Decoding glacial landscapes using automated geomorphological mapping and machine learning

极地冰盖和山脉冰川的融化将是21世纪海平面上升的最大原因，但对未来冰块质量下降速度和模式的预测仍然存在不确定性。冰冻圈对地球历史上过去几次气候变化的反应提供了一个重要的类比，可以用来帮助开发对未来行为的更可靠的预测。北极、南极洲和山区的地貌提供了一系列时空尺度上的历史冰川和河流侵蚀活动的宝贵记录(例如，Rose等人，2013年；Paxman等人，2021年)。这反过来又可以对过去的冰层范围和动态提供重要的见解。然而，由于这些地区无法进入，人们对其中许多地区的景观演变和冰川历史知之甚少。随着最近获得大型冰下地形数据集(例如MacGregor等人，2021年)和开发高分辨率裸露地形数字高程模型(例如“ArcticDEM”)，现在有了对区域和大陆尺度地形进行系统分析的重大机会。该项目的目的是利用自动化技术绘制冰下和/或空中地形的地貌图，进而重建侵蚀模式和过去的冰川范围和动态。学生将建立在最近开发的方法上，例如连续的山谷宽度测量(Clubb等人，2022)和使用自动分类方案来描述冰下环境(Jamieson等人，2014)。地貌解释将与数值冰盖模型和近海沉积物记录的年代学(如有)相结合，以限制过去的冰川和气候条件。这个项目是多学科的，学生有机会发展景观形态测量分析，地质统计学技术和机器学习的使用，以及数值建模方面的专业知识。还有一种选择是进行实地考察，以查明自动化方法的真相。这名学生将密切参与达勒姆地理系的“海平面、冰与气候”和“流域与河流”研究集群，该学院在冰川和河流景观以及冰盖历史研究方面处于世界领先地位。这名学生还将与纽卡斯尔大学的导师罗斯一起花时间研究冰层探测雷达数据和地貌解释。更广泛地说，该项目将针对国际研究方案的主要目标，如INSTEST(南极洲的不稳定性和阈值；在南极研究科学委员会内)，并涉及与地貌学、冰川学和古气候方面的国际研究人员的合作。

Melting of ice from polar ice sheets and mountain glaciers will be the largest contributor to 21st Century sea level rise, but uncertainties remain in projections of future rates and patterns of ice mass loss. The response of the cryosphere to past episodes of climatic change in Earth history provides an important analogue that can be used to help develop more robust predictions of future behaviour. Landscapes in the Arctic, Antarctica, and mountainous regions provide a valuable record of historical glacial and fluvial erosive activity over a range of spatial and temporal scales (e.g., Rose et al., 2013; Paxman et al., 2021). This, in turn, can shed important insights into past ice extent and dynamics. However, owing to their inaccessibility, the landscape evolution and glacial history of many of these regions is poorly understood. With the recent acquisition of large subglacial topography datasets (e.g., MacGregor et al., 2021) and the development of high-resolution digital elevation models of exposed terrain (e.g., the 'ArcticDEM'), there are now significant opportunities for systematic analysis of regional- and continental-scale topography. The aim of this project is to use automated techniques to map the morphology of subglacial and/or subaerial landscapes and in turn reconstruct patterns of erosion and past ice extent and dynamics. The student will build on recently developed methods such as continuous valley width measurement (Clubb et al., 2022) and the use of automated classification schemes to characterise subglacial environments (Jamieson et al., 2014). Geomorphological interpretations will be integrated with numerical ice sheet modelling and (where available) chronology from offshore sediment records to constrain past glacial and climatic conditions. The project is multi-disciplinary, with opportunities for the student to develop expertise in landscape morphometric analysis, use of geostatistical techniques and machine learning, and numerical modelling. There is also an option to undertake fieldwork to ground-truth the automated methods. The student will be closely embedded in the 'Sea Level, Ice and Climate' and 'Catchments and Rivers' research clusters in the Department of Geography in Durham, which is world-leading in the study of glacial and fluvial landscapes and ice sheet history. The student will also spend time working on ice-sounding radar data and geomorphological interpretation with supervisor Ross at Newcastle University. More broadly, this project will address key goals of international research programmes such as INSTANT (INStabilities and Thresholds in ANTarctica; within the Scientific Committee on Antarctic Research), and involve collaboration with international researchers in geomorphology, glaciology, and palaeoclimate.