Abstract
Passive ocean acoustic thermometry (POAT) needs long accumulation time to achieve high accuracy. This article provides a machine learning-based method, Random Forest, to obtain the averaged sound speed (AVSS). With supervised learning, the AVSS can be estimated from half an hour accumulated noise cross-correlation functions (NCFs). Based on the feature importance analysis, an empirical equation is proposed to briefly describe the relationships between the features. The results of estimations are compared among different methods to demonstrate the advantage of the machine learning-based approach.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
