共查询到20条相似文献,搜索用时 0 毫秒
1.
2.
Mathematical Geosciences - Recent years have seen a steady growth in the number of papers that apply machine learning methods to problems in the earth sciences. Although they have different... 相似文献
3.
4.
5.
6.
7.
8.
9.
Environmental studies require multivariate data such as chemical concentrations with space-time coordinates. There are two
general conditions related to such data: the existence of correlations among the coregionalized variables and the differences
in numbers of data which occur because of insufficient data caused by measurement error or bad weather conditions. This study
proposes geostatistical techniques for space-time multivariate modeling that take into consideration these correlations and
data absences. These techniques consist of suitable modeling of semivariograms and cross-semivariograms for quantifying correlation
structures among multivariables and of extending standardized ordinary cokriging. The tensor product cubic smoothing surface
method is used for space-time semivariogram modeling. These methods are applied to the chemical component data of the Ariake
Sea, a typical closed sea in southwest Japan. In order to clarify environmental changes in the Ariake Sea, the concentration
data of four nutritive salts (NO2–N, NO3–N, NH4–N, and PO4–P) at 38 stations over 25 years are used as environmental indicators. For each of the kinds of data, there are spaces and
times for which there is no data available. The effectiveness of the modeling of space-time semivariograms and the high estimation
capability of the extended cokriging are demonstrated by cross-validation. Compared with ordinary kriging for a single variable,
multivariate space-time standardized ordinary cokriging can provide a more detailed concentration map of nutritive salts and
while elucidating their temporal changes over sparsely spaced data areas. In the space-time models by ordinary kriging, on
the other hand, smooth trends are obvious. 相似文献
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.