A peak‐capture algorithm used on an autonomous underwater vehicle in the 2010 Gulf of Mexico oil spill response scientific survey

Abstract

Institute autonomous underwater vehicle (AUV) was deployed to make high-resolution surveys of the water column in targeted areas. There were 10 2-liter samplers on the AUV for acquiring water samples. An essential challenge was how to autonomously trigger the samplers when peak hydrocarbon signals were detected. In ship hydrocasts (measurements by lowered instruments) at a site to the southwest of the Deepwater Horizon wellhead, the hydrocarbon signal showed a sharp peak between 1,100-and 1,200-m depths, suggesting the existence of a horizontally oriented subsurface hydrocarbon plume. In response to this finding, we deployed the AUV at this site to make high-resolution surveys and acquire water samples. To autonomously trigger the samplers at peak hydrocarbon signals, we modified an algorithm that was previously developed for capturing peaks in a biological thin layer. The modified algorithm still uses the AUV's sawtooth (i.e., yo-yo) trajectory in the vertical dimension and takes advantage of the fact that in one yo-yo cycle, the vehicle crosses the horizontal plume (i.e., the strong-signal layer) twice. On the first crossing, the vehicle detects the peak and logs the corresponding depth (after correcting for the detection delay). On the second crossing, a sampling is triggered when the vehicle reaches the depth logged on the first crossing, based on the assumption that the depth of the horizontal oil layer does not vary much between two successive crossings that are no more than several hundred meters apart. In this paper, we present the algorithm and its performance in an AUV mission on 3 June 2010 in the Gulf of Mexico. In addition, we present an improvement to the algorithm and the corresponding results from postprocessing the AUV mission data.

AUV Adaptive Sampling Methods: A Review

“…In the case of crude oils, a third of the volume of a slick evaporates within the first day. However, a non-buoyant oil plume has also been reported [67] during the largest anthropogenic oil spill accident following the Deepwater Horizon offshore drilling rig explosion in 2010 in the Gulf of Mexico. This was an unprecedented case, as approximately 780,000 m 3 of crude oil was spilled and a significant amount of this oil was trapped in the deep-water column at around a depth of 1000 m. Jakuba et al [68] developed a semi-autonomous method using data-denial simulation for data interpretation and adaptive sampling plan design.…”

Front delineation and tracking with multiple underwater vehicles

“…A number of different near real‐time feature tracking methods exist for applications such as thermoclines (Cruz & Matos, ; Petillo, ; Petillo et al, ; Sun, Li, Yan, Wang, & Chen, ; Zhang et al, ), and oil spills (Zhang et al, ). These approaches focus on tracking a one‐dimensional feature using a single vehicle, while we utilize multiple vehicles to track a two‐dimensional feature.…”

“…The suitability of this technique for a given field program depends on the scientific questions of that experiment and the relevant temporal and spatial scales. Sampling a single feature can be very effective when, for example, following the evolution of an oil spill (Zhang et al, ) or the thermocline (Cruz & Matos, ; Petillo et al, ; Petillo, ; Sun et al, ; Zhang et al, ). When observations on larger scales are necessary, this approach could be integrated into a larger scale observation network.…”

Toward autonomous underwater mapping in partially structured 3D environments

“…Other environmental sensors mounted that have been used on AUVs include sheet lasers [75], fluorometers [15], mass spectrometers [36], optical backscatter sensors [100], and magnetometers [88]. If a prior map of su cient resolution is available, these environmental sensors can be used to navigate within the map.…”