Accuracy evaluation of different statistical and geostatistical censored data imputation approaches (Case study: Sari Gunay gold deposit)

Most of the geochemical datasets include missing data with different portions and this may cause a significant problem in geostatistical modeling or multivariate analysis of the data. Therefore, it is common to impute the missing data in most of geochemical studies. In this study, three approaches called half detection (HD), multiple imputation (MI), and the cosimulation based on Markov model 2 (MM2) are used to impute the censored data. According to the fact that the new datasets have to satisfy the original data underlying structure, the Multidimensional Scaling (MDS) approach has been used to explore the validity of different imputation methods. Log-ratio transformation (alr transformation) was performed to open the closed compositional data prior to applying the MDS method. Experiments showed that, based on the MDS approach, the MI and the MM2 could not satisfy the original underlying structure of the dataset as well as the HD approach. This is because these two mentioned approaches have produced values higher than the detection limit of the variables.

Keywords:

censored data , collocated cosimulation Markov model 2 , half detection , imputation , multidimensional scaling , multiple imputation