Geometric attribute and shape characterization of modern depositional elements: a quantitative GIS method for empirical analysis
Date
2015
Authors
Nyberg, B.
Buckley, S.
Howell, J.
Nanson, R.
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Computers and Geosciences, 2015; 82:191-204
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Björn Nyberg, Simon J. Buckley, John A. Howell, Rachel A. Nanson
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Abstract
The recent rise in the quality and availability of remote sensing data has benefitted the geoscience community by allowing high resolution studies of the geometry of modern clastic depositional elements, which are analogous to the elements that control fluid flow in subsurface reservoirs. Established methods used to describe the geometry of these features have been predominantly subjective. We present a new objective technique to automate the characterization of centerline attributes (CA) of mapped depositional elements. This technique measures key parameters at a defined sampling interval along a calculated centerline for each input shape, which are automatically analyzed to define geometric shape, length, width, sinuosity, adjacency and centerline deviation. To demonstrate the applicability of the method to a range of depositional environments, mapped sandbodies from two contrasting modern systems were analyzed: (1) the 520 km2 mixed-process Mitchell River Delta, Gulf of Carpentaria, Australia; (2) a 1200 km reach of the anabranching Congo River, Democratic Republic of the Congo.
1696 Wave- and fluvial-derived elements from the Mitchell Delta were analyzed using our CA method and the conventional minimum bounding box (MBB) approach. The MBB results defined the regression slopes as 1.25–4.47 times wider and 0.31–0.97 times shorter than their CA values. Results applied to 2221 mid-channel bar elements in the Congo River showed similar CA and MBB relationships, with linear regression slopes of a MBB as 1.06 times wider and 0.97 times shorter. The inconsistency in the comparative MBB and CA results for these two datasets is attributed to the very different geometries of the sandbodies in these contrasting depositional environments. This suggests that caution should be exercised when applying current methods. A major benefit of the proposed CA method is that it allows quantitative study at scales and levels of detail typically not practical using manual solutions.
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© 2015 Elsevier Ltd. All rights reserved.