Qiulong Yang, Kunde Yang, Shunli Duan, Yuanliang Ma. Statistics of underwater ambient noise at high sea states arisen from typhoon out zones in the Philippine Sea and South China Sea[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-022-1991-7
Citation:
Qiulong Yang, Kunde Yang, Shunli Duan, Yuanliang Ma. Statistics of underwater ambient noise at high sea states arisen from typhoon out zones in the Philippine Sea and South China Sea[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-022-1991-7
Qiulong Yang, Kunde Yang, Shunli Duan, Yuanliang Ma. Statistics of underwater ambient noise at high sea states arisen from typhoon out zones in the Philippine Sea and South China Sea[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-022-1991-7
Citation:
Qiulong Yang, Kunde Yang, Shunli Duan, Yuanliang Ma. Statistics of underwater ambient noise at high sea states arisen from typhoon out zones in the Philippine Sea and South China Sea[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-022-1991-7
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2.
Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an 710072, China
3.
Key Laboratory of Marine Environmental Information Technology, Ministry of Natural Resources, Tianjin 300171, China
Funds:
The Project of Global Change and Air-Sea Interaction under contract No. D5120210106; the Open Fund Project of Key Laboratory of Marine Environmental Information Technology, Ministry of Natural Resources of the People’s Republic of China under contract No. D5110200611; the Fundamental Research Funds for the Central Universities under contract No. 3102019HHZY030011; the China Postdoctoral Science Foundation under contract No. 2019M663822; the National Natural Science Foundation of China under contract Nos 11574251 and 11704313.
Oceanic noise is the background interference in sonar performance prediction and evaluation at high sea states. Statistics of underwater ambient noise during typhoon Soulik and Nida were analyzed on the basis of experimental measurements conducted in a deep area of the Philippine Sea and the South China Sea. Generated linear regression, frequency correlation matrix (FCM), Burr distribution and Gumbel distribution were described for the analysis of correlation with environmental parameters including wind speed (WS), significant wave height (SWH), and the inter-frequency relationship and probability density function of noise levels (NLs). When the typhoons were quite close to the receivers, the increment of NLs exceeded 10 dB. Whilst ambient noise was completely dominated by wind agitation, NLs were proportional to the cubic and quintic functions of WS and SWH, respectively. The fitted results between NLs and oceanic parameters were different for “before typhoon” and “after typhoon”. The fitted slopes of linear regression showed a linear relationship with the logarithm of frequency. The average observed typhoon-generated NLs were 5 dB lower than the Wenz curve at the same wind force due to the insufficiently developed sea state or the delay between NLs and WS. The cross-correlation coefficient of FCM, which can be utilized in the identification of noise sources in different bands, exceeded 0.8 at frequencies higher than 250 Hz. Furthermore, standard deviation increased with frequency. The kurtosis was equal to 3 at >400 Hz approximately. The characteristics of NLs showed good agreement with the results of FCM.
Figure 1. Experimental measurement and received noise level (NL). a. Schematic of experiment; b. wind field and trace of “Soulik”; c. wind field and trace of “Nida”; d. ocean meteorological parameters; e. NL, in WP2013; f. NL, in SCS2016.
Figure 2. Relationship between noise levels (NLs) and local wind speed (WS). a. 500 Hz in WP2013; b. 1 000 Hz in WP2013; c. frequency dependence in WP2013; d. 500 Hz in SCS2016; e. 1 000 Hz in SCS2016; f. frequency dependence in SCS2016.
Figure 3. Relationship between noise levels (NLs) and local significant wave height (SWH). a. 500 Hz in WP2013; b. 1 000 Hz in WP2013; c. frequency dependence in WP2013; d. 500 Hz in SCS2016; e. 1 000 Hz in SCS2016; f. frequency dependence in SCS2016.
Figure 4. Experimental noise levels (NLs) at different wind force scales and comparison with Wenz curve. a. NL in WP2013; b. WF-3, WP2013; c. WF-5, WP2013; d. NL in SCS2016; e. WF-3, in SCS2016; f. WF-5, in SCS2016.
Figure 5. Precipitation rate in experiments.
Figure 6. Fitting results between noise levels (NLs) and noise level (NL) at 1 kHz during typhoon periods in experiments. a. 630Hz, WP2013; b. 3150Hz, WP2013; c. 630Hz, SCS2016; d. 3150Hz, SCS2016.
Figure 7. Correlation coefficient of noise levels during typhoon period in experiments. a. WP2013; b. SCS2016.
Figure 8. Histogram of National Centers for Environmental Prediction (NCEP) wind speed (WS) during typhoon period in experiments of WP2013 and SCS2016.
Figure 9. Probability density function (PDF) of ambient noise levels (NLs) during typhoon period in experiments of WP2013 and SCS2016. a. 160 Hz, WP2013; b. 1 000 Hz, WP2013; c. 4 000 Hz, WP2013; d. 160 Hz, SCS2016; e. 1 000 Hz, SCS2016; f. 4 000 Hz, SCS2016.
Figure 10. Comparison of statistic results of ambient noise levels during typhoon period in experiments of Wp2013 and SCS2016. a. Standard deviation; b. skewness; c. kurtosis.
Figure 11. Measured sound speed profiles (a) and shipping distribution (b).
Figure 12. Transmission loss simulation results at azimuth angles 0° (a), 90° (b), 180° (c) and 270° (d) at 500 Hz in WP2013.
Figure 13. Transmission loss simulation results at azimuth angles 0° (a), 90° (b), 180° (c) and 270° (d) at 500 Hz in SCS2016.
Figure 14. Simulation results of N×2D transmission loss (TL) with ray approach. a. N×2D-TL, WP2013; b. N×2D -TL, SCS2016.
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