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Areal parameter estimates from multiple datasets
A wide range of methods exist for interpolation between spatially distributed points drawn from a single population. Yet often multiple datasets are available with differing distribution, character and reliability. A simple scheme is introduced to allow the fusion of multiple datasets. Each dataset...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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The Royal Society Publishing
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894539/ https://www.ncbi.nlm.nih.gov/pubmed/31824215 http://dx.doi.org/10.1098/rspa.2019.0352 |
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author | Kennett, B. L. N. |
author_facet | Kennett, B. L. N. |
author_sort | Kennett, B. L. N. |
collection | PubMed |
description | A wide range of methods exist for interpolation between spatially distributed points drawn from a single population. Yet often multiple datasets are available with differing distribution, character and reliability. A simple scheme is introduced to allow the fusion of multiple datasets. Each dataset is assigned an a priori spatial influence zone around each point and a relative weight based on its physical character. The composite result at a specific location is a weighted combination of the spatial terms for all the available data points that make a significant contribution. The combination of multiple datasets is illustrated with the construction of a unified Moho surface in part of southern Australia from results exploiting a variety of different styles of analysis. |
format | Online Article Text |
id | pubmed-6894539 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-68945392019-12-10 Areal parameter estimates from multiple datasets Kennett, B. L. N. Proc Math Phys Eng Sci Research Article A wide range of methods exist for interpolation between spatially distributed points drawn from a single population. Yet often multiple datasets are available with differing distribution, character and reliability. A simple scheme is introduced to allow the fusion of multiple datasets. Each dataset is assigned an a priori spatial influence zone around each point and a relative weight based on its physical character. The composite result at a specific location is a weighted combination of the spatial terms for all the available data points that make a significant contribution. The combination of multiple datasets is illustrated with the construction of a unified Moho surface in part of southern Australia from results exploiting a variety of different styles of analysis. The Royal Society Publishing 2019-11 2019-11-06 /pmc/articles/PMC6894539/ /pubmed/31824215 http://dx.doi.org/10.1098/rspa.2019.0352 Text en © 2019 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Research Article Kennett, B. L. N. Areal parameter estimates from multiple datasets |
title | Areal parameter estimates from multiple datasets |
title_full | Areal parameter estimates from multiple datasets |
title_fullStr | Areal parameter estimates from multiple datasets |
title_full_unstemmed | Areal parameter estimates from multiple datasets |
title_short | Areal parameter estimates from multiple datasets |
title_sort | areal parameter estimates from multiple datasets |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894539/ https://www.ncbi.nlm.nih.gov/pubmed/31824215 http://dx.doi.org/10.1098/rspa.2019.0352 |
work_keys_str_mv | AT kennettbln arealparameterestimatesfrommultipledatasets |