Machine Learning Application In Underwater Channel Modelling.

Overview

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.

Key highlights
  • Underwater acoustics is important for various applications, including ocean mapping and military/sonar applications.
  • Traditional mathematical models for acoustic propagation under water suffer from errors and limitations.
  • 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.
  • 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.
  • 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.
Key Challenges
  • Signal loss and fading: EM waves do not propagate well underwater, making acoustic waves the main medium for underwater communication
  • Complex environmental factors: Underwater environment is complex and variable, with factors such as temperature, salinity, and water depth affecting the propagation of sound waves.
  • Limited data: Unlike other fields where machine learning has been successfully applied, such as computer vision, there is a limited amount of data available in underwater acoustics.
  • Computational limitations:¬†Some of the numerical models for acoustics require a significant amount of computational power, making it challenging to run simulations and analyze data in a timely manner.
  • Interference and noise: Underwater communication can be disrupted by interference and noise from natural sources, such as marine life and geological activity, as well as human-made sources, such as ships and submarines.
Major Opportunities
  • Improving accuracy of underwater acoustic models using machine learning and deep learning techniques.
  • Developing autonomous underwater vehicles with improved navigation and communication capabilities.
  • Enhancing marine life monitoring and conservation efforts through improved underwater acoustic sensing.
  • Improving underwater communication systems for commercial and scientific applications.

“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.”