Literature DB >> 11325107

Matched field processing with data-derived modes.

P Hursky1, W S Hodgkiss, W A Kuperman.   

Abstract

The authors demonstrate MFP using data-derived modes and the sound speed profile, using no a priori bottom information. Mode shapes can be estimated directly from vertical line array data, without a priori knowledge of the environment and without using numerical wave field models. However, it is difficult to make much headway with data-derived modes alone, without wave numbers, since only a few modes at a few frequencies may be captured, and only at depths sampled by the array. Using a measured sound speed profile, the authors derive self-consistent, complete sets of modes, wave numbers, and bottom parameters from data-derived modes. Bottom parameters enable modes to be calculated at all frequencies, not just those at which modes were derived from data. This process is demonstrated on SWellEx-96 experiment data. Modes, wave numbers, and bottom parameters are derived from one track and MFP based on this information is demonstrated on another track.

Year:  2001        PMID: 11325107     DOI: 10.1121/1.1353592

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  1 in total

1.  Multi-frequency sparse Bayesian learning for robust matched field processing.

Authors:  Kay L Gemba; Santosh Nannuru; Peter Gerstoft; William S Hodgkiss
Journal:  J Acoust Soc Am       Date:  2017-05       Impact factor: 1.840

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.