2017
DOI: 10.1016/j.amc.2017.06.018
|View full text |Cite
|
Sign up to set email alerts
|

Random neighborhood graphs as models of fracture networks on rocks: Structural and dynamical analysis

Abstract: We propose a new model to account for the main structural characteristics of rock fracture networks (RFNs). The model is based on a generalization of the random neighborhood graphs to consider fractures embedded into rectangular spaces. We study a series of 29 real-world RFNs and find the best fit with the random rectangular neighborhood graphs (RRNGs) proposed here. We show that this model captures most of the structural characteristics of the RFNs and allows a distinction between small and more spherical roc… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
6
0

Year Published

2019
2019
2023
2023

Publication Types

Select...
4
1
1

Relationship

3
3

Authors

Journals

citations
Cited by 6 publications
(10 citation statements)
references
References 48 publications
0
6
0
Order By: Relevance
“…It is worth mentioning that for β = 1, Eqs. (2), (5) and (7) reduce to the same expression. Indeed, the case β = 1 is well known in the literature as Gabriel graph [8] and addressed as a 1-skeleton graph.…”
Section: Definitions Of β-Skeleton Graphsmentioning
confidence: 91%
See 1 more Smart Citation
“…It is worth mentioning that for β = 1, Eqs. (2), (5) and (7) reduce to the same expression. Indeed, the case β = 1 is well known in the literature as Gabriel graph [8] and addressed as a 1-skeleton graph.…”
Section: Definitions Of β-Skeleton Graphsmentioning
confidence: 91%
“…In particular, BSGs are useful to study geometric complex systems where the connectivity between two items is interfered by the presence of a third one in between them. This is the case, for instance of granular materials [5], for representing urban street networks [6], as well as for representing fractures in rocks [7], among others.…”
Section: Introductionmentioning
confidence: 99%
“…Since we are interested in quantifying spatial variability, we may recast the problem as that of identifying clusters within the network. Clustering is also referred to as unsupervised classification and is a process of finding groups within a set of objects with an assigned measurement (Everitt et al, 2011). If we consider a dataset, D = [X 1 , X 2 , .…”
Section: Combining Dissimilarity Measures With Clustering Algorithmsmentioning
confidence: 99%
“…Other techniques based on multipoint statistics (Bruna et al, 2019b) attempt imagebased approaches to modelling non-stationary networks. Estrada and Sheerin (2017) presents a different approach in which DFNs are directly generated as spatial graphs (referred to as random rectangular graphs). Such a method can incorporate insights from outcrop-derived NFRs.…”
Section: Regionmentioning
confidence: 99%