| Summary: | Thesis (M.S.) University of Alaska Fairbanks, 2022 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 selforganizing 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. North Pacific Research Board (NPRB) via project #1426 and the Alaska Ocean Observing System (AOOS) under award #NA11NOS0120020
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