ABSTRACT The Arctic sea-ice region has become an increasingly important study area since it is not only a key driver of the Earth’s climate but also a sensitive indicator of climate change. Therefore, it is crucial to extract high-resolution geophysical features of sea ice from remote sensing data to model and validate sea-ice changes. With large volumes of high spatial resolution data and intensive feature extraction, classification, and analysis processes, cloud infrastructure solutions can support Earth science. One example is the Arctic CyberInfrastructure (ArcCI), which was built to address image management and processing for sea-ice studies. The ArcCI system employs an efficient geophysical feature extraction workflow that is based on the object-based image analysis (OBIA) method alongside an on-demand web service for Arctic cyberinfrastructure. By integrating machine learning classification approaches, the on-demand sea-ice high spatial resolution (HSR) imagery management and processing service and framework allows for the efficient and accurate extraction of geophysical features and the spatiotemporal analysis of sea-ice leads.

Abstract Marine geological and geophysical data from Alpha Ridge in the Arctic Ocean are sparse because of thick perennial sea-ice cover, which prevents access by most surface vessels. Rare seismic data in this area, acquired largely from drifting ice-camps, had shown the hemipelagic drape that covers most of the ridge is highly disrupted within a large (>90 000 km 2 ) south central region. Here, evidence of pronounced seafloor erosion and debris flows infilling seafloor lows was previously interpreted to be the result of a possible bolide impact. In recent years, several icebreaker expeditions have successfully acquired multibeam bathymetry and sub-bottom profiler data in the western segment of this region. Analysis of these data reveals a complex seafloor morphology characterized by ridges and troughs, angular blocks and escarpments as well as seismic facies characterized by hyperbolic seafloor reflections, and convoluted to incoherent and transparent sub-bottom reflectivity. These features are interpreted as evidence of sediment mass movement with varying degrees of lateral transport deformation. At least two episodes of failure are interpreted based on the presence of both buried and surficial mass-transport features. As multiple events are interpreted, seismicity is the most plausible trigger mechanism rather than bolide impact.