Collaborative Research: Advancing Statistical Surrogates for Linking Multiple Computer Models with Disparate Data for Quantifying Uncertain Hazards

2014年导致43人死亡的华盛顿州奥索山体滑坡，以及2010年(冰岛)埃亚菲亚德拉火山喷发导致欧洲航空旅行中断的火山灰羽流，都是罕见和灾难性地球物理事件的例子。如果预测仅仅基于之前的观察，它们的罕见性质使得此类事件几乎不可能预测。为了捕捉罕见事件，研究人员必须依赖复杂的物理和数学模型，这些模型往往需要大量的计算资源才能进行练习。此外，用一系列不同尺度的不同现象的不同模型来描述这样的事件可能是最好的。例如，研究人员可能需要将降雨模型、斜坡破坏模型和滑动碎石模型组合在一起，以创建滑坡事件的整体模型。这项研究的主要目标是开发有效的统计和计算策略来结合这些模型，从而促进危险预测的最新水平。基于直接模拟的危险评估需要数千到数万个链接的时空模拟。此外，为了在危险评估中发挥最大作用，这些模拟应由观测数据集通报和验证，观测数据集本身的范围可以从稀疏数据(罕见事件)到海量数据(例如卫星数据)，并探索新出现的情景。使问题复杂化的是，感兴趣的问题的许多特征要么表征不佳，要么不可预测，并且人们希望以它们每一个的值的范围运行模拟程序；这很快导致感觉到需要为数十万或数百万不同的参数值和条件组合运行模拟程序(这可能需要数小时才能完成)。没有足够的时间或足够的计算能力让这种蛮力方法取得成功。为了解决上述情况，PI将继续开发用于海量时空模拟器数据的并行部分模拟器，允许在地球物理模拟中常用的自适应时空网格上构建模拟器，为其输出创建更平滑的模拟器，并允许使用减少的输入空间。PI将开始研究通过多个仿真器连接多个仿真器的策略。将寻求一种特别强大的半解析方式来链接仿真器，围绕该方法的准确性以及在为并行部分仿真器设想的巨大数据场景中实施该方法的可能性而产生的各种研究问题。PIS还将开始研究提取(几乎)最优基集的技术、数据简化方法和加速仿真器构建的算法方法，所有这些都有助于更稳健地处理大型数据集。这些新的方法将提供工具，为级联地球物理事件快速构建基于概率的灾害预报图。快速预测地图允许最终用户在各种任意情景下执行危险分析。此外，这一新方法将能够快速评估认识上的不确定性。这一方法是对以科学为基础的决策支持的重大改进。

The Oso, Washingon landslide of 2014, which resulted in 43 fatalities, and the ash plumes from the Eyjafjallajökull (Iceland) eruption of 2010, which shut down air travel in Europe, are examples of rare and catastrophic geophysical events. Their rare nature makes such events nearly impossible to forecast, if forecasts are based only on previous observations. To capture rare events, researchers must rely on complex physical and mathematical models that often require significant computational resources to exercise. Furthermore, events like these may be best described by a series of different models of different phenomena at different scales. For example, a researcher may need to combine a model of rainfall, a model of slope failure, and a model of sliding debris to create on overall model for a landslide event. The main objective of this research is the development of efficient statistical and computational strategies to combine such models, thus advancing the state of the art in hazard forecasting.Direct simulation-based hazard assessment would require thousands to tens of thousand of linked, space-time simulations. Furthermore, to be of most use in hazard assessment, these simulations should be informed and validated by observational data sets, which themselves can range from sparse data (rare events) to massive data (e.g. satellite data), and explored for emerging scenarios. To complicate the matter, a number of features of the problems of interest are either poorly characterized or unpredictable, and one would like to run the simulation programs at a range of values of each of them; this quickly leads to a perceived need to run a simulation program (which may take hours to complete) for hundreds of thousands or millions of different combinations of parameter values and conditions. There simply is not enough time or enough computing power for such a brute force approach to succeed. To tackle the situation just described, the PIs will continue to develop parallel partial emulators for massive space-time simulator data allowing emulator construction on the adaptive space-time grids commonly used in geophysical simulations, creating smoothers for their output, and enabling the use of reduced input spaces. The PIs will begin the investigation of a strategy for linking multiple simulators via multiple emulators. A particularly powerful semi-analytic way of linking emulators will be pursued, with a variety of research questions arising centering around the accuracy of the method, as well as the possibility of its implementation in the huge data scenario envisaged for the parallel partial emulator. The PIs will also begin to investigate techniques to extract (nearly) optimal basis sets, data reduction methods, and algorithmic approaches to accelerate the construction of emulators, all of which contribute to a more robust handling of large datasets. These new methodologies will provide tools to rapidly construct probability-based hazard forecast maps for cascading geophysical events. Rapid forecast maps allow end users to perform hazard analysis under a wide variety of aleatoric scenarios. Furthermore this new methodology will enable fast assessment of epistemic uncertainties. This approach constitutes a dramatic improvement in scientifically-based decision support.