Articles | Volume 12, issue 10
Research article
30 Sep 2021
Research article |  | 30 Sep 2021

Investigating spatial heterogeneity within fracture networks using hierarchical clustering and graph distance metrics

Rahul Prabhakaran, Giovanni Bertotti, Janos Urai, and David Smeulders

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Subject area: Tectonic plate interactions, magma genesis, and lithosphere deformation at all scales | Editorial team: Structural geology and tectonics, paleoseismology, rock physics, experimental deformation | Discipline: Structural geology
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Cited articles

Andresen, C., Hansen, A., Le Goc, R., Davy, P., and Hope, S.: Topology of fracture networks, AIP Conf. Proc., 1, 7,, 2013. a, b, c, d, e
Bagrow, J. P. and Bollt, E. M.: An information-theoretic, all-scales approach to comparing networks, Applied Network Science, 4, 45,, 2019. a, b, c, d, e
Bagrow, J. P., Bollt, E. M., Skufca, J. D., and ben Avraham, D.: Portraits of complex networks, Europhys. Lett., 81, 68004,, 2008. a
Barthelemy, M.: Morphogenesis of Spatial Networks, Lecture Notes in Morphogenesis, Springer International Publishing, 2018 edn.,, 2018. a, b
Belayneh, M.: Palaeostress orientation inferred from surface morphology of joints on the southern margin of the Bristol Channel Basin, UK, pp. 243–255, 1, Geol. Soc. Sp., 231, 243–255,, 2004. a
Short summary
Rock fractures are organized as networks with spatially varying arrangements. Due to networks' influence on bulk rock behaviour, it is important to quantify network spatial variation. We utilize an approach where fracture networks are treated as spatial graphs. By combining graph similarity measures with clustering techniques, spatial clusters within large-scale fracture networks are identified and organized hierarchically. The method is validated on a dataset with nearly 300 000 fractures.