Savannah Sandy

Savannah Sandy

Ph.D. Student

Physical Oceanography


College of Fisheries and Ocean Sciences
2150 Koyukuk Drive
107 O'Neill Bldg
Fairbanks, AK 99775
ssandy3@alaska.edu

 
Education

University of 成人影片 Fairbanks
M.S. Oceanography
2022
 
West Texas A&M University
M.A. Music (Composition)
2011

Eastern New Mexico University
B.S. Computer Science
2009

 

Thesis

Automating the Acoustic Detection and Characterization of Sea Ice and Surface Waves
 

 

Advisor

 

Selected Publications

Sandy, S.J., Danielson, S.L., and Mahoney, A.R. 2022. Automating the Acoustic Detection and
Characterization of Sea Ice and Surface Waves. Journal of Marine Science and Engineering,
10(11), 1577. DOI:

 

Biography

Savannah Sandy is a PhD student in the Department of Oceanography at the University of 成人影片 Fairbanks College of Fisheries and Ocean Sciences, studying physical oceanography under Dr. Seth Danielson. She graduated in 2022 from UAF with a M.S. in Oceanography, but felt that this was not quite enough and plans to continue studying the fascinating physical oceanographical processes in the Arctic. Her work focuses on using acoustics to study sea ice in the northeast Chukchi Sea.
 

Research Overview

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) has collected data from the upper 30 m of the water column every 10-20 seconds since 2014. Using this nearly continuous dataset, I describe the processing of the AZFP鈥檚 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 machine learning algorithm to 15-minute ensembles of these data, I am able to accurately differentiate between the presence of sea ice and open water and thus characterize statistical properties of the ice drafts and surface wave height envelopes. The ability to algorithmically identify small-scale features within the information-dense acoustic dataset enables efficient and rich characterizations of sea ice conditions and the ocean surface wave environment. Corrections for instrument tilt, speed of sound, and water level allow us to resolve the sea surface reflection interface to within approximately 0.06卤0.09 m. Automating the acoustic data processing and alleviating labor- and time-intensive analyses adds additional value to 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.