Large-scale natural fracture network patterns: Insights from automated mapping in the Lilstock (Bristol Channel) limestone outcrops)

Prabhakaran, Rahul; Urai, János L.; Bertotti, Giovanni; Weismüller, Christopher; Smeulders, David

doi:10.31223/x5b61z

Cited by 4 publications

(5 citation statements)

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“…Andresen et al (2013) and Vevatne et al (2014) report negative assortativity coefficients for fracture networks that are represented in the dual form. Prabhakaran et al (2021b) found such a correlation between dual graph node degree and length.…”

Section: Introductionmentioning

confidence: 84%

“…To validate the proposed approach based on graph distance metrics and hierarchical clustering, we utilize a 2-D joint fracture dataset from the Lilstock pavement in the Bristol Channel, UK (Prabhakaran et al, 2021b). The dataset consists of fracture joints automatically traced using a technique described in from UAV photogrammetric data published by .…”

Section: Fracture Datasetsmentioning

confidence: 99%

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

et al. 2021

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Abstract. Rock fractures organize as networks, exhibiting natural variation in their spatial arrangements. Therefore, identifying, quantifying, and comparing variations in spatial arrangements within network geometries are of interest when explicit fracture representations or discrete fracture network models are chosen to capture the influence of fractures on bulk rock behaviour. Treating fracture networks as spatial graphs, we introduce a novel approach to quantify spatial variation. The method combines graph similarity measures with hierarchical clustering and is applied to investigate the spatial variation within large-scale 2-D fracture networks digitized from the well-known Lilstock limestone pavements, Bristol Channel, UK. We consider three large, fractured regions, comprising nearly 300 000 fractures spread over 14 200 m2 from the Lilstock pavements. Using a moving-window sampling approach, we first subsample the large networks into subgraphs. Four graph similarity measures – fingerprint distance, D-measure, Network Laplacian spectral descriptor (NetLSD), and portrait divergence – that encapsulate topological relationships and geometry of fracture networks are then used to compute pair-wise subgraph distances serving as input for the statistical hierarchical clustering technique. In the form of hierarchical dendrograms and derived spatial variation maps, the results indicate spatial autocorrelation with localized spatial clusters that gradually vary over distances of tens of metres with visually discernable and quantifiable boundaries. Fractures within the identified clusters exhibit differences in fracture orientations and topology. The comparison of graph similarity-derived clusters with fracture persistence measures indicates an intra-network spatial variation that is not immediately obvious from the ubiquitous fracture intensity and density maps. The proposed method provides a quantitative way to identify spatial variations in fracture networks, guiding stochastic and geostatistical approaches to fracture network modelling.

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Section: Introductionmentioning

confidence: 84%

Section: Fracture Datasetsmentioning

confidence: 99%

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

et al. 2021

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“…We implement the fracture detection algorithm on 80 published outcrop maps [Duffy et al, 2017;Prabhakaran et al, 2021;Bertrand et al, 2015;Thiele et al, 2017;Odling, 1997;Jafari, 2011;Gillespie et al, 1993;Segall and Pollard, 1983b;Holland et al, 2009;Bisdom, 2016;Barton, 1995;Watkins et al, 2015;Healy et al, 2017;Becker et al, 2018;Odling et al, 1999;Wyller, 2019]. These outcrop maps are collected from different parts of the world as shown in Fig.…”

Section: Synthetic Analysis Of Fracture Geometries and Topological St...mentioning

confidence: 99%

Enhancing fracture network characterization: A data-driven, outcrop-based analysis

Zhu

Santoso

et al. 2022

Computers and Geotechnics

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Section: Synthetic Analysis Of Fracture Geometries and Topological Structuresmentioning

confidence: 99%

Enhancing Fracture Network Characterization: A Data-Driven, Outcrop-Based Analysis

Zhu

Santoso

et al. 2021

Preprint

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Large-scale natural fracture network patterns: Insights from automated mapping in the Lilstock (Bristol Channel) limestone outcrops)

Abstract: Large-scale natural fracture network patterns: Insights from automated mapping in the Lilstock (Bristol Channel

Cited by 4 publications

References 42 publications

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

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

Enhancing fracture network characterization: A data-driven, outcrop-based analysis

Enhancing Fracture Network Characterization: A Data-Driven, Outcrop-Based Analysis

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