Interpolating sparse scattered data using flow information
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- 作者：Gregory J. Streletza ; gjstreletz@ucdavis.edu" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Geoffrey Gebbieb ; ggebbie@whoi.edu" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Oliver Kreylosc ; kreylos@cs.ucdavis.edu" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Bernd Hamanna ; hamann@cs.ucdavis.edu" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Louise H. Kelloggc ; kellogg@ucdavis.edu" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Howard J. Speroc ; hjspero@ucdavis.edu" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Scattered data interpolation ; Flow-based non-Euclidean distance metrics ; Cross-correlation-based parameter optimization ; Oceanographic reconstruction problems
- 刊名：Journal of Computational Science
- 出版年：2016
- 出版时间：September 2016
- 年：2016
- 卷：16
- 期：Complete
- 页码：156-169
- 全文大小：3777 K
文摘
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We have implemented a flow-based scattered data interpolation and approximation method.
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Our flow-based method utilizes non-Euclidean distance measures constructed with respect to a known flow field.
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By enabling the use of flow information during reconstruction, our method produces quality reconstructions even for very sparse data.
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The method is particularly well-suited for the problem of creating reconstructions of ocean properties based upon sparse collections of core samples taken from the ocean floor.
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The method exhibits desirable properties when used to create reconstructions of ocean properties in the vicinity of water mass boundaries.