C. Soares email@example.com
S.M. Jesus firstname.lastname@example.org
A. Mantouka, email@example.com
P. Felisberto, firstname.lastname@example.org
LARSys, University of Algarve,
Campus de Gambelas, PT-8005-139 Faro, Portugal.
Comments: download pdf file (not available).
Ref.: MTS/IEEE Oceans, Aberdeen, June 2017.
The WiMUST (Widely scalable Mobile Underwater Sonar Technology) Project envisions using a team of autonomous underwater vehicles towing short acoustic arrays for seismic surveying of seabottom geoacoustic properties. One of the objectives in the project is to tackle the inversion of acoustic data collected with short towed horizontal arrays by means of a Matched-Field Inversion (MFI) technique. While there is great deal of experience in MFI and the so-called focalization applied to horizontal propagation scenarios, in near vertical propagation scenarios, with a source receiver horizontal distance limited to a few tens of meter or less, there is little understanding in terms of feasibility of the acoustic inversion of bottom properties. In particular, the simultaneous inversion of bottom properties (sound speeds, densities, attenuations) of multiple bottom layers has to be tackled, since the experimenter has to account for the admissible mismatch of other environmental properties such as water sound speed and depth, and the potential solution ambiguity inherent to an optimization problem with ten or more unknown parameters. The actual simulation study, carried out with an environmental scenario and geometric set up based on the Peljesac data set, considers a shallow water acoustic propagation scenario with a short array. A sensitivity analysis in MFI provides understanding on the observability of the unknown parameters of interest. A mismatch analysis indicates that water column mismatch (sound speed and depth) may cause the MFI procedure to break down. Based on the conclusions taken from the sensitive and mismatch analysis, an iterative acoustic inversion concept with feedback of intermediate parameter estimates is developed and tested with simulated data.
ACKNOWLEDGMENT: This work was funded under project WiMUST contract 645141, H2020 program of the EU.
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