The research discusses the importance of underwater acoustics in understanding sound propagation in water and its various applications. Traditional mathematical models, such as the Parabolic Equation Range Dependent Acoustic Model (PE-RAM), have been used to study underwater acoustics, but they have limitations and require manual input. Machine Learning and Deep Learning techniques have the potential to overcome these limitations and reduce manual input dependencies. The research also discusses the various domains involved in underwater acoustics, including the mathematical aspects of the PE model, and the Machine Learning aspects of the field. Overall, the research provides an informative overview of the current state of underwater acoustics and the potential for Machine Learning and Deep Learning techniques to enhance research in this field.
1. Underwater acoustics is important for various applications, including ocean mapping and military/sonar applications.
2. Traditional mathematical models for acoustic propagation under water suffer from errors and limitations.
3. The Parabolic Equation model is a widely used numerical solution, but requires manual input and may suffer from errors due to physiographic differences in the Indian Ocean.
4. Machine Learning and Deep Learning techniques can be used to model the data and learn the patterns, reducing the reliance on manual input and improving accuracy.
5. One of the main advantages of machine learning and deep learning approaches is reduced dependencies on manual inputs, making them more adaptable to the complex and varied physiography of the Indian Ocean region.
“”Due to the heavy signal loss and fading undergone by EM waves underwater, acoustic waves are the main medium of propagation of signals under the water.
Research Intern, MRC