Textural entropy groupings from a modern lake‐lagoon system and its ancient analogue

Abstract

Entropy analysis of grain-size distributions from 95 modern lake-lagoon sediment samples results in clusters which can be directly related to depositional setting. Similar entropy groupings of 60 samples obtained from the Pleistocene rock record show that the modern and ancient sedimentary settings are analogous. Additional splitting of the modern dataset results in groupings which reflect bedform-scale and element-scale features in the lake-lagoon system. Given sufficient data, it appears that this methodology could be used to discriminate small-scale sedimentary environments.

Patterns of mixed siliciclastic–carbonate sedimentation adjacent to a large dry‐tropics river on the central Great Barrier Reef shelf, Australia

“…The usefulness of this technique with mixed siliciclastic–bioclastic shelf environments has been demonstrated for both ancient and modern environments (Bradshaw et al . 1994; Woolfe 1995; Woolfe et al . 1998a, b, 2000; Orpin et al .…”

Sedimentary and foraminiferal facies in Exmouth Gulf, in arid tropical northwestern Australia

“…The usefulness and relative success of this technique with mixed terrigenous-bioclastic shelf environments has been demonstrated for both ancient and modern environments (Bradshaw et al 1994;Orpin 1995;Woolfe 1995). The laser-derived frequency distributions were grouped into self-similar clusters, utilising the entire particle-size envelope.…”

Blending single beam RoxAnn and multi-beam swathe QTC hydro-acoustic discrimination techniques for the Stonehaven area, Scotland, UK

“…However, we employed the entropy-based methodology which was proved to be a useful 23 tool when analysing heterogeneous, multi-modal sediments that characterised our study area identifying the optimum number of groups that describe the sample distributions 1 (e.g. Woolfe, 1995;Woolfe et al, 2000;Orpin and Kostylev, 2006;Stewart et al, 2009). 2 3 There were some limitations to our analysis of sediment grain size data.…”

“…18 To more precisely describe the different sediment types present in the study area as a 19 basis for a quantitative comparison with the acoustic classes, we used 'EntropyMax' 20 software (Stewart et al, 2009 and references therein). EntropyMax is essentially a k-21 means clustering algorithm that groups grain-size distribution data into self-similar 22 clusters by testing for all possible groupings of samples, thereby minimising within-23cluster variability whilst maximising between-cluster variability (e.g Woolfe, 1995; . 24 provides two statistics, which provide an aid when determining the number of clusters to size analysis of all the grab samples dataset identified four groups as 8 the optimum number that described the grain size distributions(Fig.…”

“…group 2 and 3 in figure10 2). Standard classification schemes such as the Udden-Wentworth scale (Udden, 1914; 11 Wentworth, 1922) use median grain size (measured in phi, Φ=-log 2 (d), where 'd' 12represents the grain diameter), sorting level, skewness and kurtosis to describe the 13 difference or similarity between the sediment samples(Woolfe, 1995). However, this 14 scheme does not readily accommodate mixed sediments with multi-modal grain size 15 distributions.…”