Spatial Markov Model of Anomalous Transport Through Random Lattice Networks

Kang, Peter K.; Dentz, Marco; Le Borgne, Tanguy; Juanes, Ruben

doi:10.1103/PhysRevLett.107.180602

Title: Spatial Markov Model of Anomalous Transport Through Random Lattice Networks

Full Record
Other Related Research

Authors:: Kang, Peter K.; Dentz, Marco; Le Borgne, Tanguy; Juanes, Ruben

Publication Date:: Thu Oct 27 00:00:00 EDT 2011

Sponsoring Org.:: USDOE

OSTI Identifier:: 1101049

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: Physical Review Letters

Additional Journal Information:: Journal Volume: 107; Journal Issue: 18; Journal ID: ISSN 0031-9007

Publisher:: American Physical Society

Country of Publication:: United States

Language:: English

Citation Formats


                    Kang, Peter K., Dentz, Marco, Le Borgne, Tanguy, and Juanes, Ruben. Spatial Markov Model of Anomalous Transport Through Random Lattice Networks.  United States: N. p., 2011. 
Web.  doi:10.1103/PhysRevLett.107.180602.

Copy to clipboard


                    Kang, Peter K., Dentz, Marco, Le Borgne, Tanguy, & Juanes, Ruben. Spatial Markov Model of Anomalous Transport Through Random Lattice Networks.  United States.  https://doi.org/10.1103/PhysRevLett.107.180602

Copy to clipboard


                    Kang, Peter K., Dentz, Marco, Le Borgne, Tanguy, and Juanes, Ruben. Thu .  
"Spatial Markov Model of Anomalous Transport Through Random Lattice Networks".  United States.  https://doi.org/10.1103/PhysRevLett.107.180602.

Copy to clipboard


                    
@article{osti_1101049,

  title        = {Spatial Markov Model of Anomalous Transport Through Random Lattice Networks},

  author       = {Kang, Peter K. and Dentz, Marco and Le Borgne, Tanguy and Juanes, Ruben},

  abstractNote = {},

  doi          = {10.1103/PhysRevLett.107.180602},

  journal      = {Physical Review Letters},

  number       = 18,

  volume       = 107,

  place        = {United States},

  year         = {Thu Oct 27 00:00:00 EDT 2011},

  month        = {Thu Oct 27 00:00:00 EDT 2011}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (Publisher)

Publisher's Version of Record
https://doi.org/10.1103/PhysRevLett.107.180602

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 79 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Anomalous transport in disordered fracture networks: Spatial Markov model for dispersion with variable injection modes

Journal Article Kang, Peter K. ; Dentz, Marco ; Le Borgne, Tanguy ; ... - Advances in Water Resources

Here we investigate tracer transport on random discrete fracture networks that are characterized by the statistics of the fracture geometry and hydraulic conductivity. While it is well known that tracer transport through fractured media can be anomalous and particle injection modes can have major impact on dispersion, the incorporation of injection modes into effective transport modeling has remained an open issue. The fundamental reason behind this challenge is that—even if the Eulerian fluid velocity is steady—the Lagrangian velocity distribution experienced by tracer particles evolves with time from its initial distribution, which is dictated by the injection mode, to a stationarymore »« less
Cited by 54
https://doi.org/10.1016/j.advwatres.2017.03.024

Full Text Available
A review of spatial Markov models for predicting pre-asymptotic and anomalous transport in porous and fractured media

Journal Article Sherman, Thomas ; Engdahl, Nicholas B. ; Porta, Giovanni ; ... - Journal of Contaminant Hydrology

Heterogeneity across a broad range of scales in geologic porous media often manifests in observations of non-Fickian or anomalous transport. While traditional anomalous transport models can successfully make predictions in certain geological systems, increasing evidence suggests that assumptions relating to independent and identically distributed increments constrain where and when they can be reliably applied. A relatively novel model, the Spatial Markov model (SMM), relaxes the assumption of independence. The SMM belongs to the family of correlated continuous time random walks and has shown promise across a wide range of transport problems relevant to natural porous media. It has been successfullymore »« less
https://doi.org/10.1016/j.jconhyd.2020.103734

Full Text Available
Enhancing gene regulatory network inference through data integration with markov random fields

Journal Article Banf, Michael ; Rhee, Seung Y. - Scientific Reports

Here, a gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization schememore »« less
Cited by 29
https://doi.org/10.1038/srep41174

Full Text Available
A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

Journal Article Nosedal-Sanchez, Alvaro ; Jackson, Charles S. ; Huerta, Gabriel - Geoscientific Model Development (Online)

Abstract. A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates ofmore »« less
Cited by 4
https://doi.org/10.5194/gmd-9-2407-2016
Markovian transport processes in a heterogeneous, variably saturated watershed: A multi-domain spatial Markov model

Journal Article Engdahl, Nicholas B. ; Bolster, Diogo - Advances in Water Resources

Transport processes in watersheds remain challenging to describe accurately with upscaled models because of their intricate complexities at multiple scales that can lead to multi-modal, non-Fickian breakthrough curves. One of the recent advances in solute transport modeling has been the Lagrangian spatial Markov model (SMM), which describes transport using velocity increments and models the correlated transitions between them. The method has been applied previously to saturated porous media and fractured media, but these successful applications suggest it may be useful for describing watershed scale processes as well. An existing, 3-D, heterogeneous variably saturated model of a headwaters catchment and classicalmore »« less
Cited by 7
https://doi.org/10.1016/j.advwatres.2020.103555

Full Text Available

Similar Records