UACE2017 Proceedings: Bottom parameters inversion via Bayesian theory in the deep ocean

• Session:
  Inversion methods for estimating geoacoustic profiles of the ocean bottom
• Paper:
  Bottom parameters inversion via Bayesian theory in the deep ocean
• Author(s):
  Xiaole Guo, Kunde Yang, Rui Duan, Yuanliang Ma
• Abstract:
  This paper develops a new approach to estimating bottom parameters based on Bayesian theory in the deep ocean. The solution in a Bayesian inversion is characterized by its posterior probability density (PPD), which combines prior information about the model with information from an observed data set. Bottom parameters are sensitive to the transmission loss (TL) data in the shadow zone of the deep ocean. In this study, TLs of different frequencies from the South China Sea (SCS) in the summer of 2014 are used as observed data sets. The interpretation of the multidimensional PPD requires the calculation of its moments, such as the mean, covariance, and marginal distributions, which provide parameter estimates and uncertainties. Considering that the sensitivities of shallow-zone TLs vary for different frequencies of the bottom parameters in the deep ocean, this research obtained bottom parameters at varying frequencies. Then, the inversion results compare with the sampling data. Besides, this paper shows the inversion results for multi-frequency combined inversion.
•   Download the full paper

Contact details

• Contact person:
  Mr Xiaole Guo
• e-mail:
  
• Affiliation:
  School of Marine Science and Technology, Northwestern Polytechnical University
• Country:
  China