Postdoctoral Fellowship: EAR-PF: Exploring the significance of the spatial field from integrated DInSAR+GNSS time series for machine learning and volcano early warning applications

博士后奖学金：EAR-PF：探索集成 DInSAR GNSS 时间序列的空间场对于机器学习和火山预警应用的重要性

• 批准号: 2304871
• 负责人: Brianna Corsa
• 金额: $ 18万
• 依托单位: Corsa, Brianna Durelle
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304871&HistoricalAwards=false
• 关键词: Postdoctoral; Fellowship; EAR; PF; Exploring

Leading up to volcanic eruptions, the ground deforms in response to the movement of magma or hydrothermal fluids at depth. These surface motion patterns are recorded using high-resolution satellite remote sensing techniques such as Differential Interferometric Synthetic Aperture Radar (DInSAR), Digital Elevation Models (DEMs), and Global Navigation Satellite Systems (GNSS). Advanced computational models can then harness this data to determine volcanic source parameters and eruptive thresholds, track magmatic transport behavior, and create additional synthetic time series data for training machine learning algorithms. Furthermore, DInSAR, DEMs, and GNSS may be integrated to form a three-dimensional (east, north, up) time series with enhanced ground motion measurements. Integrated results are delivered either as a plotted time series at a single pixel location or as deformation maps over large regions. The plotted time series only contain a temporal component, while the deformation maps contain both temporal and spatial elements. Machine learning algorithms will first be trained using only the time-sensitive input, then compared to algorithms trained with integrated deformation maps consisting of both spatial and temporal properties. Analyzing the effects of spatial information within the machine learning algorithm’s training data will lead to essential awareness of crustal to subsurface dynamics, help classify each stage of a volcanic eruption, more accurately estimate locations and geometries of magmatic storage reservoirs or transport pathways, and better characterize or forecast environmental changes through time. This project contributes towards NSF’s mission to promote the progress of science, prosperity, and welfare. Societally relevant outcomes will include but are not limited to the development of enhanced processing frameworks for hazard early warning and volcanic research, the potential to save human life or protect community infrastructure due to natural hazards, and to increase public awareness and knowledge of scientific methods.The greatest challenges in the field of volcanic observation research involve how to prepare for, or when and where to anticipate eruptive or high-magnitude events. To better understand magmatic structure, eruptive behavior, and to improve hazard early warning systems, this project will support the development of a standard workflow in which machine learning approaches are used to model geodetic deformation data. Multi-band Synthetic Aperture Radar (SAR), Global Navigation Satellite Systems (GNSS), and high-resolution Digital Elevation Model (DEM) data collected over eruptive volcanoes in Iceland, Hawaii, and the Canary Islands will be integrated to generate novel, high-resolution, three-dimensional (east, north, up) time series containing surface deformation measurements with improved precision. This project will provide researchers with free, complex geodetic products and processing routines, and will expand on existing volcanic models by merging various source geometries and locations to better constrain the physical parameters unique to each volcanic system. Advanced numerical and physical models from collaborators at the USGS Volcano Observatories, University of Iceland, and the Spanish National Research Council (IPNA-CSIC and IGEO-CSIC), will use the satellite observations to quantify volcanic composition, resolve magmatic transport behavior over local to regional scales, invert for subsurface structures such as pressure bodies or sources of dislocation, and generate synthetic training data. Machine learning algorithms will be trained to detect anomalous motions, or gradients, between remote sensing images and time series for volcanic monitoring and forecasting applications. Plotted time series will be streamed through Long-Term-Short-Memory (LSTM) algorithms to predict the next, most-likely position of a ground point. Convolutional Neural Network (CNN) image classification algorithms will be trained using 3D, high-resolution, cumulative ground deformation maps, which involve dual spatiotemporal components. These methods will determine how interacting surface signals may be used to evaluate volcanic unrest, how topographic change from neighboring pixels affects the ways in which machine learning algorithms consume or process knowledge, and how efficient and reliable they are at forecasting various phases of an eruption. Ultimately, the end goal of this research is to build a scalable system capable of ingesting datasets from disparate sources and domains (i.e., seismic, gas emission, surface temperature, tide gauge, tiltmeters, thermal, etc.), and recognizing patterns across all signals to alert scientists to major natural hazard events. Doing so will advance geodetic technology and processing methods, support scientific analyses regarding the onset of hazardous events, and contribute towards eruption early warning for the safety of nearby communities.This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

导致火山喷发后，地面响应于深度岩浆或热液流体的运动而变形。这些表面运动模式是使用高分辨率卫星遥感技术（例如差分干涉合成孔径雷达（Dinsar），数字高程模型（DEMS）和全球导航卫星系统（GNSS）记录的。然后，先进的计算模型可以利用此数据来确定火山源参数和喷发阈值，跟踪岩浆传输行为，并创建用于训练机器学习算法的其他合成时间序列数据。此外，丁萨，Dems和GNSS可以集成以形成三维（东，北，UP）时间序列，并具有增强的地面运动测量。集成的结果是在单个像素位置作为绘制时间序列的绘制时间序列，或在大区域上作为变形图。绘制的时间序列仅包含临时组件，而变形图既包含临时元素和空间元素。机器学习算法将首先仅使用时间敏感的输入进行训练，然后将其与由空间和临时属性组成的集成变形图训练的算法进行比较。分析机器学习算法中空间信息的效果将导致对地下动态的地壳的重要意识，有助于对火山喷发的每个阶段进行分类，更准确的估计位置和岩浆存储储量或运输路径的几何形状，并通过时间来更好地表征或预测环境通过时间变化。该项目有助于NSF促进科学，繁荣和福利的进步。社会相关的结果将包括但不限于发展危险预警和火山研究的加工框架的发展，挽救人类生命或保护人类生命或保护社区基础设施的潜力以及对科学方法的公众认识和对科学方法的知识的提高，这是Volcanic观察研究领域的最大挑战，这些挑战涉及到现场的最大挑战，可以使事件变得越来越高，以期预期或在何时挑战或折磨。 To better understand magmatic structure, eruptive behavior, and to improve hazard early warning systems, this project will support the development of a standard workflow in which machine learning approaches Multi-band Synthetic Aperture Radar (SAR), Global Navigation Satellite Systems (GNSS), and high-resolution Digital Elevation Model (DEM) data collected over eruptive volcanoes in Iceland, Hawaii, and the Canary Islands will be integrated to generate novel,高分辨率，三维（东，向北，UP）时间序列，其中包含表面变形测量，并提高了精度。该项目将为研究人员提供免费，复杂的测量产品和加工程序，并通过合并各种源几何和位置来扩展现有火山模型，以更好地限制每个火山系统独特的物理参数。 Advanced numerical and physical models from collaborators at the USGS Volcano Observatories, University of Iceland, and the Spanish National Research Council (IPNA-CSIC and IGEO-CSIC), will use the satellite observations To quantify volcanic composition, resolve magmatic transport behavior over local to regional scales, invert for subsurface structures such as pressure bodies or sources of dislocation, and generate synthetic training data.机器学习算法将经过培训，以检测遥感图像和用于火山监测和预测应用的时间序列之间的异常运动或梯度。绘制的时间序列将通过长期存储（LSTM）算法进行流式传输，以预测下一个地面的下一个位置。卷积神经网络（CNN）图像分类算法将使用3D，高分辨率，累积的地面变形图训练，该图涉及双时空组件。这些方法将确定如何使用相互作用的表面信号来评估火山动荡，相邻像素的地形变化如何影响机器学习算法消耗或过程知识的方式，以及它们在预测的各种阶段的效率和可靠性。最终，这项研究的最终目标是建立一个可扩展的系统，能够从不同的来源和域（即地震，气体发射，表面温度，潮汐量规，倾斜度，tiltmeters，Thermal等）摄取数据集，并识别所有信号以向大型自然危害事件发出警告。这样做将推进大地技术和处理方法，支持有关危险事件发作的科学分析，并有助于对近社区安全的喷发预警。该项目由地球科学局共同资助了地球科学局，以支持AI/ML在地理上的AI/ML的进步，以反映了NSF的Infortral and Infortial the Infortial deem deem deem deem deem deem deem deem，影响审查标准。