Reflections on Selection about Ship Seismic Sensor

2014 ◽  
Vol 687-691 ◽  
pp. 3175-3178 ◽  
Author(s):  
Pei Cui ◽  
Chun Zhi Bai
Keyword(s):  
Seismic Wave ◽  
Acoustic Signal ◽  
Low Frequency ◽  
Poor Performance ◽  
Seismic Signal ◽  
Structure Type ◽  
Ocean Bottom ◽  
Underwater Acoustic ◽  
Wave Signal ◽  
Underwater Acoustic Signal

In the special marine environmental conditions, there are certain advantages of ship seismic wave relative to underwater acoustic signal. However, due to the poor performance of seismic wave sensor, it is difficult to effectively detect seismic signal. From the signal characteristics of ship seismic wave, we can see that the ship seismic wave signal is better than underwater acoustic signal through comparative analysis in some special environment. In the low frequency band of DC-20Hz, the energy of the ship seismic is stronger and the line spectrum is more obvious. It briefly explains the mathematical model of ocean bottom seismometers receiving ship seismic wave information based on coupling theory. Finally, this paper focuses on many factors need to be considered in the selection of seismic wave sensor from the structure, type and performance of sensor. In view of different functionality of sensor types, it is suggested to use acceleration sensor to detect the ship seismic wave signal.

scholarly journals A New Hybrid Model for Underwater Acoustic Signal Prediction

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Guohui Li ◽  
Wanni Chang ◽  
Hong Yang
Keyword(s):  
Hybrid Model ◽  
Acoustic Signal ◽  
Signal Sequence ◽  
Model Performance ◽  
Low Frequency ◽  
Support Vector ◽  
Underwater Acoustic ◽  
Bee Colony ◽  
Mode Decomposition ◽  
Underwater Acoustic Signal

The prediction of underwater acoustic signal is the basis of underwater acoustic signal processing, which can be applied to underwater target signal noise reduction, detection, and feature extraction. Therefore, it is of great significance to improve the prediction accuracy of underwater acoustic signal. Aiming at the difficulty in underwater acoustic signal sequence prediction, a new hybrid prediction model for underwater acoustic signal is proposed in this paper, which combines the advantages of variational mode decomposition (VMD), artificial intelligence method, and optimization algorithm. In order to reduce the complexity of underwater acoustic signal sequence and improve operation efficiency, the original signal is decomposed by VMD into intrinsic mode components (IMFs) according to the characteristics of the signal, and dispersion entropy (DE) is used to analyze the complexity of IMF. The subsequences (VMD-DE) are obtained by adding the IMF with similar complexity. Then, extreme learning machine (ELM) is used to predict the low-frequency subsequence obtained by VMD-DE. Support vector regression (SVR) is used to predict the high-frequency subsequence. In addition, an artificial bee colony (ABC) algorithm is used to optimize model performance by adjusting the parameters of SVR. The experimental results show that the proposed new hybrid model can provide enhanced accuracy with the reduction of prediction error compared with other existing prediction methods.

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