A Geospatial Liquefaction Model for Rapid Response and Loss Estimation

地震后立即用于评估强度和潜在影响的快速反应图和损失估计目前不包括液化危害的影响。因此，迫切需要发展新的方法来估计液化的可能性，这种方法可以从具体地震强度估计和简单的地理空间特征中迅速和广泛地得出。先验概率液化模型的一个基本限制是液化数据集包含很少的非液化点（抽样偏差）。将液化效应纳入损失估计和快速反应图的第二个挑战是，大多数液化模型依赖于特定地点和区域的数据集（例如地表地质图），这些数据集的收集既费时又费钱。该项目的初步结果表明，这些问题是如何通过从液化的代表性数据集开发逻辑回归模型来解决的，该模型作为关键输入参数的函数，可以很容易地从全球数据集（例如数字高程模型或DEM）和标准地震特定强度数据（例如峰值地面加速度）中估计出来。候选的解释变量包括由DEM导出的变量以及土壤饱和度、植被、气候和水文指数。在初步工作中，利用来自两个地区（日本神户和新西兰克赖斯特彻奇）的数据开发了一个逻辑回归模型，该模型基于峰值地面加速度、海拔、到海岸的距离和一个水文参数——复合地形指数——预测液化的概率，该参数被用作土壤饱和度的代理。该模型已在海地太子港进行了测试，并提供了液化概率的一致估计。这一论证表明，所提出的估算液化概率的新方法可以从地震特定烈度估算和简单的地理空间特征中快速而广泛地推导出来。然而，为了开发一个全球适用的地理空间液化模型，需要将该数据库扩展到更多的地质和气候环境，以便该模型能够约束地理空间代理的区域差异。在这个项目中，将根据全球地震开发一个地理空间液化数据库，其中的解释变量将是广泛可用的地理空间数据。抽样偏差将通过从代表液化真实分布的观测数据中开发数据库来解决。这标志着液化潜力模型发展的转变，迄今为止，液化潜力模型的发展主要集中在偏向于液化发生观察的病例历史数据库上。该项目的目标是：1)建立一个具有地理空间变量的全球液化观测数据库。2)测试饱和度和土壤密度的一阶代理3)标准化不同地貌和气候区域的代理4)开发概率地理空间液化模型5)培训本科土木工程师进行地震危害和损失估计。提议工作的更广泛影响和潜在变革方面是该模型的全球适用性，这将使液化效应能够包括在未来的快速反应地图，损失估计，以及世界上任何地方未来任何事件的情景模拟，从而改善灾害反应并减少损失。此外，塔夫茨大学地理信息系统课程的本科生研究和推广将用于向土木工程本科生介绍地震危害和地震损失估计的重要性。

Rapid response maps and loss estimates that are used immediately after an earthquake to assess intensity and potential impact do not currently include effects from liquefaction hazard. Thus, there is a critical need to develop new methods of estimating the likelihood of liquefaction that can be rapidly and broadly derived from both earthquake-specific intensity estimates and simple geospatial features. A fundamental limitation of prior probabilistic liquefaction models is that the liquefaction datasets contain few non-liquefaction sites (a sampling bias). A second challenge to including liquefaction effects in loss estimation and rapid response maps is that most liquefaction models rely on site- and region-specific datasets (e.g. surficial geology maps) that are time and cost intensive to collect. The preliminary results for this project demonstrate how these problems have been solved by developing logistic regression models from representative datasets of liquefaction as a function of key input parameters that can easily be estimated from global datasets (e.g. digital elevation models or DEM) and standard earthquake-specific intensity data (e.g. peak ground acceleration). Candidate explanatory variables include those derived from the DEM as well as indexes for soil saturation, vegetation, climate, and hydrology. In preliminary work, a logistic regression model was developed using data from two regions (Kobe, Japan and Christchurch, New Zealand) which predicts probabilities of liquefaction based on peak ground acceleration, elevation, distance to coast, and a hydrologic parameter - compound topographic index - which is used as a proxy for soil saturation. The model has been tested in Port-au-Prince, Haiti and provides a consistent estimate of liquefaction probability. This demonstration shows that the proposed new method of estimating the probability of liquefaction can be rapidly and broadly derived from both earthquake-specific intensity estimates and simple geospatial features. However, in order to develop a geospatial liquefaction model that will be globally applicable, the database needs to be extended to more geologic and climatic environments so that the model can constrain regional variations in the geospatial proxies. In this project, a geospatial liquefaction database will be developed from global earthquakes, where the explanatory variables will be broadly available geospatial data. Sampling bias will be addressed by developing the database from observations that are representative of the true distribution of liquefaction. This marks a shift in liquefaction potential model development, which to date has focused on case history databases that are biased toward observations of liquefaction occurrence. The goals of the project are to: 1) Develop a global database of liquefaction observations with geospatial variables. 2) Test first-order proxies for saturation and soil density 3) Normalize proxies for different geomorphic and climatic regions 4) Develop a probabilistic geospatial liquefaction model 5) Train undergraduate civil engineers in seismic hazard and loss estimation The broader impact and potentially transformative aspect of the proposed work is the global applicability of the model which will enable liquefaction effects to be included in future rapid response maps, loss estimates, and scenario simulations for any future event anywhere in the world and, therefore improve disaster response and reduce loss. In addition, undergraduate research and outreach within the geographic information systems class at Tufts will be used to introduce civil engineering undergraduate students to the importance of seismic hazard and loss estimation for earthquakes.