Deep learning and physics-based approaches for ice-ocean monitoring

用于冰海监测的深度学习和基于物理的方法

• 批准号: RGPIN-2022-03324
• 负责人: Scott, Andrea
• 金额: $ 3.13万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765920
• 关键词: Deep; learning; physics; based; approaches

Sea ice cover in the Arctic is undergoing significant change. Decreasing ice extent and thickness, and an increasing open water season, are driving increased pressures on this ecologically sensitive region. Ways to monitor these changes are desperately needed by ice service operations worldwide as well as governmental decision makers. Sea ice concentration is a key variable that indicates the fraction of a specified area of the ocean that is covered by ice. It is typically monitored using remote sensing data acquired in the low-frequency microwave portion of the electromagnetic spectrum because data at these frequencies are not sensitive to cloud cover or sunlight. Both passive and active microwave sensors are used for sea ice concentration monitoring. Passive microwave sensors measure the energy naturally emitted by the earth, while active microwave sensors send a signal to the earth and measure the backscatter. Passive microwave sensors are used to monitor sea ice concentration at large scales (e.g., 30 km), while synthetic aperture radar (SAR) sensors provide higher resolution data (e.g. 50 m). SAR sensors are well suited to monitoring the marginal ice zone, a realm where ice eddies, floes and waves significantly modulate the sea ice cover. The recent launch of multiple SAR constellation systems is providing a rapidly growing data volume from SAR. This has subsequently spurred strong interest in automated methods to extract information from these data. Deep learning is a data-driven approach that can learn patterns from data, and is well suited to this task. However, these methods are widely viewed as a `black box', which hinders widespread acceptance of deep learning in downstream applications. Erroneous information provided by a deep learning algorithm could endanger those operating on the ice. For example, fishing boats that lack ice-strengthening need an accurate ice edge location. While there are several 'opening the box' methods proposed, these are designed for the problem space of everyday images that have distinct objects and colours. SAR sea ice images are greyscale images of multi-scale phenomena. Additionally, sea ice is a physical system that can be modelled using differential equations. The proposed research program will exploit these aspects to develop tractable, scale-aware, physically guided approaches. Both real data and simulated data will be used to develop the deep learning approaches, which is different from past work in this problem domain. This will allow a thorough investigation of the proposed methodologies. Expected outcomes are improved estimates of sea ice concentration in the marginal ice zone from satellite sensors benchmarked quantitatively against current leading products; and a novel deep learning framework for multiscale physical systems.

北极的海冰覆盖正在发生重大变化。海冰面积和厚度的减少，以及开放水域季节的增加，正在给这个生态敏感地区带来越来越大的压力。监测这些变化的方法是全球冰服务运营以及政府决策者迫切需要的。海冰浓度是一个关键变量，它表明海洋某一特定区域被冰覆盖的比例。通常使用在电磁波谱的低频微波部分获得的遥感数据进行监测，因为这些频率的数据对云层或阳光不敏感。无源和有源微波传感器均用于海冰浓度监测。被动微波传感器测量地球自然发射的能量，而主动微波传感器向地球发送信号并测量后向散射。无源微波传感器用于监测大尺度（例如30公里）的海冰浓度，而合成孔径雷达（SAR）传感器提供更高分辨率的数据（例如50米）。SAR传感器非常适合监测边缘冰区，这是一个冰涡、浮冰和波浪显著调节海冰覆盖的区域。最近推出的多个SAR星座系统正在提供快速增长的SAR数据量，这随后激发了人们对从这些数据中提取信息的自动化方法的强烈兴趣。深度学习是一种数据驱动的方法，可以从数据中学习模式，非常适合这项任务。然而，这些方法被广泛视为“黑箱”，阻碍了深度学习在下游应用中的广泛接受。深度学习算法提供的错误信息可能会危及在冰上操作的人员。例如，缺乏冰强化的渔船需要精确的冰边缘位置。虽然提出了几种“打开盒子”的方法，但这些方法都是为具有不同物体和颜色的日常图像的问题空间而设计的。SAR海冰图像是多尺度现象的灰度图像。此外，海冰是一个可以用微分方程来模拟的物理系统。拟议的研究计划将利用这些方面来开发易于处理的、规模感知的、物理指导的方法。真实数据和模拟数据都将用于开发深度学习方法，这与过去在该问题领域的工作不同。这将允许对提议的方法进行彻底的调查。预期结果是通过卫星传感器对边缘冰区海冰浓度的改进估计，以目前的主要产品为定量基准；以及一种新的多尺度物理系统深度学习框架。