Functional outlier detection in grain-size distribution curves of detrital sediments

This article introduces functional outlier detection as a mathematical tool for the recognition of outliers in grain-size distribution curves. Two methods, namely the functional high density region (HDR) boxplot and functional bagplot, were applied for outlier detection in detrital sediment grain-size curves. The results of these two approaches were compared with those obtained with a classical modified z-score method. In this regard, while the HDR and functional bagplots revealed a significant number of curves as outliers, the former showed superior sensitivity. Despite the visual appreciation of differences between the curves produced by the classical method, this technique was not able to detect outliers on the basis of just one characteristic parameter of the curves (the median in our case). None of the sedimentary structures (eolian and tidal) addressed was detected as outliers by the algorithms, thus these structures were incorporated into natural variability. The results suggest that the HDR bagplot and the functional bagplot could be introduced as a preceding outlier detection step in geochemical, sedimentological and coastal studies.