Detecting Convergence Zone Paths in Acoustic Model Outputs Using Machine Learning
Michael Sinegar · ScholarWorks @ The University of New Orleans (The University of New Orleans) · 2020
Sound propagated underwater can possibly travel according to several different patterns. One such pattern, convergence zone (CZ), is the main focus of this thesis. This thesis presents an ArcGIS-based tool to easily choose specific points in the Atlantic Ocean based on latitude and longitude, then gather data about the propagation of sound at that point. In addition to this, a mini-app that generates machine learning datasets was created. It easily allows for one to label thousands of images in a short amount of time. A thousand CZ and a thousand non-CZ images were used to train a machine learning algorithm based on neural networks. The TensorFlow software library was used. To the best of our knowledge, this research work is the first attempt to detect a particular type of underwater sound propagation path from an image using machine learning. The algorithm can recognize CZ in images with 96% accuracy.