FengChen_2018_adaptive_grid_manuscript.pdf (26.5 MB)
An adaptive grid to improve the efficiency and accuracy of modelling underwater noise from shipping
journal contributionposted on 2018-06-12, 07:57 authored by Leah E. Trigg, Feng Chen, Georgy I. Shapiro, Simon N. Ingram, Clare B. Embling
Underwater noise pollution from shipping is a significant ecological concern. Acoustic propagation models are essential to predict noise levels and inform management activities to safeguard ecosystems. However, these models can be computationally expensive to execute. To increase computational efficiency, ships are spatially partitioned using grids but the cell size is often arbitrary. This work presents an adaptive grid where cell size varies with distance from the receiver to increase computational efficiency and accuracy. For a case study in the Celtic Sea, the adaptive grid represented a 2 to 5 fold increase in computational efficiency in August and December respectively, compared to a high resolution 1 km grid. A 5 km grid increased computational efficiency 5 fold again. However, over the first 25 km, the 5 km grid produced errors up to 13.8 dB compared to the 1 km grid, whereas, the adaptive grid generated errors of less than 0.5 dB.
This work was supported by a Plymouth University Research Studentship and the Plymouth Ocean Forecasting Centre (LG-33/300/01/2014).
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Published inMarine Pollution Bulletin
Pages589 - 601
CitationTRIGG, L.E. ... et al, 2018. An adaptive grid to improve the efficiency and accuracy of modelling underwater noise from shipping. Marine Pollution Bulletin, 131 Part A, pp.589-601.
- AM (Accepted Manuscript)
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
NotesThis paper was accepted for publication in the journal Marine Pollution Bulletin and the definitive published version is available at https://doi.org/10.1016/j.marpolbul.2018.04.034.