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Granulometric characterization of paleosols in loess series by automated static image analysis

Author:
Varga, György, Kovács, János, Szalai, Zoltán, Cserháti, Csaba, Újvári, Gábor
Source:
Sedimentary geology 2018 v.370 pp. 1-14
ISSN:
0037-0738
Subject:
dust, image analysis, loess, microscopy, paleosolic soil types, uncertainty
Abstract:
An automated image analysis method is proposed here to study the size and shape of siliciclastic sedimentary particles of paleosols of Central European loess sequences. Several direct and indirect measurement techniques are available for grain size measurements of sedimentary mineral particles. Indirect techniques involve the use of some kind of physical laws, however, all requirements for calculations are in many cases not known. Even so, the direct manual microscopic observation and measurement of large, representative number of grains is time-consuming and sometimes rather subjective. Therefore, automated image analyses techniques provide a new and perspective way to analyse grain size and shape sedimentary particles. Here we test these indirect (laser diffraction) and direct (automated static image analysis) techniques and provide new granulometric (size and shape) data of paleosols. Our results demonstrate that grain size data of the mineral dust samples are strongly dependent on shape parameters of particles, and shape heterogeneity was different between different size classes. Due to the irregular grain shape parameters, uncertainties have arisen also for determination of grain sizes. In this paper we present a possible correction procedure to reduce the differences among the results of the laser diffraction and image analysis methods. By applying new correction factors, results of the two approaches could become closer but the unknown thickness of particles remains a problem to solve.The other presented correction procedure to assess the uncertain 3ʳᵈ dimension of particles by their intensity-size relationships makes us able to reduce further the deviations of the two sizing methods.
Agid:
5933470