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.

在火山喷发之前，地面会因岩浆或热液在深处的运动而发生变形，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）记录的。 （DEM）和全球导航卫星系统（GNSS）然后可以利用这些数据来确定火山源参数和喷发。此外，DInSAR、DEM 和 GNSS 可以集成以形成具有增强地面运动的三维（东、北、上）时间序列。综合结果以单个像素位置的绘制时间序列或大区域的变形图的形式提供。绘制的时间序列仅包含时间分量，而变形图则包含时间和空间元素。第一的仅使用时间敏感输入进行训练，然后与使用由空间和时间属性组成的集成变形图训练的算法进行比较，分析机器学习算法训练数据中空间信息的影响将导致对地壳到地下动力学的基本认识。 ，帮助对火山喷发的每个阶段进行分类，更准确地估计岩浆储存库或运输路径的位置和几何形状，并更好地描述或预测随时间变化的环境变化。该项目有助于实现 NSF 促进科学进步的使命。与社会相关的成果将包括但不限于开发灾害早期预警和火山研究的增强处理框架、拯救人类生命或保护自然灾害造成的社区基础设施的潜力，以及提高公众意识和水平。科学方法知识。火山观测研究领域的最大挑战涉及如何准备、何时何地预测喷发或高强度事件，以更好地了解磁结构、喷发行为，并改进灾害早期预警。该项目将支持标准工作流程的开发，其中机器学习方法用于对多波段合成孔径雷达（SAR）、全球导航卫星系统（GNSS）和高分辨率数字高程模型进行建模。在冰岛、夏威夷和加那利群岛的喷发火山上收集的 (DEM) 数据将被整合，以生成包含地表的新颖、高分辨率、三维（东、北、上）时间序列该项目将为研究人员提供免费、复杂的大地测量产品和处理程序，并将通过合并各种源几何形状和位置来扩展现有的火山模型，以更好地约束每个火山系统特有的物理参数。美国地质调查局火山观测站、冰岛大学和西班牙国家研究委员会（IPNA-CSIC 和 IGEO-CSIC）合作者的物理模型将利用卫星观测来量化火山活动合成，解决局部到区域尺度上的磁输运行为，反演地下结构（例如压力体或位错源），并生成合成训练数据。机器学习算法将被训练以检测遥感图像和图像之间的异常运动或梯度。用于火山监测和预测应用的时间序列。绘制的时间序列将通过长期短记忆（LSTM）算法进行流式传输，以预测下一个最可能的地面点位置。神经网络 (CNN) 图像分类算法将使用 3D、高分辨率、累积地面变形图进行训练，其中涉及双时空分量。这些方法将确定如何使用表面相互作用信号来评估火山动荡以及邻近地区的地形变化。像素会影响机器学习算法消耗或处理知识的方式，以及它们预测火山爆发各个阶段的效率和可靠性，最终，这项研究的最终目标是构建一个能够吸收的可扩展系统。来自不同来源和领域的数据集（即地震、气体排放、表面温度、潮汐计、倾斜仪、热能等），并识别所有信号的模式，以提醒科学家注意重大自然灾害事件，这样做将推进大地测量技术和处理方法，支持有关危险事件发生的科学分析，并为附近社区的安全提供喷发预警。该项目由地球科学局共同资助，以支持人工智能/机器学习在该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。