Characterization and utilization of heterogeneous ambient noise field for imaging subsurface structure in the Itoshima Peninsula, Japan

2021 ◽  
Author(s):  
Tatsunori Ikeda ◽  
Takeshi Tsuji ◽  
Chisato Konishi ◽  
Hideki Saito
2018 ◽  
Vol 26 (02) ◽  
pp. 1850007 ◽  
Author(s):  
Qiulong Yang ◽  
Kunde Yang ◽  
Shunli Duan

Sea-surface wind agitation can be considered the dominant noise sources whose intensity relies on local wind speed during typhoon period. Noise source levels in previous researches may be unappreciated for all oceanic regions and should be corrected for modeling typhoon-generated ambient noise fields in deep ocean. This work describes the inversion of wind-driven noise source level based on a noise field model and experimental measurements, and the verification of the inverted noise source levels with experimental results during typhoon period. A method based on ray approach is presented for modeling underwater ambient noise fields generated by typhoons in deep ocean. Besides, acoustic field reciprocity is utilized to decrease the calculation amount in modeling ambient noise field. What is more, the depth dependence and the vertical directionality of noise field based on the modeling method and the Holland typhoon model are evaluated and analyzed in deep ocean. Furthermore, typhoons named “Soulik” in 2013 and “Nida” in 2016 passed by the receivers deployed in the western Pacific (WP) and the South China Sea (SCS). Variations in sound speed profile, bathymetry, and the related oceanic meteorological parameters are analyzed and taken into consideration for modeling noise field. Boundary constraint simulated annealing (SA) method is utilized to invert the three parameters of noise source levels and to minimize the objective function value. The prediction results with the inverted noise source levels exhibit good agreement with the measured experiment data and are compared with predicted results with other noise sources levels derived in previous researches.


Polar Science ◽  
2018 ◽  
Vol 17 ◽  
pp. 40-49 ◽  
Author(s):  
M.C. Sanjana ◽  
G. Latha ◽  
A. Thirunavukkarasu ◽  
R. Venkatesan

1988 ◽  
Vol 84 (S1) ◽  
pp. S122-S122
Author(s):  
Jim Rohr ◽  
Ray Glass ◽  
Brett D. Castile

2020 ◽  
Vol 12 (18) ◽  
pp. 2969
Author(s):  
Guoli Wu ◽  
Hefeng Dong ◽  
Ganpan Ke ◽  
Junqiang Song

Ambient noise carries abundant subsurface structure information and attracts ever-increasing attention in the past decades. However, there are lots of interference factors in the ambient noise in the real world, making the noise difficult to be utilized in seismic interferometry. The paper performs shear-wave tomography on a very short recording of ocean ambient noise with interference. An adapted eigenvalue-based filter is adopted as a pre-processing method to deal with the strong, directional interference problem. Beamforming and the noise crosscorrelation analyses show that the filter works well on the noise recorded by the array. Directional energy is significantly suppressed and the background diffuse component of the noise is relatively enhanced. The shear-wave tomography shows a 4-layer subsurface structure of the area covered by the array, with relatively homogeneous distribution of the shear-wave velocity values in the top three layers and a complicated structure in the bottom layer. Moreover, 3 high-velocity zones can be recognized in the bottom layer. The result is compared with several other tomography results using different methods and data. It demonstrates that, although the ambient noise used in this paper is very short and severely contaminated, a reasonable tomography result can be obtained by applying the adapted eigenvalue-based filter. Since it is the first application of the adapted eigenvalue-based filter in seismic tomography using ambient noise, the paper proves the effectiveness of this technique and shows the potential of the technique in ambient noise processing and passive seismic interferometry.


2012 ◽  
Author(s):  
Jianheng Lin ◽  
Pengfei Jiang ◽  
Baoyou Yin ◽  
JiaLiang Li ◽  
Li Ma

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