Natural Resources Research

, Volume 28, Issue 1, pp 145–152 | Cite as

Using Mahalanobis Distance to Detect and Remove Outliers in Experimental Covariograms

  • David Alvarenga DrumondEmail author
  • Roberto Mentzingen Rolo
  • João Felipe Coimbra Leite Costa
Original Paper


Experimental variograms are crucial for most geostatistical studies. In kriging, for example, the variography has a direct influence on the interpolation weights. Despite the great importance of variogram estimators in predicting geostatistical features, they are commonly influenced by outliers in the dataset. The effect of some randomly spatially distributed outliers can mask the pattern of the experimental variogram and produce a destructuration effect, implying that the true data spatial continuity cannot be reproduced. In this paper, an algorithm to detect and remove the effect of outliers in experimental variograms using the Mahalanobis distance is proposed. An example of the algorithm’s application is presented, showing that the developed technique is able to satisfactorily detect and remove outliers from a variogram.


Mahalanobis distance Outliers Variogram 


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Copyright information

© International Association for Mathematical Geosciences 2018

Authors and Affiliations

  • David Alvarenga Drumond
    • 1
    Email author
  • Roberto Mentzingen Rolo
    • 1
  • João Felipe Coimbra Leite Costa
    • 1
  1. 1.Universidade Federal do Rio Grande do Sul (UFRGS)Porto AlegreBrazil

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