We present a model study investigating the summer evolution of supraglacial lakes on the Greenland ice margin. Using a one-dimensional (1-D) model we calculate the surface ablation for a bare ice surface and beneath supraglacial lakes for 30 days in the summers of 1999 and 2001. The surface ablation beneath the lake was enhanced by 110% in 1999 and 170% in 2001 compared with the ablation for bare ice. We then use the results from the 1-D model to further model the vertical and horizontal evolution of the supraglacial lakes, the results of which are compared with satellite images. Within the region of the ice sheet where supraglacial lakes presently occur, the area covered by supraglacial lakes is found to be more or less independent of the summer melt rate but controlled by topography. We therefore predict that, inside this region, the area covered by supraglacial lakes will remain constant even in a warmer climate. However, in a warmer climate, surface melting will occur higher on the ice sheet where small surface slopes favour formation of large supraglacial lakes. Enhanced surface melting beneath such lakes is a hitherto overlooked feedback mechanism related to climate warming.

We explore propagation of seismic interpretation by deep learning in stacked 2D sections. We show the application of state-of-the-art image classification algorithms on seismic data. These algorithms were trained on big labeled photograph databases. We use transfer learning to benefit from pre-trained networks and evaluate their performance on seismic data. Presentation Date: Wednesday, October 17, 2018 Start Time: 8:30:00 AM Location: 204B (Anaheim Convention Center) Presentation Type: Oral