TY - JOUR
T1 - Inversion with virtual arrays in the Seabed Characterization Experiment 2022a)
AU - Michalopoulou, Zoi Heleni
AU - Gerstoft, Peter
AU - Hodgkiss, William S.
N1 - Publisher Copyright:
© 2025 Acoustical Society of America.
PY - 2025/6/1
Y1 - 2025/6/1
N2 - The Seabed Characterization Experiment 2022 (SBCEX22) was carried out in the spring and summer of 2022 with the goal of studying the fine grain sediment off the coast of New England and evaluating different methodologies for the estimation of the sediment geoacoustic properties. Towards this goal, tonal data measured during the experiment at a vertical line array are employed for source localization and geoacoustic inversion via traditional matched-field inversion (MFI) and Gaussian process (GP) based MFI. The latter approach relies on the generation of virtual arrays with functions that capture the coherence of the acoustic field at different depths in the ocean. The predicted data at densely spaced virtual sensors, resulting from interpolation of the original array, are used for inversion in place of raw measurements. A Gaussian kernel is integrated in the prediction process and different spacings between virtual sensors are considered for array interpolation. Genetic algorithms are used for optimization of the inversion for both methodologies, which are compared through an analysis of their estimates and the ensuing uncertainty. The GP-based technique is found superior, with the results in good agreement with ground truth information and with reduced uncertainty in comparison to the traditional approach.
AB - The Seabed Characterization Experiment 2022 (SBCEX22) was carried out in the spring and summer of 2022 with the goal of studying the fine grain sediment off the coast of New England and evaluating different methodologies for the estimation of the sediment geoacoustic properties. Towards this goal, tonal data measured during the experiment at a vertical line array are employed for source localization and geoacoustic inversion via traditional matched-field inversion (MFI) and Gaussian process (GP) based MFI. The latter approach relies on the generation of virtual arrays with functions that capture the coherence of the acoustic field at different depths in the ocean. The predicted data at densely spaced virtual sensors, resulting from interpolation of the original array, are used for inversion in place of raw measurements. A Gaussian kernel is integrated in the prediction process and different spacings between virtual sensors are considered for array interpolation. Genetic algorithms are used for optimization of the inversion for both methodologies, which are compared through an analysis of their estimates and the ensuing uncertainty. The GP-based technique is found superior, with the results in good agreement with ground truth information and with reduced uncertainty in comparison to the traditional approach.
UR - https://www.scopus.com/pages/publications/105008643403
UR - https://www.scopus.com/inward/citedby.url?scp=105008643403&partnerID=8YFLogxK
U2 - 10.1121/10.0036920
DO - 10.1121/10.0036920
M3 - Article
C2 - 40540710
AN - SCOPUS:105008643403
SN - 0001-4966
VL - 157
SP - 4526
EP - 4537
JO - Journal of the Acoustical Society of America
JF - Journal of the Acoustical Society of America
IS - 6
ER -