
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
                                    
Âé¶¹¹ÙÍø Fairbanks
                                                         
                                                         M.S. Oceanography
2022
                                                         
                                                         2022
West Texas A&M University
                                                         
                                                         M.A. Music (Composition)
                                                         
                                                         2011
Eastern New Mexico University
                                                         
                                                         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 Alaska 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.
                                                   Âé¶¹¹ÙÍø 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’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 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±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.
                                                   
 
				
