Stochastic Interpolation of Spatial Random Fields by BF/MCF-ISM
Stochastic Interpolation of Spatial Random Fields by BF/MCF-ISM
复制标题
通过 BF/MCF-ISM 进行空间随机场的随机插值
DOI：
10.1061/(asce)0733-9399(2008)134:2(198
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
M. Hoshiya
中科院分区：
文献类型：
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作者：
O. Maruyama;M. Hoshiya
过去，随机场的插值问题成功地处理了高斯场的Kriging方法和一类非高斯平移场的条件模拟技术。近年来，自举滤波器/蒙特卡罗滤波器（BF/MCF）被广泛用于一般非高斯场的插值。然而，虽然BF/MCF是用于内插非高斯场的通用工具，即生成预测状态向量和滤波状态向量两者的一组样本实现的算法，但是由于所需的样本大小，计算成本是昂贵的。为了减少所需的样本量，一个重要的采样函数推导出高斯场的更新理论被应用到普通的BF/MCF。首先利用数值模拟数据证明了空间场的插值，然后针对条件非高斯场的状态估计问题，研究了BF/MCF-ISM结合重要抽样技术（BF/MCF-ISM）在降低方差方面的有效性。
In the past, interpolation of random fields was successfully treated by Kriging methods for Gaussian fields, and by conditional simulation techniques for a class of non-Gaussian translation fields. Recently, bootstrap filter/Monte Carlo filter (BF/MCF) is extensively used for interpolation of general non-Gaussian fields. However, while BF/MCF is a versatile tool to interpolate non-Gaussian fields, that is an algorithm of generating a set of sample realizations of both a predicted state vector and a filtered state vector, the computational cost is expensive due to the required sample size. In order to reduce the required sample size, an importance sampling function derived from the updating theory of Gaussian fields is applied to the ordinary BF/MCF. Interpolation of spatial fields is first demonstrated by using numerically simulated data, and the BF/MCF incorporated with importance sampling technique (BF/MCF-ISM) for the state estimation of conditional non-Gaussian fields is performed with respect to its efficiency in variance reduction.