Artificial Neural Networks: A New Landscape Characterization Toolbox

该项目使用自组织地图（一种人工神经网络技术）来确定冰川过程在横贯南极山脉（TAM）隆起中的作用。侵蚀和造山之间的联系是已知的，但很难解决，因为这两个过程是动态耦合的。TAM为探索这种关系提供了一个独特的机会，因为它们自晚渐新世以来一直处于构造静止状态，但经历了各种形式的冰川作用。以前对其景观演变的调查主要基于对地形和地表特征的实地考察，这是一项耗时且后勤困难的工作。虽然这些研究已经记录了气候对TAM的不同影响，以及河流、冰川和沙漠过程在塑造景观中的重要性，但我们仍然没有对整个TAM冰川侵蚀的影响进行统计。该项目使用数字高程模型和自组织地图分析来提供定量指标，以表征冰川地貌，并确定冰川在造山过程中的总体作用。这项工作的更广泛影响是通过展示气候和地貌之间的联系，提高社会对全球气候变化的理解。

This project uses self-organizing maps, a form of artificial neural network techniques, to determine the role of glacial processes in uplift of the Transantarctic Mountains (TAM). The link between erosion and mountain building is known, but difficult to resolve because the processes are dynamically coupled. The TAM offer a unique opportunity to explore this relationship because they have been tectonically quiescent since the late Oligocene, but have undergone various forms of glaciation. Previous investigations of their landscape evolution have been based primarily on field inspection of landforms and surface features, a time intensive and logistically difficult undertaking. While these studies have documented the varying climatic influences on the TAM and the importance of fluvial, glacial, and desert processes in shaping the landscape, we are still left without an accounting of the impact of glacial erosion along the TAM as a whole. This project uses digital elevation models and self-organizing map analysis to provide quantitative metrics that characterize glacial landforms and determine the overall role of glaciation in mountain-building.The broader impacts of this work are improving society's understanding of global climate change by demonstrating links between climate and landform.