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Acoustic Detection and Characterization of Sea Ice and Surface Waves in the Northeast Chukchi Sea.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Acoustic Detection and Characterization of Sea Ice and Surface Waves in the Northeast Chukchi Sea./
作者:	Sandy, Savannah J.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	58 p.
附註:	Source: Masters Abstracts International, Volume: 83-10.
Contained By:	Masters Abstracts International83-10.
標題:	Physical oceanography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29063061
ISBN:	9798209992936

Acoustic Detection and Characterization of Sea Ice and Surface Waves in the Northeast Chukchi Sea.
Sandy, Savannah J.

Acoustic Detection and Characterization of Sea Ice and Surface Waves in the Northeast Chukchi Sea. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 58 p.

Source: Masters Abstracts International, Volume: 83-10.

Thesis (M.S.)--University of Alaska Fairbanks, 2022.

This item must not be sold to any third party vendors.

Monitoring the status of Arctic marine ecosystems is aided by oceanographic moorings that autonomously collect data year-round. Near Hanna Shoal in the northeast Chukchi Sea, the Chukchi Ecosystem Observatory moorings include an ASL Environmental Sciences Acoustic Zooplankton Fish Profiler (AZFP) datalogger, a multi-frequency upward-looking sonar that is programmed to collect data from across the upper 30 m of the water column every 10-20 seconds. Using six years of nearly continuous data, here we describe a statistical analysis of the datalogger's 455 kHz acoustic backscatter return signal. When used in conjunction with a self-organizing map machine learning algorithm, these data allow us to accurately differentiate between the presence of sea ice and open water and characterize surface waves. The approach detects short-duration (e.g., 15 minutes or longer) sea ice leads that pass over the mooring in winter, and sparse ice floes that pass over in summer. The ability to algorithmically identify small-scale features within the information-dense acoustic dataset enables rich characterizations of sea ice conditions and the ocean surface wave environment. Example applications include quantifying the recurrence of leads during ice-covered seasons, sparse ice in otherwise open water, statistics of ice keels and level ice, and wave height statistics. By automating the acoustic data processing and alleviating labor- and time-intensive analyses, we can maximize the use of these year-round acoustic data. Beyond applications to newly produced datasets, the approach opens possibilities for the efficient extraction of new information from existing upward-looking sonar records from recent decades.

ISBN: 9798209992936Subjects--Topical Terms:

3168433
Physical oceanography.
Subjects--Index Terms:

Acoustics

Acoustic Detection and Characterization of Sea Ice and Surface Waves in the Northeast Chukchi Sea.
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520 $a Monitoring the status of Arctic marine ecosystems is aided by oceanographic moorings that autonomously collect data year-round. Near Hanna Shoal in the northeast Chukchi Sea, the Chukchi Ecosystem Observatory moorings include an ASL Environmental Sciences Acoustic Zooplankton Fish Profiler (AZFP) datalogger, a multi-frequency upward-looking sonar that is programmed to collect data from across the upper 30 m of the water column every 10-20 seconds. Using six years of nearly continuous data, here we describe a statistical analysis of the datalogger's 455 kHz acoustic backscatter return signal. When used in conjunction with a self-organizing map machine learning algorithm, these data allow us to accurately differentiate between the presence of sea ice and open water and characterize surface waves. The approach detects short-duration (e.g., 15 minutes or longer) sea ice leads that pass over the mooring in winter, and sparse ice floes that pass over in summer. The ability to algorithmically identify small-scale features within the information-dense acoustic dataset enables rich characterizations of sea ice conditions and the ocean surface wave environment. Example applications include quantifying the recurrence of leads during ice-covered seasons, sparse ice in otherwise open water, statistics of ice keels and level ice, and wave height statistics. By automating the acoustic data processing and alleviating labor- and time-intensive analyses, we can maximize the use of these year-round acoustic data. Beyond applications to newly produced datasets, the approach opens possibilities for the efficient extraction of new information from existing upward-looking sonar records from recent decades.
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