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，但不确定性仍在未来的未来比率和冰质量损失的模式中。冰圈对地球历史上气候变化的过去发作的反应提供了一个重要的模拟，可用于帮助建立对未来行为的更强大的预测。北极，南极和山区的景观在一系列的空间和时间尺度上提供了历史冰川和河流侵蚀活性的宝贵记录（例如Rose等，2013; Paxman等，2021）。反过来，这可以将重要的见解带入过去的冰范围和动态。但是，由于它们的无法获取，许多这些地区的景观进化和冰川历史知之甚少。随着最近收购大型亚冰川地形数据集（例如MacGregor等，2021），并开发了裸露地形的高分辨率数字高程模型（例如，“ Arcticdem”），现在有重大的对系统分析的机会区域和大陆规模的地形。该项目的目的是使用自动化技术来绘制亚冰川和/或亚赛下景观的形态，然后又重建了侵蚀以及过去的冰范围和动态的模式。该学生将基于最近开发的方法，例如连续山谷宽度测量（Clubb等，2022），并使用自动分类方案来表征亚冰分环境（Jamieson等，2014）。地貌解释将与数值冰盖建模和（如果在可用的情况下）从海上沉积物记录中进行集成，以限制过去的冰川和气候条件。该项目是多学科的，学生有机会在景观形态分析，地统计技术和机器学习以及数值建模方面发展专业知识。还可以选择进行现场工作来基于自动化方法。该学生将在达勒姆（Durham）地理部的“海平面，冰和气候”和“流域和河流”研究集群中紧密嵌入，该研究集群是在研究冰川和河流景观和冰原历史的研究中。该学生还将花时间与纽卡斯尔大学的Ross主管Ross一起从事冰上的雷达数据和地貌解释。更广泛地说，该项目将解决国际研究计划的关键目标，例如即时（南极的不稳定性和阈值；在南极研究科学委员会内），并涉及与国际地貌学，冰川学和palaeoclimate的国际研究人员的合作。