Spectral analysis of seismic noise induced by rivers: A new tool to monitor spatiotemporal changes in stream hydrodynamics

Burtin, Arnaud; Bollinger, Laurent; Vergne, Jérôme; Cattin, Rodolphe; Nábělek, J.

doi:10.1029/2007jb005034

Cited by 159 publications

(236 citation statements)

References 37 publications

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“…For example, a wide range of ambient noise cross-correlation studies make use of surface-to-surface Green's functions, either explicitly or implicitly (e.g., Shapiro et al, 2005;Lin et al, 2008;Prieto and Beroza, 2008). Other studies of near-surface phenomena such as river sediment transport (Burtin et al, 2008;Tsai et al, 2012), sea ice (Kedar et al, 2008;Stutzmann et al, 2009;Tsai and McNamara, 2011), landslides (Hibert et al, 2011;Ekstrom and Stark, 2013), and volcanic tremor (McNutt and Nishimura, 2008) also make use of surface-to-surface surfacewave amplitudes. For such studies of near-surface seismic sources, the tabulation of the coefficients N L and N R ij will provide a useful estimate of the expected ground-motion amplitudes, which is a first step to utilize the seismic data to constrain physical processes.…”

Section: Resultsmentioning

confidence: 99%

“…In the last decade, there has been an increasing interest in using seismology to quantitatively analyze near-surface processes such as landslides (e.g., Ekstrom and Stark, 2013), glaciers (e.g., Tsai and Ekstrom, 2007), rivers (e.g., Burtin et al, 2008), and volcanic tremor (e.g., McNutt and Nishimura, 2008). In parallel, there has also been an exponential growth in ambient noise cross-correlation applications in which a given station-pair cross correlation is assumed to be related to the surface-wave Green's function (e.g., Snieder, 2004;Shapiro et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Green's Functions for Surface Waves in a Generic Velocity Structure

Tsai¹,

Atiganyanun²

2014

Bulletin of the Seismological Society of America

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Methodologies for calculating surface-wave velocities and the associated displacement/stress eigenfunctions and Green's functions have been well established for many decades. However, to our knowledge, no one has ever documented a quantitative evaluation of these properties for commonly used empirical scalings. For example, it is currently not possible to take a given power-law dependence of shear-wave velocity on depth and look up the corresponding dependence of phase velocity on frequency, or Green's function surface displacement. We address this gap in the literature and here provide explicit quantitatively accurate expressions for phase velocities and Green's function amplitudes for a few commonly used empirical formulas for near-surface velocity structure. These exact expressions are found to be immediately useful in applications that use shallow phase velocities and also in applications that interpret seismic amplitudes or amplitude ratios from near-surface processes such as fluvial transport, icequakes, landslides, and volcanic tremor.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Green's Functions for Surface Waves in a Generic Velocity Structure

Tsai¹,

Atiganyanun²

2014

Bulletin of the Seismological Society of America

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“…Hibert et al, 2011;Lacroix and Helmstetter, 2011), debris flows (Burtin et al, 2014) and bed load transport in rivers (e.g. Burtin et al, 2008;Gimbert et al, 2014). This emerging research field and the use of seismic noise crosscorrelation methods to investigate the states and changes of subsurface conditions are referred to as environmental seismology (Larose et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Seismic monitoring of small alpine rockfalls – validity, precision and limitations

Dietze

Mohadjer²,

Turowski

et al. 2017

Earth Surf. Dynam.

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Abstract. Rockfall in deglaciated mountain valleys is perhaps the most important post-glacial geomorphic process for determining the rates and patterns of valley wall erosion. Furthermore, rockfall poses a significant hazard to inhabitants and motivates monitoring efforts in populated areas. Traditional rockfall detection methods, such as aerial photography and terrestrial laser scanning (TLS) data evaluation, provide constraints on the location and released volume of rock but have limitations due to significant time lags or integration times between surveys, and deliver limited information on rockfall triggering mechanisms and the dynamics of individual events. Environmental seismology, the study of seismic signals emitted by processes at the Earth's surface, provides a complementary solution to these shortcomings. However, this approach is predominantly limited by the strength of the signals emitted by a source and their transformation and attenuation towards receivers. To test the ability of seismic methods to identify and locate small rockfalls, and to characterise their dynamics, we surveyed a 2.16 km 2 large, near-vertical cliff section of the Lauterbrunnen Valley in the Swiss Alps with a TLS device and six broadband seismometers. During 37 days in autumn 2014, 10 TLS-detected rockfalls with volumes ranging from 0.053 ± 0.004 to 2.338 ± 0.085 m 3 were independently detected and located by the seismic approach, with a deviation of 81 +59 −29 m (about 7 % of the average inter-station distance of the seismometer network). Further potential rockfalls were detected outside the TLS-surveyed cliff area. The onset of individual events can be determined within a few milliseconds, and their dynamics can be resolved into distinct phases, such as detachment, free fall, intermittent impact, fragmentation, arrival at the talus slope and subsequent slope activity. The small rockfall volumes in this area require significant supervision during data processing: 2175 initially picked potential events reduced to 511 potential events after applying automatic rejection criteria. The 511 events needed to be inspected manually to reveal 19 short earthquakes and 37 potential rockfalls, including the 10 TLS-detected events. Rockfall volume does not show a relationship with released seismic energy or peak amplitude at this spatial scale due to the dominance of other, process-inherent factors, such as fall height, degree of fragmentation, and subsequent talus slope activity. The combination of TLS and environmental seismology provides, despite the significant amount of manual data processing, a detailed validation of seismic detection of small volume rockfalls, and revealed unprecedented temporal, spatial and geometric details about rockfalls in steep mountainous terrain.Published by Copernicus Publications on behalf of the European Geosciences Union.

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“…In the past decades, the potential of using acoustic and seismic signals for geomorphological gain has been explored in several studies (e.g. Bäzinger and Burch, 1990;Taniguchi et al, 1992;Govi et al, 1993;Rickenmann and McArdell, 2007;Burtin et al, 2008Burtin et al, , 2010Burtin et al, , 2011Gray et al, 2010;Hsu et al, 2011;Schmandt et al, 2013;Díaz et al, 2014;Roth et al, 2014;Chao et al, 2015;Barrière et al, 2015a). The approach makes use of the fact that the displacement of mass at the Earth's surface generates elastic seismic waves that propagate through the subsurface and can be recorded by acoustic or seismic sensors.…”

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confidence: 99%

Seismic monitoring of torrential and fluvial processes

2016

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Abstract. In seismology, the signal is usually analysed for earthquake data, but earthquakes represent less than 1 % of continuous recording. The remaining data are considered as seismic noise and were for a long time ignored. Over the past decades, the analysis of seismic noise has constantly increased in popularity, and this has led to the development of new approaches and applications in geophysics. The study of continuous seismic records is now open to other disciplines, like geomorphology. The motion of mass at the Earth's surface generates seismic waves that are recorded by nearby seismometers and can be used to monitor mass transfer throughout the landscape. Surface processes vary in nature, mechanism, magnitude, space and time, and this variability can be observed in the seismic signals. This contribution gives an overview of the development and current opportunities for the seismic monitoring of geomorphic processes. We first describe the common principles of seismic signal monitoring and introduce time-frequency analysis for the purpose of identification and differentiation of surface processes. Second, we present techniques to detect, locate and quantify geomorphic events. Third, we review the diverse layout of seismic arrays and highlight their advantages and limitations for specific processes, like slope or channel activity. Finally, we illustrate all these characteristics with the analysis of seismic data acquired in a small debris-flow catchment where geomorphic events show interactions and feedbacks. Further developments must aim to fully understand the richness of the continuous seismic signals, to better quantify the geomorphic activity and to improve the performance of warning systems. Seismic monitoring may ultimately allow the continuous survey of erosion and transfer of sediments in the landscape on the scales of external forcing.

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Spectral analysis of seismic noise induced by rivers: A new tool to monitor spatiotemporal changes in stream hydrodynamics

Cited by 159 publications

References 37 publications

Green's Functions for Surface Waves in a Generic Velocity Structure

Green's Functions for Surface Waves in a Generic Velocity Structure

Seismic monitoring of small alpine rockfalls – validity, precision and limitations

Seismic monitoring of torrential and fluvial processes

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