Modeling and simulation of underwater high frequency noise spectrum using artificial neural networks

In this work, estimation of high frequency underwater ambient noise spectrum carried out using neural network is reported. The periodic ambient noise data were measured at 5 m depth in Bay of Bengal using omni directional hydrophone. The data were acquired using portable, broadband high frequency data acquisition system. The noise level at high frequencies in the range of 5 kHz to 12 kHz was analyzed for the entire data set (N=100). The influence of wind speed covering a rage of 2.5 m/s to 9 m/s and wave height in the range of 0.2 m to 2 m were used for the analysis. Feed Forward Neural Network architecture using LevenbergMarquardt (LM) algorithm was employed and the network model obtained after training was used to estimate the noise level. Analysis of experimental data reveals that the noise level decreases with increase in frequency for the observed range of frequencies. The noise level increases with increase in wind speed and the variations were found to be non linear in nature. Similar observation was recorded for the considered range of wave height. Further it appears that the proposed method is useful in the estimation and interpolation of underwater Noise Spectrum Level (NSL) for the considered frequency range and it is observed through Mean Squared Error analysis. As the underwater measurements are often inhospitable due to the remote oceanic regions and conditions, these studies seems to be relevant. In this work, the objectives, experimental arrangement, data acquisition, neural network based modeling and analysis are presented in detail.