Pockmarks are crater-like depressions in marine or lacustrine sediments. They are often interpreted as the surface manifestation of hydrocarbon venting but may also result from freshwater flow in coastal regions, compaction induced sediment dewatering, or bottom scouring around natural or anthropogenic objects. Hence, they can be of relevance for the global carbon cycle, offshore infrastructure, benthic life, and slope stability. Pockmarks and associated gas-bubbles in the water-column can be mapped with modern ship-based multibeam systems. However, to understand their relevance for geologic and hydrodynamic processes in the oceans, repeated and multi-disciplinary data sets are necessary that resolve their formation mechanism and transient seep activity. Owing to the presence of a large cabled underwater observatory, a wealth of multi-resolution and multi-disciplinary seafloor data is available from a recently discovered huge pockmark field at Barkley Canyon (Canada). Available data include repeated multibeam surveys, seafloor video footage, seismic and EK60 echo-sounder profiles, dredged boulders from the pockmarks, and multibeam water-column information. Many of these data sets have been collected repeatedly over the last 10 years. I will use this data to analyse, for the first time, how the distribution, morphology and seepage activity of a pockmark field has developed over a period of ten years. The results will allow formulating a comprehensive model that puts the Barkley Canyon pockmarks into the regional geological and hydrodynamical context and that will help to better understand the processes that control pockmark formation and activity globally.

Pockmark fields, including the one around Barkley Canyon, typically yield several hundreds to thousands of pockmarks. This abundance constitutes a challenge for their analysis, but also a great opportunity for statistical evaluation. In order to support analyses of the Barkley Canyon pockmark field, I will create automated workflows that are able to map and characterize large numbers of morphologic features in digital elevation models. All morphologic workflows will be made publicly available for automated mapping and classification of large numbers of concentric features in digital elevation models.