Estimation of Spatial Distribution Considering Indirect Data Using Gaussian Process Regression

被引：0

作者：

Tsuda, Yuto ^{[1
]}

Tomizawa, Yukihisa ^{[1
]}

Yoshida, Ikumasa ^{[2
]}

Otake, Yu ^{[3
]}

机构：

[1] Tokyo City Univ, Grad Sch Integrat Sci & Engn, Disaster Mitigat Lab, Setagaya Ku, Tokyo, Japan

[2] Tokyo City Univ, Dept Urban & Civil Engn, Disaster Mitigat Lab, Setagaya Ku, Tokyo, Japan

[3] Tohoku Univ, Dept Civil Environm Engn, Adv Infrastruct Syst Lab, Aoba Ku, Sendai, Miyagi, Japan

来源：

GEO-RISK 2023: INNOVATION IN DATA AND ANALYSIS METHODS | 2023年 / 345卷

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Since soil properties are generally measured at limited locations, it is preferable to use all available measured data including indirect data effectively to estimate the spatial distribution in a rational manner. A more accurate estimation can be achieved by considering the indirect measurement data that are highly correlated with the soil property in interest. In this study, we use GPR with multiple random fields. The random and trend components are estimated using the Whittle-Matern autocorrelation function. The accuracy of estimation is compared between the cases with and without consideration of indirect data for the random component. Even if the direct data is not observed around the target point, a detailed spatial distribution is obtained by considering the surrounding indirect data when they have strong correlation.

引用

页码：94 / 101

页数：8

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