Echofilter: A Deep Learning Segmentation Model Improves the Automation, Standarization, and Timeliness for Post-Processing Echosounder Data in Tidal Energy Streams
Acoustics, Biological/Ecological Effects: S. Lowe, L. McGarry, J. Douglas, J. Newport, S. Oore, C. Whidden, D. Hasselman (2022/08/29)
Front. Mar. Sci. 9:867857. doi: 10.3389/fmars.2022.867857
Link: https://fundyforce.ca/resources/d5fdcd3ef87f9774e163ede0b9057b34/Lowe%20et%20al.%202022.pdf
Understanding the abundance and distribution of fish in tidal energy streams is important
for assessing the risks presented by the introduction of tidal energy devices into the
habitat. However, tidal current flows suitable for tidal energy development are often highly
turbulent and entrain air into the water, complicating the interpretation of echosounder
data. The portion of the water column contaminated by returns from entrained air must
be excluded from data used for biological analyses. Application of a single algorithm
to identify the depth-of-penetration of entrained air is insufficient for a boundary that is
discontinuous, depth-dynamic, porous, and varies with tidal flow speed.
Using a case study at a tidal energy demonstration site in the Bay of Fundy, we describe
the development and application of deep machine learning models with a U-Net based
architecture that produce a pronounced and substantial improvement in the automated
detection of the extent to which entrained air has penetrated the water column.
Our model, Echofilter, was found to be highly responsive to the dynamic range of
turbulence conditions and sensitive to the fine-scale nuances in the boundary position,
producing an entrained-air boundary line with an average error of 0.33 m on mobile
downfacing and 0.5–1.0 m on stationary upfacing data, less than half that of existing
algorithmic solutions. The model’s overall annotations had a high level of agreement
with the human segmentation, with an intersection-over-union score of 99% for mobile
downfacing recordings and 92–95% for stationary upfacing recordings. This resulted in a
50% reduction in the time required for manual edits when compared to the time required to
manually edit the line placement produced by the currently available algorithms. Because
of the improved initial automated placement, the implementation of the models permits
an increase in the standardization and repeatability of line placement.
Keywords: machine learning, deep learning, hydroacoustics, entrained air, marine renewable energy, tidal energy, environmental monitoring, marine technology