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

2014年华盛顿州奥索滑坡导致43人死亡，2010年冰岛埃亚菲亚德拉冰盖火山喷发的火山灰柱导致欧洲航空旅行关闭，这些都是罕见的灾难性地球物理事件的例子。它们的罕见性使得这些事件几乎不可能预测，如果预测仅基于以前的观察。 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，这些模型通常需要大量的计算资源来运行。 此外，像这样的事件可能是最好的描述，由一系列不同的模型，不同的现象，在不同的尺度。例如，研究人员可能需要结合联合收割机降雨模型、斜坡破坏模型和滑动碎片模型来创建滑坡事件的整体模型。 本研究的主要目标是开发有效的统计和计算策略，以联合收割机这样的模型，从而推进在灾害forecasting.Direct基于模拟的灾害评估的艺术状态将需要成千上万的链接，时空模拟。 此外，为了在灾害评估中发挥最大作用，这些模拟应得到观测数据集的信息和验证，这些数据集本身可以是稀疏的数据（罕见事件），也可以是大量的数据（例如卫星数据），并应针对新出现的情景进行探索。 使问题复杂化的是，感兴趣的问题的许多特征要么被很差地表征要么不可预测，并且人们希望在它们中的每一个的值的范围内运行仿真程序;这很快导致感知到需要针对数十万或数百万个参数值和条件的不同组合运行仿真程序（这可能需要数小时才能完成）。根本没有足够的时间或足够的计算能力让这种暴力方法成功。为了解决刚才描述的情况，PI将继续为大量时空模拟器数据开发并行部分仿真器，允许在地球物理模拟中常用的自适应时空网格上构建仿真器，为其输出创建平滑器，并允许使用减少的输入空间。PI将开始研究通过多个模拟器连接多个模拟器的策略。 一个特别强大的半分析的方式链接仿真器将追求，围绕该方法的准确性，以及其实施的可能性，在巨大的数据场景中设想的并行部分仿真器所产生的各种研究问题。PI还将开始研究提取（接近）最佳基集的技术、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，用于为级联地球物理事件快速绘制基于概率的灾害预报图。快速预报地图使最终用户能够在各种各样的任意情况下进行危险分析。此外，这种新的方法将能够快速评估认识的不确定性。这种方法构成了基于科学的决策支持的显着改进。

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.