Safe and economic hydrocarbon exploration, development and productionoperations in the high arctic deepwater require a nuanced understanding of thesea ice environment. Robust image analysis techniques provide methods bywhich this nuance can be more objectively characterized and used for decisionmaking while in operations. Morphological segmentation and windowedstatistical analysis are proposed as two approaches that provide usefulinformation on the tactical scale by rapidly characterizing floe fieldmorphology and relative surface roughness. Their use is demonstratedwithin the context of actual high arctic field program data. Results fromthe method application are shown and the benefits and limitations of their useare discussed.


A significant hydrocarbon reserve is believed to exist in the higharctic. Hamilton [2011] used U.S. Geological Survey data [Bird et al.2008] to estimate that 40 billion bbl of oil could lie in the high arcticdeepwater (defined as water depths >100m). A vision for addressingthe technical challenges associated with safe and economic floating drilling inthe high arctic was also defined. With respect to execution of icemanagement operation support, Hamilton et al. [2011] and Younan et al. [2012]begin to address these technical challenges by defining a near-field icebreakertasking process control framework and a far-field risk characterization / alertsystem framework respectively. Selected image analysis techniques areproposed as a means to build on this ice management framework by improving theobjectivity with which far-field sea ice characterization and decision makingcan be conducted while in operations.

The proposed use of automated and semi-automated digital analysis techniquesto improve the consistency and speed of sea-ice data analysis is notnecessarily novel. Kwok et al. [1992] and Kwok [2004] demonstratedautomated segmentation methods for distinguishing between first-year ice (FYI)and multi-year ice (MYI) with synthetic aperture radar (SAR) satellitedata. The first iterations of this method used clustering to segment theimage and then a rule based look-up table (LUT) to classify image pixels. The later versions of this effort incorporated data fusion through the use ofsatellite based SSM/I and AMSR-E passive microwave and QuikSCAT radar data toimprove ice concentration and MYI classification respectively. The U.S. National Ice Center, under sponsorship of the U.S. Navy, U.S. Coast Guard andNational Oceanic and Atmospheric Administration has developed ARKTOS [Gineriset al. 2000] that uses SAR and passive microwave SSM/I data with watershedsegmentation and rule based classification.

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