Adapting conditional simulation using circulant embedding for irregularly spaced spatial data
Adapting conditional simulation using circulant embedding for irregularly spaced spatial data
复制标题
使用循环嵌入对不规则间隔的空间数据进行条件模拟
作者：
Bailey, Maggie D.;Bandyopadhyay, Soutir;Nychka, Douglas
使用条件模拟计算随机场的系综是一种理想的方法，用于检索以可用数据为条件的场的准确估计，并用于量化这些实现的不确定性。然而，用于生成随机实现的方法在计算上要求很高，特别是当估计以大量观测数据为条件并且针对大的域时。在这篇文章中，一种新的，近似条件模拟的方法是建立在循环嵌入（CE），一种快速的方法来模拟平稳高斯过程。标准CE仅限于在规则间隔的网格上模拟平稳高斯过程（可能是各向异性的）。在这项工作中，我们探讨了两种可能的算法，即局部克里格和近似网格嵌入，扩展CE不规则间隔的数据点。我们建立这些方法的准确性，以适合实际的推理和加速计算允许生成接近一个交互式的时间框架的条件字段。这些方法是由美国地质调查局的软件ShakeMap激发的，该软件提供了重大地震发生后震动强度的近真实的时间图。2019年里奇克雷斯特，加州的事件的例子，用来说明我们的方法。
Computing an ensemble of random fields using conditional simulation is an ideal method for retrieving accurate estimates of a field conditioned on available data and for quantifying the uncertainty of these realizations. Methods for generating random realizations, however, are computationally demanding, especially when the estimates are conditioned on numerous observed data and for large domains. In this article, anew,approximateconditional simulation approach is applied that builds oncirculant embedding(CE), a fast method for simulating stationary Gaussian processes. The standard CE is restricted to simulating stationary Gaussian processes (possibly anisotropic) on regularly spaced grids. In this work, we explore two possible algorithms, namely, local Kriging and approximate grid embedding, that extend CE for irregularly spaced data points. We establish the accuracy of these methods to be suitable for practical inference and the speedup in computation allows for generating conditional fields close to an interactive time frame. The methods are motivated by the U.S. Geological Survey's softwareShakeMap, which provides near real‐time maps of shaking intensity after the occurrence of a significant earthquake. An example for the 2019 event in Ridgecrest, California, is used to illustrate our method.
计算 ShakeMap 地面运动强度的空间相关性
DOI：
10.1016/j.cageo.2016.11.004
发表时间：
2017
期刊：
Comput. Geosci.
影响因子：
--
作者：
Sarah A. Verros;D. Wald;C. Worden;M. Hearne;M. Ganesh
通讯作者：
M. Ganesh
DOI：
10.1080/10618600.2018.1537924
发表时间：
2019-03-21
影响因子：
2.4
作者：
Finley, Andrew O.;Datta, Abhirup;Banerjee, Sudipto
通讯作者：
Banerjee, Sudipto