Stochastic simulation of karst conduit networks

Karst aquifers have very high spatial heterogeneity. Essentially, they comprise a system of pipes (i.e., thenetwork of conduits) superimposed on rock porosity and on a network of stratigraphic surfaces and frac-tures. This heterogeneity strongly influences the hydraulic behavior of the karst and it...

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Autores: Pardo-Igúzquiza, Eulogio, Dowd, Peter A., Chaoshui, Xu, Durán Valsero, Juan José
Tipo de recurso: artículo
Fecha de publicación:2011
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/276808
Acceso en línea:http://hdl.handle.net/10261/276808
https://doi.org/10.1016/j.advwatres.2011.09.014
Access Level:acceso abierto
Palabra clave:conduit geometry
Network topology
diffusion-limited aggregation
inception horizon
rose diagram
Z-histogram
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spelling Stochastic simulation of karst conduit networksPardo-Igúzquiza, EulogioDowd, Peter A.Chaoshui, XuDurán Valsero, Juan Joséconduit geometryNetwork topologydiffusion-limited aggregationinception horizonrose diagramZ-histogramKarst aquifers have very high spatial heterogeneity. Essentially, they comprise a system of pipes (i.e., thenetwork of conduits) superimposed on rock porosity and on a network of stratigraphic surfaces and frac-tures. This heterogeneity strongly influences the hydraulic behavior of the karst and it must be repro-duced in any realistic numerical model of the karst system that is used as input to flow and transportmodeling. However, the directly observed karst conduits are only a small part of the complete karst con-duit system and knowledge of the complete conduit geometry and topology remains spatially limited anduncertain. Thus, there is a special interest in the stochastic simulation of networks of conduits that can becombined with fracture and rock porosity models to provide a realistic numerical model of the karst sys-tem. Furthermore, the simulated model may be of interestper seand other uses could be envisaged. Thepurpose of this paper is to present an efficient method for conditional and non-conditional stochasticsimulation of karst conduit networks. The method comprises two stages: generation of conduit geometryand generation of topology. The approach adopted is a combination of a resampling method for generat-ing conduit geometries from templates and a modified diffusion-limited aggregation method for gener-ating the network topology. The authors show that the 3D karst conduit networks generated by theproposed method are statistically similar to observed karst conduit networks or to a hypothesized net-work model. The statistical similarity is in the sense of reproducing the tortuosity index of conduits,the fractal dimension of the network, the direction rose of directions, the Z-histogram and Ripley’s K-function of the bifurcation points (which differs from a random allocation of those bifurcation points).The proposed method (1) is very flexible, (2) incorporates any experimental data (conditioning informa-tion) and (3) can easily be modified when implemented in a hydraulic inverse modeling procedure. Sev-eral synthetic examples are given to illustrate the methodology and real conduit network data are used togenerate simulated networks that mimic real geometries and topologyInstituto Geológico y Minero de España, EspañaFaculty of Engineering, Computer & Mathematical Sciences, University of Adelaide, AustraliaSchool of Civil, Environmental and Mining Engineering, University of Adelaide, AustraliaElsevierMinisterio de Ciencia e Innovación (España)Australian Research Council202220222011info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://hdl.handle.net/10261/276808https://doi.org/10.1016/j.advwatres.2011.09.014reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#CGL2010-15498DP110104766https://reader.elsevier.com/reader/sd/pii/S0309170811001813?token=49988597C1826CA25948C146D621F1364D0C74B7D8D1B24BF9E4304472E956F33E700A23D1F02285AEAE34A58F3906A2info:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2768082026-05-22T06:33:51Z
dc.title.none.fl_str_mv Stochastic simulation of karst conduit networks
title Stochastic simulation of karst conduit networks
spellingShingle Stochastic simulation of karst conduit networks
Pardo-Igúzquiza, Eulogio
conduit geometry
Network topology
diffusion-limited aggregation
inception horizon
rose diagram
Z-histogram
title_short Stochastic simulation of karst conduit networks
title_full Stochastic simulation of karst conduit networks
title_fullStr Stochastic simulation of karst conduit networks
title_full_unstemmed Stochastic simulation of karst conduit networks
title_sort Stochastic simulation of karst conduit networks
dc.creator.none.fl_str_mv Pardo-Igúzquiza, Eulogio
Dowd, Peter A.
Chaoshui, Xu
Durán Valsero, Juan José
author Pardo-Igúzquiza, Eulogio
author_facet Pardo-Igúzquiza, Eulogio
Dowd, Peter A.
Chaoshui, Xu
Durán Valsero, Juan José
author_role author
author2 Dowd, Peter A.
Chaoshui, Xu
Durán Valsero, Juan José
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Australian Research Council
dc.subject.none.fl_str_mv conduit geometry
Network topology
diffusion-limited aggregation
inception horizon
rose diagram
Z-histogram
topic conduit geometry
Network topology
diffusion-limited aggregation
inception horizon
rose diagram
Z-histogram
description Karst aquifers have very high spatial heterogeneity. Essentially, they comprise a system of pipes (i.e., thenetwork of conduits) superimposed on rock porosity and on a network of stratigraphic surfaces and frac-tures. This heterogeneity strongly influences the hydraulic behavior of the karst and it must be repro-duced in any realistic numerical model of the karst system that is used as input to flow and transportmodeling. However, the directly observed karst conduits are only a small part of the complete karst con-duit system and knowledge of the complete conduit geometry and topology remains spatially limited anduncertain. Thus, there is a special interest in the stochastic simulation of networks of conduits that can becombined with fracture and rock porosity models to provide a realistic numerical model of the karst sys-tem. Furthermore, the simulated model may be of interestper seand other uses could be envisaged. Thepurpose of this paper is to present an efficient method for conditional and non-conditional stochasticsimulation of karst conduit networks. The method comprises two stages: generation of conduit geometryand generation of topology. The approach adopted is a combination of a resampling method for generat-ing conduit geometries from templates and a modified diffusion-limited aggregation method for gener-ating the network topology. The authors show that the 3D karst conduit networks generated by theproposed method are statistically similar to observed karst conduit networks or to a hypothesized net-work model. The statistical similarity is in the sense of reproducing the tortuosity index of conduits,the fractal dimension of the network, the direction rose of directions, the Z-histogram and Ripley’s K-function of the bifurcation points (which differs from a random allocation of those bifurcation points).The proposed method (1) is very flexible, (2) incorporates any experimental data (conditioning informa-tion) and (3) can easily be modified when implemented in a hydraulic inverse modeling procedure. Sev-eral synthetic examples are given to illustrate the methodology and real conduit network data are used togenerate simulated networks that mimic real geometries and topology
publishDate 2011
dc.date.none.fl_str_mv 2011
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/276808
https://doi.org/10.1016/j.advwatres.2011.09.014
url http://hdl.handle.net/10261/276808
https://doi.org/10.1016/j.advwatres.2011.09.014
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
CGL2010-15498
DP110104766
https://reader.elsevier.com/reader/sd/pii/S0309170811001813?token=49988597C1826CA25948C146D621F1364D0C74B7D8D1B24BF9E4304472E956F33E700A23D1F02285AEAE34A58F3906A2
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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