scholarly journals Erratum: On seismic ambient noise cross-correlation and surface-wave attenuation

2020 ◽  
Vol 222 (2) ◽  
pp. 1090-1092
Author(s):  
Lapo Boschi ◽  
Fabrizio Magrini ◽  
Fabio Cammarano ◽  
Mark van der Meijde
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Wave Attenuation ◽  
Seismic Ambient Noise ◽  
Noise Cross Correlation

scholarly journals On seismic ambient noise cross-correlation and surface-wave attenuation

2019 ◽  
Vol 219 (3) ◽  
pp. 1568-1589
Author(s):  
Lapo Boschi ◽  
Fabrizio Magrini ◽  
Fabio Cammarano ◽  
Mark van der Meijde
Keyword(s):  
Rayleigh Wave ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Wave Attenuation ◽  
Mathematical Expression ◽  
Seismic Ambient Noise ◽  
Multiplicative Factor ◽  
Cross Correlations ◽  
Noise Cross Correlation ◽  
Preliminary Application

SUMMARY We derive a theoretical relationship between the cross correlation of ambient Rayleigh waves (seismic ambient noise) and the attenuation parameter α associated with Rayleigh-wave propagation. In particular, we derive a mathematical expression for the multiplicative factor relating normalized cross correlation to the Rayleigh-wave Green’s function. Based on this expression, we formulate an inverse problem to determine α from cross correlations of recorded ambient signal. We conduct a preliminary application of our algorithm to a relatively small instrument array, conveniently deployed on an island. In our setup, the mentioned multiplicative factor has values of about 2.5–3, which, if neglected, could result in a significant underestimate of α. We find that our inferred values of α are reasonable, in comparison with independently obtained estimates found in the literature. Allowing α to vary with respect to frequency results in a reduction of misfit between observed and predicted cross correlations.

CC-FJpy: A Python Package for Extracting Overtone Surface-Wave Dispersion from Seismic Ambient-Noise Cross Correlation

2021 ◽  
Author(s):  
Zhengbo Li ◽  
Jie Zhou ◽  
Gaoxiong Wu ◽  
Jiannan Wang ◽  
Gongheng Zhang ◽  
...  
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Wave Dispersion ◽  
Ease Of Use ◽  
Processing Unit ◽  
Surface Wave Dispersion ◽  
Seismic Ambient Noise ◽  
Noise Cross Correlation ◽  
Python Package

Abstract In the past two decades, seismic ambient-noise cross correlation (CC) has been one of the most important technologies in seismology. Usually, only the fundamental-mode surface-wave dispersion was extracted from the ambient noise. Recently, with the frequency–Bessel transform (F-J) method, overtone dispersion can also be extracted from the ambient noise and it adds significant value in inversion. This method has also been verified to be effective for array seismic records of earthquake events. In this article, we describe our algorithm and a Python package called CC-FJpy. For the F-J method, we use the Nvidia’s graphics processing unit to accelerate the computation, which can achieve a 100-fold computational efficiency. We have encapsulated our experiences and technologies into CC-FJpy and tested the CC-FJpy by ambient-noise and earthquake data to ensure its speed and ease of use. Our open-source package CC-FJpy can benefit the development of surface-wave studies using ambient noise and make it easier to start with high-mode surface waves.

scholarly journals Surface wave group velocity in the Osaka sedimentary basin, Japan, estimated using ambient noise cross-correlation functions

2017 ◽  
Vol 69 (1) ◽  
Author(s):  
Kimiyuki Asano ◽  
Tomotaka Iwata ◽  
Haruko Sekiguchi ◽  
Kazuhiro Somei ◽  
Ken Miyakoshi ◽  
...  
Keyword(s):  
Surface Wave ◽  
Group Velocity ◽  
Correlation Functions ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Sedimentary Basin ◽  
Wave Group ◽  
Noise Cross Correlation

Passive estimation of scattering and intrinsic absorption parameters at 18 active volcanoes in Japan using envelopes of seismic ambient noise cross-correlation functions

2021 ◽  
Author(s):  
Takashi Hirose ◽  
Hideki Ueda ◽  
Eisuke Fujita
Keyword(s):  
Ambient Noise ◽  
Cross Correlation ◽  
Complex Structure ◽  
Estimation Method ◽  
Seismic Ambient Noise ◽  
Intrinsic Absorption ◽  
Noise Data ◽  
Volcanic Islands ◽  
Noise Cross Correlation ◽  
Active Volcanoes

<p>    Estimating seismic scattering and intrinsic absorption parameters, which are measures of medium heterogeneity, is important for understanding the complex structure in shallow regions of volcanoes. In recent years, seismic ambient noise cross-correlation functions (CCFs) have been used instead of records of natural earthquakes or active seismic experiments to estimate those parameters (e.g., Hirose et al., 2019; Hirose et al., 2020; van Dinther et al., 2020). This passive approach possibly allows us to estimate scattering and intrinsic absorption parameters in previously unmeasured regions and frequency bands. In this study, we apply the passive estimation method proposed by Hirose et al. (2019) to 18 active volcanoes in Japan and measure those parameters of Rayleigh waves. We used three-component seismic ambient noise data in the frequency bands of 0.5-1 Hz, 1-2 Hz, and 2-4 Hz at seismic stations of NIED, JMA, HSRI, and MFRI. Before computing CCFs, the temporal flattening technique (Weaver, 2011) was applied to ambient noise data for reducing the effect of temporal fluctuations in noise levels with retaining relative amplitudes among the stations. Daily CCFs of three components (ZZ, ZR, ZT) were computed by stacking 10-minutes-CCFs. We stacked daily CCFs over 1 year and computed mean squared envelopes by smoothing squared amplitude with 4 s (0.5-1 Hz), 2 s (1-2 Hz), or 1 s (2-4 Hz) long time windows. Scattering and intrinsic absorption parameters were estimated by modeling the space-time distributions of energy densities calculated from CCFs with 2D radiative transfer theory. Best-fit values of scattering mean free path at the 18 active volcanoes range between 1.0-4.6 km at 0.5-1Hz band, 0.7-2.9 km at 1-2 Hz band, and 0.9-2.9 km at 2-4 Hz band, respectively. These values are 2 orders of magnitude shorter than those in non-volcanic regions (e.g., Sato et al., 2012). Those of intrinsic absorption parameter range between 0.05-0.26 s<sup>-1</sup> at the 0.5-1 Hz band, 0.06-0.24 s<sup>-1</sup> at the 1-2 Hz band, and 0.06-0.32 s<sup>-1 </sup>at the 2-4 Hz band, respectively. They are at most one order of magnitude larger than those in the non-volcanic regions. Especially strong intrinsic attenuations are estimated at volcanic islands. Water-bearing layers at a depth of several hundred meters below these islands may cause such strong intrinsic attenuations. The frequency dependence of scattering attenuations is also strong at these volcanic islands, suggesting non-uniform structures that largely fluctuate along depths. The results of this study suggest that the passive estimation method of scattering and intrinsic absorption parameters proposed by Hirose et al. (2019) is applicable to various volcanoes. Comparing estimated values of these parameters at various volcanoes will improve our understanding of complex structure at the shallow regions of volcanoes. Moreover, the parameters estimated in this study will boost locating spatial distributions of seismic velocity and/or scattering property changes associated with volcanic activities at the 18 volcanoes.</p><p>Acknowledgments: We used seismograms recorded by Japan Meteorological Agency (JMA), Hot Springs Research Institute (HSRI) of Kanagawa Prefecture, and Mount Fuji Research Institute (MFRI), Yamanashi Prefectural Government.</p>

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