Articles | Volume 18, issue 4
https://doi.org/10.5194/gmd-18-939-2025
https://doi.org/10.5194/gmd-18-939-2025
Model description paper
 | 
19 Feb 2025
Model description paper |  | 19 Feb 2025

GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds

David Oakley, Christelle Loiselet, Thierry Coowar, Vincent Labbe, and Jean-Paul Callot

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Cited articles

Aghaee, A., Shamsipour, P., Hood, S., and Haugaard, R.: A convolutional neural network for semi-automated lineament detection and vectorisation of remote sensing data using probabilistic clustering: A method and a challenge, Comput. Geosci., 151, 104724, https://doi.org/10.1016/j.cageo.2021.104724, 2021. 
Allmendinger, R. W.: GMDE: Extracting quantitative information from geologic maps, Geosphere, 16, 1495–1507, https://doi.org/10.1130/GES02253.1, 2020. 
An, Y., Guo, J., Ye, Q., Childs, C., Walsh, J., and Dong, R.: Deep convolutional neural network for automatic fault recognition from 3D seismic datasets, Comput. Geosci., 153, 104776, https://doi.org/10.1016/j.cageo.2021.104776, 2021. 
An, Y., Du, H., Ma, S., Niu, Y., Liu, D., Wang, J., Du, Y., Childs, C., Walsh, J., and Dong, R.: Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review, Earth-Sci. Rev., 243, 104509, https://doi.org/10.1016/j.earscirev.2023.104509, 2023. 
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Short summary
In this work, we develop two automated workflows for identifying fold structures on geological maps using machine learning. In one method, we identify map patterns suggestive of folding based on pre-defined rules and apply a clustering algorithm to group those from the same fold together. In the other, we train a convolutional neural network to identify folds based on a set of training examples. We apply both methods to a set of synthetic maps and to real-world maps from two locations in France.
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