ATD: Data-driven stochastic source inversion algorithms for event reconstruction of biothreat agent dispersion

ATD：数据驱动的随机源反演算法，用于生物威胁剂扩散的事件重建

• 批准号: 1043107
• 负责人: Jodi Mead
• 金额: $ 46.68万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1043107&HistoricalAwards=false
• 关键词: ATD; Data; driven; stochastic; source

Realistic event reconstruction problems require PDE-based models, and incorporating sensor data into them must be efficient for emergency response purposes. The investigator and her colleagues will efficiently solve this problem using techniques from numerical linear algebra that do not require multiple simulations of the forward model. These techniques can be viewed from the Bayesian perspective as finding first and second moments for point and uncertainty estimates, respectively. Least squares estimates will be weighted with inverse covariance matrices found by a new technique developed as part of this work that does not require normally distributed errors. These weights make least squares estimates more accurate, and the approach is computationally more efficient than full Bayesian methods. The investigator and her colleagues will quantify uncertainty in the PDE based forward model, while accounting for both data and parameter uncertainty. These PDE-based models will adopt a multi-GPU computing paradigm for overall acceleration of the algorithms for threatdetection, and it is expected that near real-time inversions of the three-dimensional contaminant dispersion model will be produced.This problem is motivated by the fact that in their June 2008 report (GAO-08-180) to Congressional requesters, the Government AccountabilityOffice (GAO) has found that ?While the Department of Homeland Security (DHS) and other agencies have taken steps to improve homeland defense, local first responders still do not have tools to accurately identify right away what, when, where, and how much chemical, biological, radiological, or nuclearmaterials are released in U.S. urban areas, accidentally or by terrorists?. DHS has deployed the BioWatch program in several major cities to monitor the air for biothreat agents. The number of sensors in urban areas is limited, and a reliable account of the chemical-biological dispersion event and its impact on the population cannot be created purely from measurements. The PI and her colleagues will develop computationally fast mathematical algorithms to reconstruct the dispersion of a chemical or biological agent that is detected by a sensor network. This will allow first responders to identify and quantify the location and amount of chemical-biological agent release. Once the dispersion event is backtracked in time it can then be projected forward using high-fidelity atmospheric transport and dispersion models to predict the hazard zone for emergency response and hazard mitigation. The problem under consideration is equally significant in defense operations on the battlefield, where estimates on the location, strength and time of chemical-biological agent release can support tactical decisions such as areas to avoid, protective gear usage and medical response.

现实的事件重建问题需要基于偏微分方程的模型，并将传感器数据到他们必须是有效的应急响应的目的。研究人员和她的同事将使用数值线性代数的技术有效地解决这个问题，这些技术不需要对前向模型进行多次模拟。这些技术可以从贝叶斯的角度来看，分别为点和不确定性估计找到一阶和二阶矩。 最小二乘估计值将与逆协方差矩阵进行加权，该矩阵由作为本工作一部分开发的新技术发现，该技术不需要正态分布误差。这些权重使得最小二乘估计更准确，并且该方法在计算上比完全贝叶斯方法更有效。 研究人员和她的同事将量化基于PDE的正演模型中的不确定性，同时考虑数据和参数的不确定性。 这些基于偏微分方程的模型将采用多GPU计算模式的整体加速算法的threatdetection，预计近实时反演的三维污染物扩散models.This问题的动机是，在他们的2008年6月的报告（GAO-08-180）国会请求，政府问责局（GAO）已经发现，？虽然国土安全部（DHS）和其他机构已采取措施改善国土防御，但当地的第一反应人员仍然没有工具来准确地确定在美国城市地区意外或恐怖分子释放了什么，何时，何地以及多少化学，生物，放射性或核材料？国土安全部已在几个主要城市部署了BioWatch计划，以监测空气中是否存在生物威胁因子。城市地区的传感器数量有限，无法纯粹从测量数据中可靠地说明化学-生物扩散事件及其对人口的影响。PI和她的同事将开发计算速度快的数学算法，以重建传感器网络检测到的化学或生物制剂的分散。这将使第一反应者能够识别和量化化学生物制剂释放的位置和数量。一旦扩散事件被及时回溯，就可以使用高保真大气传输和扩散模型向前预测，以预测危险区，用于应急反应和减灾。 所考虑的问题在战场上的防御行动中同样重要，在战场上，对化学生物制剂释放的位置、强度和时间的估计可以支持战术决策，如避免的区域、防护装备的使用和医疗反应。