence: 99%

Response of hydrothermal system to stress transients at Lassen Volcanic Center, California, inferred from seismic interferometry with ambient noise

Taira

Brenguier

2016

Earth Planets Space

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Abstract:Time-lapse monitoring of seismic velocity at volcanic areas can provide unique insight into the property of hydrothermal and magmatic fluids and their temporal variability. We established a quasi real-time velocity monitoring system by using seismic interferometry with ambient noise to explore the temporal evolution of velocity in the Lassen Volcanic Center, Northern California. Our monitoring system finds temporal variability of seismic velocity in response to stress changes imparted by an earthquake and by seasonal environmental changes. Dynamic stress changes from a magnitude 5.7 local earthquake induced a 0.1 % velocity reduction at a depth of about 1 km. The seismic velocity susceptibility defined as ratio of seismic velocity change to dynamic stress change is estimated to be about 0.006 MPa −1 , which suggests the Lassen hydrothermal system is marked by high-pressurized hydrothermal fluid. By combining geodetic measurements, our observation shows that the long-term seismic velocity fluctuation closely tracks snowinduced vertical deformation without time delay, which is most consistent with an hydrological load model (either elastic or poroelastic response) in which surface loading drives hydrothermal fluid diffusion that leads to an increase of opening of cracks and subsequently reductions of seismic velocity. We infer that heated-hydrothermal fluid in a vapor-dominated zone at a depth of 2-4 km range is responsible for the long-term variation in seismic velocity

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Section: Data and Analysismentioning

confidence: 99%

Response of hydrothermal system to stress transients at Lassen Volcanic Center, California, inferred from seismic interferometry with ambient noise

Taira

Brenguier

2016

Earth Planets Space

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“…Relative velocity changes ( v/v = δv) were measured with the stretching method (Sens-Schönfelder & Wegler 2006;Hadziioannou et al 2009Hadziioannou et al , 2011, which compares individual ACFs (φ(t i , τ )) calculated from noise signal that was recorded at time t i to stretched or compressed versions of a long-term averaged reference ACF ξ (τ ). Here τ indicates the lapse time of the ACF, that is, the traveltime of waves.…”

Section: Data a N D M E T H O Dmentioning

confidence: 99%

Field observations of seismic velocity changes caused by shaking-induced damage and healing due to mesoscopic nonlinearity

et al. 2016

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“…The principle of this method is to retrieve signals, which contain the structural response of the medium from noise cross-correlations (LOBKIS and WEAVER 2001;DERODE et al 2003;WAPENAAR 2004;SNIEDER 2004a;ROUX et al 2005, among others). Besides the cross-correlations between two stations, auto-correlations of the noise wave field recorded at one station can be employed to monitor medium changes (e.g., SENS-SCHÖ NFELDER and WE- GLER 2006;HOBIGER et al 2014;NAKAHARA 2015). There are many advantages of using ambient seismic noise to monitor temporal variations of the Earth structure.…”

Section: Introductionmentioning

confidence: 99%

“…In the past decade, monitoring medium changes gained increasing interest due to the recent progress in understanding the seismic noise wave field and its usage for monitoring (e.g., SNIEDER 2004b;BRENGUIER et al 2008a;HADZIIOANNOU et al 2011) and imaging studies (e.g., CAMPILLO and PAUL 2003;SHAPIRO et al 2005). The seismic noise wave field can be used for monitoring structural changes whenever the noise wave field does not change its characteristics (ZHAN et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…And third, it is a non-invasive or destructive monitoring method. For these reasons, this method has been applied in many studies, including earthquake monitoring (WEGLER and SENS-SCHONFELDER 2007;Brenguier et al 2008b;OHMI et al 2008;WEGLER et al 2009;MINATO et al 2012;ZACCARELLI et al 2011;HOBIGER et al 2014) and volcano monitoring (e.g., SENS-SCHÖ NFELDER and WEGLER 2006;MORDRET et al 2010).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Subtle Hydromechanical Medium Changes Caused By a Small-Magnitude Earthquake Swarm in NE Brazil

D’Hour

Schimmel

Nascimento

et al. 2015

Pure Appl. Geophys.

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Abstract-Ambient noise correlation analyses are largely used in seismology to map heterogeneities and to monitor the temporal evolution of seismic velocity changes associated mostly with stress field variations and/or fluid movements. Here we analyse a small earthquake swarm related to a main m R 3.7 intraplate earthquake in North-East of Brazil to study the corresponding post-seismic effects on the medium. So far, post-seismic effects have been observed mainly for large magnitude events. In our study, we show that we were able to detect localized structural changes even for a small earthquake swarm in an intraplate setting. Different correlation strategies are presented and their performances are also shown. We compare the classical auto-correlation with and without pre-processing, including 1-bit normalization and spectral whitening, and the phase auto-correlation. The worst results were obtained for the pre-processed data due to the loss of waveform details. The best results were achieved with the phase cross-correlation which is amplitude unbiased and sensitive to small amplitude changes as long as there exist waveform coherence superior to other unrelated signals and noise. The analysis of 6 months of data using phase auto-correlation and cross-correlation resulted in the observation of a progressive medium change after the major recorded event. The progressive medium change is likely related to the swarm activity through opening new path ways for pore fluid diffusion. We further observed for the auto-correlations a lag time frequency-dependent change which likely indicates that the medium change is localized in depth. As expected, the main change is observed along the fault.

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Improving temporal resolution in ambient noise monitoring of seismic wave speed

Cited by 71 publications

References 40 publications

Response of hydrothermal system to stress transients at Lassen Volcanic Center, California, inferred from seismic interferometry with ambient noise

Response of hydrothermal system to stress transients at Lassen Volcanic Center, California, inferred from seismic interferometry with ambient noise

Field observations of seismic velocity changes caused by shaking-induced damage and healing due to mesoscopic nonlinearity

Detection of Subtle Hydromechanical Medium Changes Caused By a Small-Magnitude Earthquake Swarm in NE Brazil

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