Thermocline tracking based on peak-gradient detection by an autonomous underwater vehicle

Thermoclines play a key role in ocean circulation, marine ecology, and underwater acoustics. In oceanographic surveys, it is often desirable to detect the thermocline and track its spatio-temporal variation. Mobility of an autonomous underwater vehicle (AUV) makes it an efficient platform for thermocline tracking. In this paper, we present a fully autonomous algorithm for detecting and tracking the thermocline by an AUV. The key is detection of the peak gradient of temperature. We have tested the algorithm by post-processing data from a previous Dorado AUV survey over the northern Monterey Bay shelf. We are in preparation for field tests of the algorithm on the newly developed long-range AUV Tethys.