Articles | Volume 5, issue 1
https://doi.org/10.5194/gc-5-11-2022
Special issue:
https://doi.org/10.5194/gc-5-11-2022
GC Insights
 | 
06 Jan 2022
GC Insights |  | 06 Jan 2022

GC Insights: Identifying conditions that sculpted bedforms – human insights to building an effective AI (artificial intelligence)

John K. Hillier, Chris Unsworth, Luke De Clerk, and Sergey Savel'ev

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Cited articles

Allen, J. R. L.: Their Relation to Patterns of Water and Sediment Motion, North Holland Publishing Company, Amsterdam, 433 pp., https://doi.org/10.1017/S001675680005946X, 1968. 
Bishop, C. M.: Neural Networks for Pattern Recognition, Oxford University Press, 1st edition, Clarendon Press, Oxford, 502 pp., ISBN 9780-198538646, 1996. 
Bullard, J., White, K., and Livingstone, I.: Morphometric analysis of aeolian bedforms in the Namib Sand Sea using ASTER data, Earth Surf. Proc. Land., 36, 1534–1549, 2011. 
Duran Vinet, O. D., Adreiotti, B., Claudin, P., and Winter, C.: A unified model of ripples and dunes in water and planetary environments, Nat. Geosci., 12, 345–350, https://doi.org/10.1038/s41561-019-0336-4, 2019. 
Edgett, K. S. and Lancaster, N.: Volcaniclastic aeolian dunes: terrestrial examples and application to martian sands, J. Arid Environ., 25, 271–297, https://doi.org/10.1006/jare.1993.1061, 1993. 
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Short summary
It is an aspiration to infer flow conditions from bedform morphology (e.g. riverbed ripples) where sedimentary structures preserve the geological past or in inaccessible environments (e.g. Mars). This study was motivated by the idea of better designing an AI (artificial intelligence) algorithm to do this by using lessons from non-AI (i.e. human) abilities, investigated using a geoscience communication activity. A survey and an artificial neural network are used in a successful proof of concept.
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