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

在火山喷发之前，地表会因深部岩浆或热液的运动而变形。这些地表运动模式是使用高分辨率卫星遥感技术记录的，例如差分干涉合成孔径雷达(DInSAR)、数字高程模型(DEM)和全球导航卫星系统(GNSS)。然后，高级计算模型可以利用这些数据来确定火山源参数和喷发阈值，跟踪岩浆运输行为，并为训练机器学习算法创建额外的合成时间序列数据。此外，DInSAR、DEM和GNSS可以结合在一起，形成具有增强的地面运动测量的三维(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变形贴图的形式提供。绘制的时间序列只包含时间成分，而形变地图既包含时间元素又包含空间元素。机器学习算法将首先只使用时间敏感的输入进行训练，然后与使用包含空间和时间属性的综合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的空间信息的影响，将导致对地壳对地下动力学的基本认识，有助于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，并更好地表征或预测随着时间的环境变化。该项目有助于实现美国国家科学基金会促进科学进步、繁荣和福利的使命。具有社会意义的成果将包括但不限于为灾害预警和火山研究制定更好的处理框架，拯救人类生命或保护因自然灾害造成的社区基础设施的潜力，以及提高公众对科学方法的认识和知识。火山观测研究领域的最大挑战涉及如何或何时何地预测喷发或高震级事件。为了更好地了解岩浆结构、喷发行为和改进灾害预警系统，该项目将支持开发一个标准工作流程，其中使用机器学习方法对大地形变数据进行建模。在冰岛、夏威夷和加那利群岛火山喷发上空收集的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(DEM)数据将被整合，以产生新的、高分辨率的三维(东、北、上)时间序列，其中包含提高精度的地表形变测量。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例程，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。冰岛大学USGS火山观测站和西班牙国家研究理事会(IPNA-CSIC和IGEO-CSIC)的合作者的高级数值和物理模型将利用卫星观测来量化火山成分，解决地方到区域尺度上的岩浆输送行为，反演压力体或位错源等地下结构，并生成综合训练数据。将训练机器学习算法，以检测遥感图像和时间序列之间的异常运动或梯度，用于火山监测和预报应用。绘制的时间序列将通过长期短期记忆(LSTM)算法进行流传输，以预测地点的下一个最可能的位置。卷积神经网络(CNN)图像分类算法将使用3D、高分辨率、累积地面变形图进行训练，其中涉及双重时空分量。这些方法将决定如何使用相互作用的地表信号来评估火山骚乱，邻近像素的地形变化如何影响机器学习算法消耗或处理知识的方式，以及它们在预测火山喷发的不同阶段时的效率和可靠性。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领域(即地震、气体排放、地表温度、潮汐测量仪、倾斜仪、热等)获取数据集，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，支持关于危险事件发生的科学分析，并有助于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，以支持地学领域的AI/ML进步。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.