File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1007/s00477-019-01718-7
- Scopus: eid_2-s2.0-85070853063
- WOS: WOS:000485973900001
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Direct simulation of two-dimensional isotropic or anisotropic random field from sparse measurement using Bayesian compressive sampling
Title | Direct simulation of two-dimensional isotropic or anisotropic random field from sparse measurement using Bayesian compressive sampling |
---|---|
Authors | |
Keywords | Spatial data Anisotropy Karhunen–Loève expansion Compressive sensing |
Issue Date | 2019 |
Publisher | Springer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/00477/index.htm |
Citation | Stochastic Environmental Research and Risk Assessment, 2019, v. 33, p. 1477-1496 How to Cite? |
Abstract | Random field theory has been increasingly adopted to simulate spatially varying environmental properties and hydrogeological data in recent years. In a two-dimensional (2D) stochastic analysis, variation of the environmental properties or hydrogeological data along different directions can be similar (i.e., isotropic) or quite different (i.e., anisotropic). To model the spatially isotropic or anisotropic variability in a stochastic analysis, conventional random field generators generally require a vast amount of measurement data to identify the random field parameters (e.g., mean, variance, and correlation structure and correlation length in different directions). However, measurement data available in practice are usually sparse and limited. The random field parameters estimated from sparse measurements might be unreliable, and the subsequent random field modeling or stochastic analysis might be misleading. This underscores the significance and challenge of generating 2D isotropic or anisotropic random fields from sparse measurements. This paper develops a novel 2D random field generator, which does not require a parametric form of correlation function or estimation of correlation length and other random field parameters, and directly generates 2D isotropic or anisotropic random field samples from sparse measurements. The proposed generator is highly efficient because simulation of a 2D random field is achieved by generation of a short 1D random vector. The effectiveness and applicability of the proposed generator are illustrated using isotropic and anisotropic numerical examples. |
Persistent Identifier | http://hdl.handle.net/10722/284029 |
ISSN | 2023 Impact Factor: 3.9 2023 SCImago Journal Rankings: 0.879 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hu, Y | - |
dc.contributor.author | Zhao, T | - |
dc.contributor.author | Wang, Y | - |
dc.contributor.author | Choi, C | - |
dc.contributor.author | Ng, CWW | - |
dc.date.accessioned | 2020-07-20T05:55:26Z | - |
dc.date.available | 2020-07-20T05:55:26Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Stochastic Environmental Research and Risk Assessment, 2019, v. 33, p. 1477-1496 | - |
dc.identifier.issn | 1436-3240 | - |
dc.identifier.uri | http://hdl.handle.net/10722/284029 | - |
dc.description.abstract | Random field theory has been increasingly adopted to simulate spatially varying environmental properties and hydrogeological data in recent years. In a two-dimensional (2D) stochastic analysis, variation of the environmental properties or hydrogeological data along different directions can be similar (i.e., isotropic) or quite different (i.e., anisotropic). To model the spatially isotropic or anisotropic variability in a stochastic analysis, conventional random field generators generally require a vast amount of measurement data to identify the random field parameters (e.g., mean, variance, and correlation structure and correlation length in different directions). However, measurement data available in practice are usually sparse and limited. The random field parameters estimated from sparse measurements might be unreliable, and the subsequent random field modeling or stochastic analysis might be misleading. This underscores the significance and challenge of generating 2D isotropic or anisotropic random fields from sparse measurements. This paper develops a novel 2D random field generator, which does not require a parametric form of correlation function or estimation of correlation length and other random field parameters, and directly generates 2D isotropic or anisotropic random field samples from sparse measurements. The proposed generator is highly efficient because simulation of a 2D random field is achieved by generation of a short 1D random vector. The effectiveness and applicability of the proposed generator are illustrated using isotropic and anisotropic numerical examples. | - |
dc.language | eng | - |
dc.publisher | Springer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/00477/index.htm | - |
dc.relation.ispartof | Stochastic Environmental Research and Risk Assessment | - |
dc.rights | This is a post-peer-review, pre-copyedit version of an article published in [insert journal title]. The final authenticated version is available online at: http://dx.doi.org/[insert DOI] | - |
dc.subject | Spatial data | - |
dc.subject | Anisotropy | - |
dc.subject | Karhunen–Loève expansion | - |
dc.subject | Compressive sensing | - |
dc.title | Direct simulation of two-dimensional isotropic or anisotropic random field from sparse measurement using Bayesian compressive sampling | - |
dc.type | Article | - |
dc.identifier.email | Choi, C: cechoi@hku.hk | - |
dc.identifier.authority | Choi, C=rp02576 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s00477-019-01718-7 | - |
dc.identifier.scopus | eid_2-s2.0-85070853063 | - |
dc.identifier.hkuros | 311034 | - |
dc.identifier.volume | 33 | - |
dc.identifier.spage | 1477 | - |
dc.identifier.epage | 1496 | - |
dc.identifier.isi | WOS:000485973900001 | - |
dc.publisher.place | Germany | - |
dc.identifier.issnl | 1436-3240 | - |