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

Author(s):  
Zhengbo Li ◽  
Jie Zhou ◽  
Gaoxiong Wu ◽  
Jiannan Wang ◽  
Gongheng Zhang ◽  
...  

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.

2020 ◽  
Vol 222 (2) ◽  
pp. 1090-1092
Author(s):  
Lapo Boschi ◽  
Fabrizio Magrini ◽  
Fabio Cammarano ◽  
Mark van der Meijde

2007 ◽  
Vol 169 (3) ◽  
pp. 1239-1260 ◽  
Author(s):  
G. D. Bensen ◽  
M. H. Ritzwoller ◽  
M. P. Barmin ◽  
A. L. Levshin ◽  
F. Lin ◽  
...  

2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.


2021 ◽  
Author(s):  
Takashi Hirose ◽  
Hideki Ueda ◽  
Eisuke Fujita

<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>


2019 ◽  
Vol 219 (3) ◽  
pp. 1568-1589
Author(s):  
Lapo Boschi ◽  
Fabrizio Magrini ◽  
Fabio Cammarano ◽  
Mark van der Meijde

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.


Author(s):  
Hao Rao ◽  
Yinhe Luo ◽  
Kaifeng Zhao ◽  
Yingjie Yang

Summary Correlation of the coda of Empirical Green's functions from ambient noise can be used to reconstruct Empirical Green's function between two seismic stations deployed different periods of time. However, such method requires a number of source stations deployed in the area surrounding a pair of asynchronous stations, which limit its applicability in cases where there are not so many available source stations. Here, we propose an alternative method, called two-station C2 method, which uses one single station as a virtual source to retrieve surface wave phase velocities between a pair of asynchronous stations. Using ambient noise data from USArray as an example, we obtain the interstation C2 functions using our C2 method and the traditional cross-correlation functions (C1 functions). We compare the differences between the C1 and C2 functions in waveforms, dispersion measurements, and phase velocity maps. Our results show that our C2 method can obtain reliable interstation phase velocity measurements, which can be used in tomography to obtain reliable phase velocity maps. Our method can significantly improve ray path coverage from asynchronous seismic arrays and enhance the resolution in ambient noise tomography for areas between asynchronous seismic arrays.


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