Automatic picking of multi-mode dispersion curves using CNN-based machine learning

Author(s):  
Li Ren ◽  
Fuchun Gao ◽  
Yulang Wu ◽  
Paul Williamson ◽  
Wenlong Wang ◽  
...  
2022 ◽  
Vol 2148 (1) ◽  
pp. 012047
Author(s):  
Feng Gong ◽  
Xiaofei Chen ◽  
Youhua Fan ◽  
Xuefeng Liu ◽  
Haibing Tang

Abstract Traditional multi-mode dispersion curve inversion requires correct mode discrimination. However, when the stratum contains complex structures such as low-speed soft interlayer or high-speed hard interlayer, the dispersion curve may show phenomena such as “mode kissing” and “mode jumping”, which can easily cause mode misjudgment and lead to erroneous inversion results. Based on the “secular function”, this paper constructs a new type of objective function applied to the inversion of dispersion curve. This objective function does not require prior mode discrimination, which effectively solves the “mode misjudgment” problem of multi-mode dispersion curve inversion. The joint inversion of Rayleigh and Love dispersion curves extracted from ambient seismic noise is used to improve the constraint of the inversion and avoid the inversion falling into a local minimum in the case of a large-scale search of parameters. Finally, a numerical simulation was performed to verify the feasibility of the new inversion method.


Ultrasonics ◽  
2016 ◽  
Vol 68 ◽  
pp. 80-88 ◽  
Author(s):  
Bouko Vogelaar ◽  
Michael Golombok ◽  
Xander Campman

2018 ◽  
Vol 26 (17) ◽  
pp. 22100 ◽  
Author(s):  
Ang Liu ◽  
Tianying Lin ◽  
Hailong Han ◽  
Xiaopei Zhang ◽  
Ze Chen ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document