Detection and quantification of oil under sea ice: The view from below

Wilkinson, Jeremy; Boyd, T.; Hagen, Bernard; Maksym, Ted; Pegau, Scott; Roman, Christopher; Singh, Hanumant; Zabilansky, Leonard J.

doi:10.1016/j.coldregions.2014.08.004

Cited by 21 publications

(12 citation statements)

References 20 publications

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“…Contactless detection of a subject in the environment around an AUV can be performed by optical or acoustical methods or a combination of both [13]. Optical data can be acquired from a visual sensor such as an underwater high-resolution still or video camera [14] or a laser system [15]. While the use of visual detection is intuitive, optical images require a great deal of processing time to analyse.…”

Section: B Ping360 Scanning Sonar Modelmentioning

confidence: 99%

“…Six different plume models with varied density distribution, different levels of roughness of the boundary, patchiness and with a varied number of holes inside the plume were generated as shown in Figure 11. They were labelled from A to F. The reference number as given in (15) roughly indicates the tendency of the plume shape and patchiness. For example, a higher value of the term incohesion corresponds to a relatively incohesive plume with a greater number of small holes and patches; whereas a lower value results in a cohesive plume consisting of a continuous body with a smaller number of holes.…”

Section: F Virtual Hydrocarbon Plume Modelsmentioning

confidence: 99%

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Oil Plume Mapping: Adaptive Tracking and Adaptive Sampling From an Autonomous Underwater Vehicle

et al. 2020

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We have developed an adaptive sampling algorithm for an Explorer autonomous underwater vehicle (AUV) to conduct in-situ analysis of acoustic measurements to perform autonomous oil plume detection and tracking. The methodology of the tracking phase involves ongoing analysis of the detected plume, assessing target validity and proximity for AUV decision-making for plume mapping. We previously introduced the bumblebee flight path, a new biomimetic search pattern designed to maximize the spatial coverage in the oil plume detection phase. This paper focuses on a new tracking strategy as the key adaptive stage in our plume delineation. For initial development we used a 360-degree scanning sonar sensor model. Simulations were done with different plume models to assess the performance of the developed adaptive sampling algorithm. A convergence study demonstrated that the algorithm could successfully track the boundary of a non-regular shaped/patchy oil plume at up to a 0.05Hz sampling frequency. A sensitivity study identified the correlations between plume feature complexity and the anticipated range of acoustic measurement update delays. The decision-making architecture consists of three separate components which implements either proximity or boundary following control and contributes to the final decision on the next desired heading of the vehicle. A weight ratio, that determined the relative allocation of each component, was varied to study its impact on the tracking performance of the AUV. The novelty of our approach is in addressing the discontinuous and patchy nature of realistic oil plumes. Our sampling algorithm and its performance in simulations is a significant step beyond the practical limitations of existing gradient-following methods because it accounts for the oil patches and droplets which gradient-following algorithms do not.

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Section: B Ping360 Scanning Sonar Modelmentioning

confidence: 99%

Section: F Virtual Hydrocarbon Plume Modelsmentioning

confidence: 99%

Oil Plume Mapping: Adaptive Tracking and Adaptive Sampling From an Autonomous Underwater Vehicle

et al. 2020

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“…Wilkinson et al conducted experiments detecting oil under ice using cameras and ultrasound [97]. Ultrasound was proposed to function, as the impedances of the water-oil and oil-ice layers would be such that reflections of ultrasound would result.…”

Section: Underwater Slick Thickness Measurementsmentioning

confidence: 99%

The Challenges of Remotely Measuring Oil Slick Thickness

Fingas¹

2018

Remote Sensing

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The thickness of oil spills on the sea is an important but poorly studied topic. Means to measure slick thickness are reviewed. More than 30 concepts are summarized. Many of these are judged not to be viable for a variety of scientific reasons. Two means are currently available to remotely measure oil thickness, namely, passive microwave radiometry and time of acoustic travel. Microwave radiometry is commercially developed at this time. Visual means to ascertain oil thickness are restricted by physics to thicknesses smaller than those of rainbow sheens, which rarely occur on large spills, and thin sheen. One can observe that some slicks are not sheen and are probably thicker. These three thickness regimes are not useful to oil spill countermeasures, as most of the oil is contained in the thick portion of a slick, the thickness of which is unknown and ranges over several orders of magnitude. There is a continuing need to measure the thickness of oil spills. This need continues to increase with time, and further research effort is needed. Several viable concepts have been developed but require further work and verification. One of the difficulties is that ground truthing and verification methods are generally not available for most thickness measurement methods.

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“…Recent work has highlighted the potential for detecting thin oil layers trapped beneath sea ice using acoustic backscatter techniques (Bassett et al, , 2015Wilkinson et al, 2015). This study builds on this work by further examining the use of high-frequency (f > 50 kHz) broadband acoustic backscatter techniques.…”

Section: Introductionmentioning

confidence: 98%

Broadband acoustic backscatter from crude oil under laboratory-grown sea ice

Bassett

Lavery

Maksym

et al. 2016

The Journal of the Acoustical Society of America

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In ice-covered seas, traditional air-side oil spill detection methods face practical challenges. Conversely, under-ice remote sensing techniques are increasingly viable due to improving operational capabilities of autonomous and remotely operated vehicles. To investigate the potential for under-ice detection of oil spills using active acoustics, laboratory measurements of high-frequency, broadband backscatter (75-590 kHz) from crude oil layers (0.7-8.1 cm) under and encapsulated within sea ice were performed at normal and 20 incidence angles. Discrete interfaces (water-oil, oil-ice, and ice-oil) are identifiable in observations following oil injections under the ice and during the subsequent encapsulation. A one-dimensional model for the total normal incidence backscatter from oil under ice, constrained by oil sound speed measurements from À10C to 20 C and improved environmental measurements compared to previous studies, agrees well with preencapsulation observations. At 20 incidence angles echoes from the ice and oil under ice are more complex and spatially variable than normal incidence observations, most likely due to interface roughness and volume inhomogeneities. Encapsulated oil layers are only detected at normal incidence. The results suggest that high-frequency, broadband backscatter techniques may allow under-ice remote sensing for the detection and quantification of oil spills.

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Detection and quantification of oil under sea ice: The view from below

Cited by 21 publications

References 20 publications

Oil Plume Mapping: Adaptive Tracking and Adaptive Sampling From an Autonomous Underwater Vehicle

Oil Plume Mapping: Adaptive Tracking and Adaptive Sampling From an Autonomous Underwater Vehicle

The Challenges of Remotely Measuring Oil Slick Thickness

Broadband acoustic backscatter from crude oil under laboratory-grown sea ice

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