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Collaborative Research: Using Precursor Information to Update Probabilistic Hazard Maps

协作研究：使用前体信息更新概率危险图

基本信息

批准号：
1821289
负责人：
Robert Wolpert
金额：
$ 20万
依托单位：
Duke University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1821289&HistoricalAwards=false
关键词：
Collaborative Research Using Precursor Information

项目摘要

In many natural hazard scenarios, precursory information becomes available before an event. Responding to an impending hazard means that time is limited; analysis and decision-making must proceed on an accelerated timetable. This project develops methodology for responding to signals from a variety of data sources that have been interpreted as suggesting that a volcanic eruption is threatened. Prior research has attempted to elucidate how physical processes predict an eruption. For example, seismic signals, gas emissions, and tilt data have all been implicated as volcanic eruption precursors. But none of these signals have been shown to make robust predictions. This project undertakes a different approach, developing methods to integrate precursor data, decide on the likely evolution of eruption scenarios, and rapidly build simulation studies and statistical emulators, to provide timely and actionable information on which to decide a course of action. The new methodology will provide tools to rapidly construct probability-based hazard forecast maps for cascading geophysical events.The prediction and management of extreme events, from volcanic eruptions to floods to stock market crashes, requires a careful analysis of the hazard event, its inputs, and its consequences. Data of different kinds, and of differing fidelity, must be incorporated into a detailed analysis of the impending hazard. The investigators will build upon their past research characterizing volcanic hazards. This work provides long-term hazard analysis and provides a bridge from incoming precursory information, such as seismic signals and gas emission, to eruption impacts, such as likely paths of mass flows. The investigators aim to develop methodology to update input distributions for physical simulations and to integrate outcomes into new adaptive designs for surrogate construction, rapid evaluations of limited simulations, and massive parallel emulation. The project will also investigate a methodology based on observed power-law relationships between precursory information and their growth to estimate the time to eruption and other outcomes under uncertain data.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.

在许多自然灾害情景中，在事件发生之前就可以获得初步信息。对即将发生的危险作出反应意味着时间有限;分析和决策必须按照加速的时间表进行。该项目制定了对来自各种数据来源的信号作出反应的方法，这些信号被解释为火山爆发的威胁。先前的研究试图阐明物理过程如何预测喷发。例如，地震信号、气体排放和倾斜数据都被认为是火山爆发的前兆。但这些信号都没有被证明能做出可靠的预测。该项目采取了不同的方法，制定方法来整合前兆数据，决定火山爆发情景的可能演变，并迅速建立模拟研究和统计模拟器，以提供及时和可采取行动的信息，据以决定行动方针。新的方法将提供工具，迅速建立基于概率的灾害预测图级联地球物理事件的预测和管理的极端事件，从火山爆发到洪水股市崩溃，需要仔细分析灾害事件，其投入，其后果。在对即将发生的危险进行详细分析时，必须结合不同种类和不同保真度的数据。调查人员将在他们过去对火山危险的研究基础上进行研究。这项工作提供了长期的危险分析，并提供了从地震信号和气体排放等外来信息到物质流可能路径等喷发影响的桥梁。研究人员的目标是开发方法来更新物理模拟的输入分布，并将结果整合到新的自适应设计中，用于代理构建，快速评估有限的模拟和大规模并行仿真。该项目还将研究一种方法，该方法基于观测到的观测信息及其增长之间的幂律关系，以估计喷发时间和不确定数据下的其他结果。该奖项反映了NSF的法定使命，并被认为值得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来支持。