Adaptive Water Sampling based on Unsupervised Clustering

HR: 0800h
AN: OS51B-0472 [Abstracts]
TI: Adaptive Water Sampling based on Unsupervised Clustering
AU: * Py, F
EM: fpy@mbari.org
AF: MBARI, 7700 Sandholdt Road, Moss Landing, CA 95039, United States
AU: Ryan, J
EM: ryjo@mbari.org
AF: MBARI, 7700 Sandholdt Road, Moss Landing, CA 95039, United States
AU: Rajan, K
EM: kanna@mbari.org
AF: MBARI, 7700 Sandholdt Road, Moss Landing, CA 95039, United States
AU: Sherman, A
EM: alana@mbari.org
AF: MBARI, 7700 Sandholdt Road, Moss Landing, CA 95039, United States
AU: Bird, L
EM: bila@mbari.org
AF: MBARI, 7700 Sandholdt Road, Moss Landing, CA 95039, United States
AU: Fox, M
EM: maria@cis.strath.ac.uk
AF: University of Strathclyde, Livingstone Tower 26 Richmond Street, Glasgow, G1 1XQ, United Kingdom
AU: Long, D
EM: derek@cis.strath.ac.uk
AF: University of Strathclyde, Livingstone Tower 26 Richmond Street, Glasgow, G1 1XQ, United Kingdom
AB: Autonomous Underwater Vehicles (AUVs) are widely used for oceanographic surveys, during which data is collected from a number of on-board sensors. Engineers and scientists at MBARI have extended this approach by developing a water sampler specialy for the AUV, which can sample a specific patch of water at a specific time. The sampler, named the Gulper, captures 2 liters of seawater in less than 2 seconds on a 21" MBARI Odyssey AUV. Each sample chamber of the Gulper is filled with seawater through a one-way valve, which protrudes through the fairing of the AUV. This new kind of device raises a new problem: when to trigger the gulper autonomously? For example, scientists interested in studying the mobilization and transport of shelf sediments would like to detect intermediate nepheloīd layers (INLs). To be able to detect this phenomenon we need to extract a model based on AUV sensors that can detect this feature in-situ. The formation of such a model is not obvious as identification of this feature is generally based on data from multiple sensors. We have developed an unsupervised data clustering technique to extract the different features which will then be used for on-board classification and triggering of the Gulper. We use a three phase approach: 1) use data from past missions to learn the different classes of data from sensor inputs. The clustering algorithm will then extract the set of features that can be distinguished within this large data set. 2) Scientists on shore then identify these features and point out which correspond to those of interest (e.g. nepheloīd layer, upwelling material etc) 3) Embed the corresponding classifier into the AUV control system to indicate the most probable feature of the water depending on sensory input. The triggering algorithm looks to this result and triggers the Gulper if the classifier indicates that we are within the feature of interest with a predetermined threshold of confidence. We have deployed this method of online classification and sampling based on AUV depth and HOBI Labs Hydroscat-2 sensor data. Using approximately 20,000 data samples the clustering algorithm generated 14 clusters with one identified as corresponding to a nepheloīd layer. We demonstrate that such a technique can be used to reliably and efficiently sample water based on multiple sources of data in real-time.
DE: 4894 Instruments, sensors, and techniques
SC: Ocean Sciences [OS]
MN: 2007 Fall Meeting