Automating the Acoustic Detection and Characterization of Sea Ice and Surface Waves
Monitoring the status of Arctic marine ecosystems is aided by multi-sensor oceanographic moorings that autonomously collect data year-round. In the northeast Chukchi Sea, an ASL Environmental Sciences Acoustic Zooplankton Fish Profiler (AZFP) collected data from the upper 30 m of the water column ev...
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ftdoajarticles:oai:doaj.org/article:37f7c782beff4abe9b7ff2595584f123 2023-05-15T15:13:28+02:00 Automating the Acoustic Detection and Characterization of Sea Ice and Surface Waves Savannah J. Sandy Seth L. Danielson Andrew R. Mahoney 2022-10-01T00:00:00Z https://doi.org/10.3390/jmse10111577 https://doaj.org/article/37f7c782beff4abe9b7ff2595584f123 EN eng MDPI AG https://www.mdpi.com/2077-1312/10/11/1577 https://doaj.org/toc/2077-1312 doi:10.3390/jmse10111577 2077-1312 https://doaj.org/article/37f7c782beff4abe9b7ff2595584f123 Journal of Marine Science and Engineering, Vol 10, Iss 1577, p 1577 (2022) acoustics upward-looking sonar sea ice waves Chukchi Sea Chukchi Ecosystem Observatory Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 article 2022 ftdoajarticles https://doi.org/10.3390/jmse10111577 2022-12-30T19:41:20Z Monitoring the status of Arctic marine ecosystems is aided by multi-sensor oceanographic moorings that autonomously collect data year-round. In the northeast Chukchi Sea, an ASL Environmental Sciences Acoustic Zooplankton Fish Profiler (AZFP) collected data from the upper 30 m of the water column every 10–20 s from 2014 to 2020. We here describe the processing of the AZFP’s 455 kHz acoustic backscatter return signal for the purpose of developing methods to assist in characterizing local sea ice conditions. By applying a self-organizing map (SOM) machine learning algorithm to 15-min ensembles of these data, we are able to accurately differentiate between the presence of open water and sea ice, and thereby characterize statistical properties surface wave height envelopes and ice draft. The ability to algorithmically identify small-scale features within the information-dense acoustic dataset enables efficient and rich characterizations of environmental conditions, such as frequency of sparse ice floes in otherwise open water and brief open-water leads amidst the ice pack. Corrections for instrument tilt, speed of sound, and water level allow us to resolve the sea surface reflection interface to within approximately 0.06 ± 0.09 m. By automating the acoustic data processing and alleviating labor- and time-intensive analyses, we extract additional information from the AZFP backscatter data, which is otherwise used for assessing fish and zooplankton densities and behaviors. Beyond applications to new datasets, the approach opens possibilities for the efficient extraction of new information from existing upward-looking sonar records that have been collected in recent decades. Article in Journal/Newspaper Arctic Chukchi Chukchi Sea ice pack Sea ice Zooplankton Directory of Open Access Journals: DOAJ Articles Arctic Chukchi Sea Journal of Marine Science and Engineering 10 11 1577 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
acoustics upward-looking sonar sea ice waves Chukchi Sea Chukchi Ecosystem Observatory Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 |
spellingShingle |
acoustics upward-looking sonar sea ice waves Chukchi Sea Chukchi Ecosystem Observatory Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Savannah J. Sandy Seth L. Danielson Andrew R. Mahoney Automating the Acoustic Detection and Characterization of Sea Ice and Surface Waves |
topic_facet |
acoustics upward-looking sonar sea ice waves Chukchi Sea Chukchi Ecosystem Observatory Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 |
description |
Monitoring the status of Arctic marine ecosystems is aided by multi-sensor oceanographic moorings that autonomously collect data year-round. In the northeast Chukchi Sea, an ASL Environmental Sciences Acoustic Zooplankton Fish Profiler (AZFP) collected data from the upper 30 m of the water column every 10–20 s from 2014 to 2020. We here describe the processing of the AZFP’s 455 kHz acoustic backscatter return signal for the purpose of developing methods to assist in characterizing local sea ice conditions. By applying a self-organizing map (SOM) machine learning algorithm to 15-min ensembles of these data, we are able to accurately differentiate between the presence of open water and sea ice, and thereby characterize statistical properties surface wave height envelopes and ice draft. The ability to algorithmically identify small-scale features within the information-dense acoustic dataset enables efficient and rich characterizations of environmental conditions, such as frequency of sparse ice floes in otherwise open water and brief open-water leads amidst the ice pack. Corrections for instrument tilt, speed of sound, and water level allow us to resolve the sea surface reflection interface to within approximately 0.06 ± 0.09 m. By automating the acoustic data processing and alleviating labor- and time-intensive analyses, we extract additional information from the AZFP backscatter data, which is otherwise used for assessing fish and zooplankton densities and behaviors. Beyond applications to new datasets, the approach opens possibilities for the efficient extraction of new information from existing upward-looking sonar records that have been collected in recent decades. |
format |
Article in Journal/Newspaper |
author |
Savannah J. Sandy Seth L. Danielson Andrew R. Mahoney |
author_facet |
Savannah J. Sandy Seth L. Danielson Andrew R. Mahoney |
author_sort |
Savannah J. Sandy |
title |
Automating the Acoustic Detection and Characterization of Sea Ice and Surface Waves |
title_short |
Automating the Acoustic Detection and Characterization of Sea Ice and Surface Waves |
title_full |
Automating the Acoustic Detection and Characterization of Sea Ice and Surface Waves |
title_fullStr |
Automating the Acoustic Detection and Characterization of Sea Ice and Surface Waves |
title_full_unstemmed |
Automating the Acoustic Detection and Characterization of Sea Ice and Surface Waves |
title_sort |
automating the acoustic detection and characterization of sea ice and surface waves |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/jmse10111577 https://doaj.org/article/37f7c782beff4abe9b7ff2595584f123 |
geographic |
Arctic Chukchi Sea |
geographic_facet |
Arctic Chukchi Sea |
genre |
Arctic Chukchi Chukchi Sea ice pack Sea ice Zooplankton |
genre_facet |
Arctic Chukchi Chukchi Sea ice pack Sea ice Zooplankton |
op_source |
Journal of Marine Science and Engineering, Vol 10, Iss 1577, p 1577 (2022) |
op_relation |
https://www.mdpi.com/2077-1312/10/11/1577 https://doaj.org/toc/2077-1312 doi:10.3390/jmse10111577 2077-1312 https://doaj.org/article/37f7c782beff4abe9b7ff2595584f123 |
op_doi |
https://doi.org/10.3390/jmse10111577 |
container_title |
Journal of Marine Science and Engineering |
container_volume |
10 |
container_issue |
11 |
container_start_page |
1577 |
_version_ |
1766344020485734400 |