Decoding glacial landscapes using automated geomorphological mapping and machine learning

使用自动地貌测绘和机器学习解码冰川景观

• 批准号: 2863174
• 金额: --
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2863174
• 关键词: Decoding; glacial; landscapes; using; automated

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.

极地冰盖和山地冰川的冰融化将是21世纪海平面上升的最大贡献者，但对未来冰量损失率和模式的预测仍存在不确定性。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，可以用来帮助对未来的行为进行更可靠的预测。北极、南极和山区的景观提供了一系列空间和时间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; Paxman等人，2021年）。这反过来又可以揭示过去冰的范围和动态的重要见解。然而，由于交通不便，对其中许多地区的地貌演变和冰川历史了解甚少。随着最近获得的大型冰下地形数据集（例如，MacGregor等人，2021年）和开发高分辨率的暴露地形数字高程模型（例如，“北极数字高程模型”），现在有重要的机会进行系统分析的区域和大陆规模的地形。该项目的目的是利用自动化技术绘制冰下和/或陆上景观的形态图，进而重建侵蚀模式和过去冰的范围和动态。学生将建立在最近开发的方法，如连续谷宽测量（Clubb等人，2022）和使用自动分类方案来识别冰下环境（Jamieson等人，2014年）。地貌解释将与数值冰盖建模和（如有）近海沉积物记录的年代学相结合，以限制过去的冰川和气候条件。该项目是多学科的，有机会让学生发展景观形态分析，使用地质统计技术和机器学习，以及数值建模的专业知识。还有一种选择是进行实地工作，以确定自动化方法的真实性。学生将密切嵌入在地理系在达勒姆，这是世界领先的冰川和河流景观和冰盖历史的研究“海平面，冰和气候”和“流域和河流”的研究集群。该学生还将花时间与纽卡斯尔大学的主管罗斯一起研究冰探测雷达数据和地貌解释。更广泛地说，该项目将涉及国际研究方案的关键目标，如南极研究科学委员会内的南极洲不稳定性和不稳定性，并涉及与地貌学、冰川学和古气候学方面的国际研究人员的合作。