This repository contains a ML model that can be used to determine seafloor habitat type from single beam acoustic data inputs. Specifically, the input files need to be produced by a Simrad EK60 scientific echosounder, collected at a minimum of two frequencies (38 and 120 kHz) and up to five frequencies (18,38,70,120, and 200 kHz) and be calibrated using standard calibration methods. The transducers used for this work produced equivalent beam angles as follows; 18 kHz at degrees , 38, 70, 120 and 200 kHz at 7 degrees. The output represents proportion of habitat predicted across five categories; sand, mixed_coarse, cobble, boulder, and bedrock. The model also estimates seafloor rugosity, which represents small scale variation in seafloor elevation. Full details are available in "Seabed classification in the Gulf of Alaska from acoustic surveys using deep learning" by K. Agarwal, C. Rooper, and K. Williams (in prep).
For this project, 2017 and 2019 acoustic survey data collected by the Midwater Assessment and Conservation Engineering Program at the Alaska Fisheries Scienc Center, NOAA, were used to classify the seafloor acros the Gulf of Alaska. The model outputs are publicly available though the Zenodo open repository under the following DOI's acoustic seafloor classification model output GOA 2017 - 10.5281/zenodo.17612923 acoustic seafloor classification model output GOA 2019 - 10.5281/zenodo.17612149 A second data set that contains the 2019 survey outputs aggregated over 1 km grids is also available at Gulf of Alaska proportional seafloor habitat classification with 1 km resolution - 10.5281/zenodo.17663313
Image data are also available upon request from
https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.nodc:305766
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