Small‐scale geophysics inversion using surface wave extracted from noise cross correlation

2006 ◽  
Vol 119 (5) ◽  
pp. 3307-3307
Author(s):  
Pierre Gouedard ◽  
Philippe Roux ◽  
Michel Campillo
Keyword(s):  
Surface Wave ◽  
Cross Correlation ◽  
Small Scale ◽  
Noise Cross Correlation

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

S-waves profiles from noise cross correlation at small scale

2009 ◽  
Vol 105 (3-4) ◽  
pp. 161-170 ◽  
Author(s):  
C. Nunziata ◽  
G. De Nisco ◽  
G.F. Panza
Keyword(s):  
Cross Correlation ◽  
Small Scale ◽  
S Waves ◽  
Noise Cross Correlation

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.

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