McNeese Andrew R.

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Last Name
McNeese
First Name
Andrew R.
ORCID
0000-0002-8363-0301

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  • Article
    Geoacoustic inversion using simple hand-deployable acoustic systems
    (Institute of Electrical and Electronics Engineers, 2022-11-22) Bonnel, Julien ; McNeese, Andrew R. ; Wilson, Preston S. ; Dosso, Stan E.
    This article proposes the use of a simple, low-cost, hand-deployable pair of experimental assets to conduct geoacoustic inversion at sea. The system consists of an expendable, fully mechanical acoustic source called a rupture induced underwater sound source (RIUSS) and a new ropeless passive acoustic mooring called a TOSSIT (not an acronym). Used together, RIUSS and TOSSIT enable the collection of acoustic data suitable to perform single-hydrophone geoacoustic inversion. The method is illustrated using data collected on the New England Mud Patch in May 2021 from a relatively small (22 m) and inexpensive chartered fishing vessel. Modal time-frequency dispersion from 15 to 385 Hz is extracted from the TOSSIT/RIUSS data using warping, and used as input for Bayesian transdimensional geoacoustic inversion. The inversion results compare favorably to results obtained with data collected on the same track with traditional assets (e.g., a vertical line array) during the 2017 Seabed Characterization Experiment, even when jointly inverting for the water-column sound speed profile and seabed geoacoustic parameters. This further demonstrates inversion repeatability in a given location using data sets collected years apart, and under different (and potentially unknown) oceanographic conditions.
  • Article
    Trans-dimensional inversion for seafloor properties for three mud depocenters on the New England shelf under dynamical oceanographic conditions
    (Acoustical Society of America, 2024-03-06) Bonnel, Julien ; Dosso, Stan E. ; Hodgkiss, William S. ; Ballard, Megan S. ; Garcia, Dante D. ; Lee, Kevin M. ; McNeese, Andrew R. ; Wilson, Preston S.
    This paper presents inversion results for three datasets collected on three spatially separated mud depocenters (hereafter called mud ponds) during the 2022 Seabed Characterization Experiment (SBCEX). The data considered here represent modal time-frequency (TF) dispersion as estimated from a single hydrophone. Inversion is performed using a trans-dimensional (trans-D) Bayesian inference method that jointly estimates water-column and seabed properties along with associated uncertainties. This enables successful estimation of the seafloor properties, consistent with in situ acoustic core measurements, even when the water column is dynamical and mostly unknown. A quantitative analysis is performed to (1) compare results with previous modal TF trans-D studies for one mud pond but under different oceanographic condition, and (2) inter-compare the new SBCEX22 results for the three mud ponds. Overall, the estimated mud geoacoustic properties show no significant temporal variability. Further, no significant spatial variability is found between two of the mud ponds while the estimated geoacoustic properties of the third are different. Two hypotheses, considered to be equally likely, are explored to explain this apparent spatial variability: it may be the result of actual differences in the mud properties, or the mud properties may be similar but the inversion results are driven by difference in data information content.