Extraction Of Normal Modes Information Using Data-derived Method

In shallow water environment, normal mode theory is a rapid numerical method of sound field with high accuracy. Matched field processing (MFP) and matched mode processing (MMP) usually require accurate a priori knowledge of the physical properties of the ocean and its bottom, such as the sound-speed profile in the water column, the geoacoustic profile of the bottom and so on. However, the costly environmental accuracy demanded by the two methods is often unattainable. So the study on the normal modes extraction technique without any a priori information about the environment is very necessary.Data-derived normal modes extraction using vertical line arrays(VLAs) data is studied in this paper, without any a priori information about the sound–speed profile in the water column and the geoacoustic profile of the bottom. The method is based on normal mode method of computational ocean acoustics and singular value decomposition(SVD), which are introduced in this paper. The pekeris waveguide is simulated by Kraken as the sound field environment and processed using MATLAB to demonstrate the efficiency of this method. Furthermore, parameters are changed individually to exam the conditions under which the mode decompsition correspond to the singular value decomposition. Then the data acquired from the Acoustics Experiment of Yellow Sea Oceanic Front and Internal Waves(AEYFI 05) is processed in Chapter 4. The depth fluctuation of No.1 normal mode's peak value can reflect the change of tide to some extent.