CAJ | 학술논문

地球科学中成分数据(compositional data)非常普通,其在进行空间插值时必须满足4个条件:每一位置各组分之和为常数,每一组分为非负,插值结果无偏最优.本文以土壤连续分类模糊隶属度值为例,数据经对数正态变换、非对称对数比转换、对称对数比转换后进行普通克里格插值结果和成分克里格插值(compositional kriging)结果进行比较.结果表明,对原始数据和经对数正态变换后数据进行插值,每一位置预测结果隶属度之和不能满足常数1.经非对称对数比转换后,插值结果虽然满足各个位置组分之和为1,但是预测结果精度较低,且预测结果空间分布连续性不明显.数据经对称对数比转换后插值结果和成分克里格插值结果,都能满足成分数据空间插值的4个条件,但二者各有优势.相比较而言,对称对数比转换方法得到的预测结果更能体现土壤空间连续渐变特征,而成分克里格插值结果能保证隶属度本身是最优无偏估计.
지구과학중성분수거(compositional data)비상보통,기재진행공간삽치시필수만족4개조건:매일위치각조분지화위상수,매일조분위비부,삽치결과무편최우.본문이토양련속분류모호대속도치위례,수거경대수정태변환、비대칭대수비전환、대칭대수비전환후진행보통극리격삽치결과화성분극리격삽치(compositional kriging)결과진행비교.결과표명,대원시수거화경대수정태변환후수거진행삽치,매일위치예측결과대속도지화불능만족상수1.경비대칭대수비전환후,삽치결과수연만족각개위치조분지화위1,단시예측결과정도교저,차예측결과공간분포련속성불명현.수거경대칭대수비전환후삽치결과화성분극리격삽치결과,도능만족성분수거공간삽치적4개조건,단이자각유우세.상비교이언,대칭대수비전환방법득도적예측결과경능체현토양공간련속점변특정,이성분극리격삽치결과능보증대속도본신시최우무편고계.
Compositional data is very common in geoscionces, which must meet four conditions in spatial interpolation, including ensuring positive definiteness and a constant sum of interpolated values at a given position, error minimization and lack of bias. This study took a case of fuzzy membership values of soil continuous classification, applied three methods of data transformation prior to kriging, i.e., logarithm transformation (LN), asymmetry Logratio transformation (ALR) and symmetry Logratio transformation (SLR). The performance of the transformed values by ordinary kriging was compared with the spatial prediction of the untransformed data using ordinary kringing (UT_(ok)), compositional kriging (CK). The results showed that the sum of interpolated values at a given position wasn't equal to constant 1 by UT_(ok) and LN. Obviously, the above predictive result was theoretically unauthentic. Contrarily, membership values of all the spatial predicted sites summed to 1 when the membership values of the known soils were transformed by asymmetry Logratio and symmetry Logratio approaches and compositional kriging. Comparatively, symmetry Logratio transform could lead to a better spatial continuous distribution pattern. Interpolation results by compositional kriging could keep membership values either anbiasad predictions or minimum prediction error variances.