A Method for Noise Source Levels Inversion with Underwater Ambient Noise Generated by Typhoon in Deep Ocean

2018 ◽  
Vol 26 (02) ◽  
pp. 1850007 ◽  
Author(s):  
Qiulong Yang ◽  
Kunde Yang ◽  
Shunli Duan
Keyword(s):  
Ambient Noise ◽  
Noise Source ◽  
Surface Wind ◽  
Deep Ocean ◽  
Noise Sources ◽  
Noise Field ◽  
Sea Surface Wind ◽  
Source Level ◽  
The Western Pacific ◽  
Good Agreement

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.

scholarly journals Estimation of the Noise Source Level of a Commercial Ship Using On-Board Pressure Sensors

2021 ◽  
Vol 11 (3) ◽  
pp. 1243
Author(s):  
Hongseok Jeong ◽  
Jeung-Hoon Lee ◽  
Yong-Hyun Kim ◽  
Hanshin Seol
Keyword(s):  
Noise Source ◽  
Pressure Sensors ◽  
Low Frequency ◽  
Frequency Resolution ◽  
Source Strength ◽  
Recording Time ◽  
Source Level ◽  
The One ◽  
Hydrophone Array ◽  
Good Agreement

The dominant underwater noise source of a ship is known to be propeller cavitation. Recently, attempts have been made to quantify the source strength using on-board pressure sensors near the propeller, as this has advantages over conventional noise measurement. In this study, a beamforming method was used to estimate the source strength of a cavitating propeller. The method was validated against a model-scale measurement in a cavitation tunnel, which showed good agreement between the measured and estimated source levels. The method was also applied to a full-scale measurement, in which the source level was measured using an external hydrophone array. The estimated source level using the hull pressure sensors showed good agreement with the measured one above 400 Hz, which shows potential for noise monitoring using on-board sensors. A parametric study was carried out to check the practicality of the method. From the results, it was shown that a sufficient recording time is required to obtain a consistent level at high frequencies. Changing the frequency resolution had little effect on the result, as long as enough data were provided for the one-third octave band conversion. The number of sensors affected the mid- to low-frequency data.

The Research of the Characteristics of the Ocean Ambient Noise Under Varying Environment

2017 ◽  
Vol 25 (02) ◽  
pp. 1750021 ◽  
Author(s):  
Qianchu Zhang ◽  
Xinyi Guo ◽  
Li Ma
Keyword(s):  
Noise Intensity ◽  
Ambient Noise ◽  
Random Noise ◽  
Grazing Angle ◽  
Infinite Plane ◽  
Noise Sources ◽  
Spatial Properties ◽  
Noise Field ◽  
Varying Environment ◽  
Directivity Characteristics

The parabolic equation approximation method is applied to build the model of the vertical spatial correlation and vertical directivity characteristics of ocean ambient noise under varying environment. The random noise sources which have the same intensity are represented by uncorrelated monopoles distributed uniformly over an infinite plane located a certain depth below the sea surface. The spatial properties of noise field are analyzed under the varying ocean environments which are the slope, seamount and varying sound speed profiles. According to the results of simulation, the varying environment changes the propagating paths of noise, so the spatial properties (correlation and directionality) of the noise field change. When the remote noise intensity increases, the noise intensity in low grazing angle and the vertical correlations of nose field become stronger. At last, the experiment’s results support the results of simulation.

Modeling ambient noise distributions for surface wave imaging based on full waveform inversion 

2021 ◽  
Author(s):  
Changjiang Zhou ◽  
Jianghai Xia ◽  
Feng Cheng ◽  
Jingyin Pang ◽  
Xinhua Chen
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Noise Source ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Noise Sources ◽  
Wave Velocities ◽  
Full Waveform ◽  
Wave Imaging

<p>Abundant noise sources in urban area has been widely utilized for subsurface investigations based on the seiemic interferometry. Reliable dispersion extraction between two seismic stations is an essential basis of surface wave imaging. Noise source directivity has become an inescapable obstacle and a main concern for passive seismic surveys: it basically breaks the physics of Green’s function retrieval in travel-time tomography; Moreover, the azimuthal effect of ambient noise sources would inherently cause different levels of early arrival on cross-correlation functions, so that the apparent velocity of surface wave could be overestimated in multichannel slant stackings.</p><p> </p><p>Instead of the conventional frequency-time analysis, which aims to extract the apparent dispersions of phase/group velocity between seismic stations, we proposed a method to jointly invert noise source distributions and the corresponding unbiased surface wave velocities based on the theoretical framework of full waveform ambient noise inversion. Waveform itself could intrinsically contains the features of travel-time, energy and asymmetry of ambient noise cross correlation functions (NCF). And they could in return map the resulted NCF into the noise source distributions and velocity structures. The L2 norm of cross-correlating waveform misfits was taken as the objective function to conduct gradient based inversion (i.e. the L-BFGS algorithm). We parametrized the noise source distributions as a temporally ensemble averaged model, which was discretized as a spatially plane grid of normalized source strength. The surface wave velocity model was approximated as the straight-ray interstation velocity. The two kinds of variants were decoupled in waveform misfit function by their corresponding partial derivatives to iteratively update the model space.</p><p> </p><p>The effectiveness of source-velocity joint imaging using above full waveform inversion work flow was qualified by both the synthetic test and the applied research in Hangzhou urban area. The inverted noise source model was comparable with the urban traffic- and construction- noise distributions. And the truthful surface wave velocities were achieved considering the constraint of noise source distributions, they were also prior constrained and later verified by local borehole datasets.</p>

Rapid Global Finite-Frequency Ambient Noise Source Inversion

2020 ◽  
Author(s):  
Jonas Igel ◽  
Laura Ermert ◽  
Andreas Fichtner
Keyword(s):  
Ambient Noise ◽  
Noise Source ◽  
Computational Cost ◽  
User Interaction ◽  
Real Data ◽  
Source Distribution ◽  
Source Inversion ◽  
Finite Frequency ◽  
Noise Sources ◽  
Cross Correlations

<p>Common assumptions in ambient noise seismology such as Green’s function retrieval and equipartitioned wavefields are often not met in the Earth. Full waveform ambient noise tomography methods are free of such assumptions, as they implement knowledge of the time- and space-dependent ambient noise source distribution, whilst also taking finite-frequency effects into account. Such methods would greatly simplify near real-time monitoring of the sub-surface. Additionally, the distribution of the secondary microseisms could act as a new observable of the ocean state since its mechanism is well understood (e.g. Ardhuin et al., 2011).</p><p>To efficiently forward-model global noise cross-correlations we implement (1) pre-computed high-frequency wavefields obtained using, for example, AxiSEM (Nissen-Meyer et al., 2014), and (2) spatially variable grids, both of which greatly reduce the computational cost. Global cross-correlations for any source distribution can be computed within a few seconds in the microseismic frequency range (up to 0.2 Hz). Similarly, we can compute the finite-frequency sensitivity kernels which are then used to perform a gradient-based iterative inversion of the power-spectral density of the noise source distribution. We take a windowed logarithmic energy ratio of the causal and acausal branches of the cross-correlations as measurement, which is largely insensitive to unknown 3D Earth structures.</p><p>Due to its parallelisation on a cluster, our inversion tool is able to rapidly invert for the global microseismic noise source distribution with minimal required user interaction. Synthetic and real data inversions show promising results for noise sources in the North Atlantic with the structure and spatial distribution resolved at scales of a few hundred kilometres. Finally, daily noise sources maps could be computed by combining our inversion tool with a daily data download and processing toolkit.</p>

scholarly journals Retrieval of Sea Surface Wind Speeds from Gaofen-3 Full Polarimetric Data

2019 ◽  
Vol 11 (7) ◽  
pp. 813 ◽  
Author(s):  
Tianyu Zhang ◽  
Xiao-Ming Li ◽  
Qian Feng ◽  
Yongzheng Ren ◽  
Yingni Shi
Keyword(s):  
Wind Speed ◽  
Linear Function ◽  
Surface Wind ◽  
Calibration Factor ◽  
Sea Surface ◽  
Radiometric Calibration ◽  
Polarization Data ◽  
Sea Surface Wind ◽  
Absolute Bias ◽  
Good Agreement

In this paper, the sea surface wind speed (SSWS) retrieval from Gaofen-3 (GF-3) quad-polarization stripmap (QPS) data in vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH) polarizations is investigated in detail based on 3170 scenes acquired from October 2016 to May 2018. The radiometric calibration factor of the VV polarization data is examined first. This calibration factor generally meets the requirement of SSWS retrieval accuracy with an absolute bias of less than 0.5 m/s but shows highly dispersed characteristics. These results lead to SSWS retrievals with a small bias of 0.18 m/s, but a rather high root mean square error (RMSE) of 2.36 m/s when compared with the ERA-Interim reanalysis model data. Two refitted polarization ratio (PR) models for the QPS HH polarization data are presented. Based on a combination of the incidence angle-dependent and azimuth angle-dependent PR model and CMOD5.N, the SSWS derived from the QPS HH data shows a bias of 0.07 m/s and an RMSE of 2.26 m/s relative to the ERA-Interim reanalysis model wind speed. A linear function relating SSWS and the normalized radar cross section (NRCS) of QPS VH data is derived. The SSWS data retrieved from the QPS VH data show good agreement with the WindSat SSWS data, with a bias of 0.1 m/s and an RMSE of 2.02 m/s. We also apply the linear function to the GF-3 Wide ScanSAR data acquired for the typhoon SOULIK, which yields very good agreement with the model results. A comparison of SSWS retrievals among three different polarization datasets is also presented. The current study and our previous work demonstrate that the general accuracy of the SSWS retrieval based on GF-3 QPS data has an absolute bias of less than 0.3 m/s and an RMSE of 2.0 ± 0.2 m/s relative to various datasets. Further improvement will depend on dedicated radiometric calibration efforts.

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