Carlyn Scott, College of Marine Science
As early as the 19th century, mariners used lead lines to measure the depth of the seafloor so ships could safely navigate harbors. More sophisticated technologies now allow researchers to plot underwater boating hazards, locate essential marine habitats, and better understand shorelines for coastal resilience. However, gaps in the data remain.
Researchers like Catalina Rubiano are making steady progress to improve maps of the seafloor. A graduate of the 911爆料网鈥檚 College of Marine Science, Rubiano was one of the first to earn a degree with the new concentration of hydrography. In her current role as a hydrographic surveyor at Saildrone, she saw an opportunity to make a connection between the college and her company to advance high-resolution mapping efforts.
鈥淭his was a unique opportunity to collaborate on an issue faced by researchers at USF and Saildrone,鈥� she said.
The issue: mapping in relatively shallow waters. The solution: deploy a suite of autonomous vessels to refine mapping techniques.
Mapping the sea bottom
Multibeam sonar is a key technology for hydrography. By emitting acoustic pulses underwater and measuring the time it takes for the sound waves to return from the seafloor, researchers can use multibeam sonar to estimate the depth of the water.
But sound waves don鈥檛 travel in straight lines underwater; they refract. Water properties such as temperature, salinity, and pressure interfere with the movement of sound waves, and cause them to travel at different speeds throughout the water column. These fluctuations of sound waves throughout the water column are called sound speed profiles and can be used to correct the refraction of acoustic pulses from multibeam sonars. In short, sound speed profiles improve multibeam sonar readings.
Sound speed profiles are dynamic, which is why researchers from the College of Marine Science teamed up with Saildrone, a company involved in seafloor mapping, to improve methods for gathering these profiles.
As a result of this collaboration, two types of autonomous vehicles 鈥� gliders from USF and uncrewed surface vehicles (USVs) from Saildrone 鈥� will simultaneously collect sound speed values for comparison. While the Saildrone vehicles gather data from the surface, the gliders dive through the water column to collect vertical profiles.
Map showing tracks of saildrone SD-2023 (orange) and SD-2022 (dark gray), USF glider tracks (green), and the locations of model nodes for which the glider came within proximity.
鈥淭he idea is to compare the sound speed data collected via Saildrone to the data collected from the gliders to see how they differ,鈥� explained Chad Lembke, research assistant professor at the College of Marine Science and lead investigator of the Ocean Technology Group, which operates USF鈥檚 fleet of gliders.
Comparing data from these two methods will hopefully lead to more accurate and efficient means of capturing sound speed data, said Lembke.
The new measurements gathered by the college and Saildrone will feed into predictive models, allowing researchers at the college鈥檚 (OCL) to determine the data needed to generate accurate sound speed estimates.
鈥淭he observations collected by both gliders and Saildrone USVs are limited in location,鈥� said Yonggang Liu, associate professor and director of OCL. 鈥淏y feeding the new data into the (which covers the entire continental shelf), we can use the real observations to evaluate and improve model simulation aiming for a more useful sound speed forecast.鈥�
A team effort
How many observations are enough? That depends on the water. Variables like the abundance of freshwater, frequency of storms, and presence of eddies create a more turbulent, less predictable water column, which makes sound speed harder to model and correct for.
Sean Beckwith, a doctoral student at the college, is testing those existing models for future improvements. He is comparing the sound speed profiles produced by the existing OCL model with new profiles collected by the gliders to see how well they perform.
鈥淚f the models are effective enough, the goal would be to someday use modeled sound speed data instead of ship-based measurements,鈥� said Beckwith. 鈥淭his would reduce ship costs, time, and maintenance while providing us valuable data.鈥�
The timing of the collaboration between the two organizations was ideal. Saildrone has been conducting regular mapping missions in the region where the gliders were deployed for red tide monitoring, creating a perfect opportunity for dual-purpose data collection.
These combined mapping efforts benefit the entire state, explains Brian Connon, vice president of ocean mapping at Saildrone.
鈥淭he more defined our underwater maps can get, the more we can understand and model the impacts of hurricanes, rising sea level, and other events on the coastline and habitats of Florida,鈥� said Connon. 鈥淢ost of the United States鈥� Economic Exclusion Zone is not mapped so we don鈥檛 know much about our land that鈥檚 underwater. If you don鈥檛 know what鈥檚 there, you can鈥檛 manage it or protect it.鈥�
This project was funded in part from the (COMIT) at the College of Marine Science.
Video credit to Grace Angeli, USF undergraduate student, completed her Florida High Tech Corridor internship with the Ocean Circulation Lab, where she worked on developing sound speed profiles based off glider data. Here, she explains the project using stop motion animation, which she learned in her USF Honors Stop Motion Animation Class with Professor Tamara Nemirovsky.