Anomaly Detection in Paleoclimate Records Using Permutation Entropy
Permutation entropy techniques can be useful for identifying anomalies in paleoclimate data records, including noise, outliers, and post-processing issues. We demonstrate this using weighted and unweighted permutation entropy with water-isotope records containing data from a deep polar ice core. In...
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ftmdpi:oai:mdpi.com:/1099-4300/20/12/931/ 2023-08-20T04:07:11+02:00 Anomaly Detection in Paleoclimate Records Using Permutation Entropy Joshua Garland Tyler R. Jones Michael Neuder Valerie Morris James W. C. White Elizabeth Bradley 2018-12-05 application/pdf https://doi.org/10.3390/e20120931 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/e20120931 https://creativecommons.org/licenses/by/4.0/ Entropy; Volume 20; Issue 12; Pages: 931 paleoclimate permutation entropy ice core anomaly detection Text 2018 ftmdpi https://doi.org/10.3390/e20120931 2023-07-31T21:53:17Z Permutation entropy techniques can be useful for identifying anomalies in paleoclimate data records, including noise, outliers, and post-processing issues. We demonstrate this using weighted and unweighted permutation entropy with water-isotope records containing data from a deep polar ice core. In one region of these isotope records, our previous calculations (See Garland et al. 2018) revealed an abrupt change in the complexity of the traces: specifically, in the amount of new information that appeared at every time step. We conjectured that this effect was due to noise introduced by an older laboratory instrument. In this paper, we validate that conjecture by reanalyzing a section of the ice core using a more advanced version of the laboratory instrument. The anomalous noise levels are absent from the permutation entropy traces of the new data. In other sections of the core, we show that permutation entropy techniques can be used to identify anomalies in the data that are not associated with climatic or glaciological processes, but rather effects occurring during field work, laboratory analysis, or data post-processing. These examples make it clear that permutation entropy is a useful forensic tool for identifying sections of data that require targeted reanalysis—and can even be useful for guiding that analysis. Text ice core MDPI Open Access Publishing Entropy 20 12 931 |
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English |
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paleoclimate permutation entropy ice core anomaly detection |
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paleoclimate permutation entropy ice core anomaly detection Joshua Garland Tyler R. Jones Michael Neuder Valerie Morris James W. C. White Elizabeth Bradley Anomaly Detection in Paleoclimate Records Using Permutation Entropy |
topic_facet |
paleoclimate permutation entropy ice core anomaly detection |
description |
Permutation entropy techniques can be useful for identifying anomalies in paleoclimate data records, including noise, outliers, and post-processing issues. We demonstrate this using weighted and unweighted permutation entropy with water-isotope records containing data from a deep polar ice core. In one region of these isotope records, our previous calculations (See Garland et al. 2018) revealed an abrupt change in the complexity of the traces: specifically, in the amount of new information that appeared at every time step. We conjectured that this effect was due to noise introduced by an older laboratory instrument. In this paper, we validate that conjecture by reanalyzing a section of the ice core using a more advanced version of the laboratory instrument. The anomalous noise levels are absent from the permutation entropy traces of the new data. In other sections of the core, we show that permutation entropy techniques can be used to identify anomalies in the data that are not associated with climatic or glaciological processes, but rather effects occurring during field work, laboratory analysis, or data post-processing. These examples make it clear that permutation entropy is a useful forensic tool for identifying sections of data that require targeted reanalysis—and can even be useful for guiding that analysis. |
format |
Text |
author |
Joshua Garland Tyler R. Jones Michael Neuder Valerie Morris James W. C. White Elizabeth Bradley |
author_facet |
Joshua Garland Tyler R. Jones Michael Neuder Valerie Morris James W. C. White Elizabeth Bradley |
author_sort |
Joshua Garland |
title |
Anomaly Detection in Paleoclimate Records Using Permutation Entropy |
title_short |
Anomaly Detection in Paleoclimate Records Using Permutation Entropy |
title_full |
Anomaly Detection in Paleoclimate Records Using Permutation Entropy |
title_fullStr |
Anomaly Detection in Paleoclimate Records Using Permutation Entropy |
title_full_unstemmed |
Anomaly Detection in Paleoclimate Records Using Permutation Entropy |
title_sort |
anomaly detection in paleoclimate records using permutation entropy |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2018 |
url |
https://doi.org/10.3390/e20120931 |
genre |
ice core |
genre_facet |
ice core |
op_source |
Entropy; Volume 20; Issue 12; Pages: 931 |
op_relation |
https://dx.doi.org/10.3390/e20120931 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/e20120931 |
container_title |
Entropy |
container_volume |
20 |
container_issue |
12 |
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931 |
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1774718652818391040 |