Global climate change is an imminent threat to sea levels. Earth’s ice sheets continuously melt due to greenhouse gases.  If both the Greenland Ice Sheet and the Antarctic ice sheet were to completely melt, the sea level would rise 66m according to the National Snow and Ice Data Center. With the constant melting of these two Ice Sheets, glaciologists need to detect either their melting rate or rate of depreciation. The problem is that Glaciologists often spend extensive hours ‘Ground Truthing’, manually detecting and outlining desired layers of ice sheets within echograms, x-ray images of ice sheets. Is there a way that this process can be modified in order to produce accurate results in a fraction of the time? Using an active contour methodology, we will (1) compare several different approaches: levelsets, snakes, and hidden markov model to the previously outlined ground truth layers. We will then use these results to answer the question: (2) “What is the best method to use in finding/creating an automated system for ice sheet layer detection?”  In addition, we will (3) use genetic algorithms to find the optimum layer detection parameters. With genetic algorithms, each individual image will be able to gather a set of its very own optimum parameter data for desired layer outlines. Currently, we are continuing research and are beginning our work with coding the snakes approach and next we will use genetic algorithms to modify this approach. At the end of our research project, we will hope to find an automated process to accurately, and efficiently identify distinct ice sheet layers (e.g. bedrock, surface, etc.). Our research efforts will affect the glaciology community in a revolutionary fashion and will no longer require enduring ours of ground truthing, but easily identify glacier rate of depreciation from any given echogram.