A python code for automatic construction of Fischer plots using proxy data

Abstract Fischer plots are widely used in paleoenvironmental research as graphic representations of sea- and lake-level changes through mapping linearly corrected variation of accumulative cycle thickness over cycle number or stratum depth. Some kinds of paleoenvironmental proxy data (especially sub...

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Main Authors: Daming Yang, Yongjian Huang, Zongyang Chen, Qinghua Huang, Yanguang Ren, Chengshan Wang
Format: Article
Language:English
Published: Nature Publishing Group 2021-05-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-021-90017-9
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spelling doaj-b7bb376b08c4448c827a3faa1a7223792021-05-23T11:32:14ZengNature Publishing GroupScientific Reports2045-23222021-05-0111111310.1038/s41598-021-90017-9A python code for automatic construction of Fischer plots using proxy dataDaming Yang0Yongjian Huang1Zongyang Chen2Qinghua Huang3Yanguang Ren4Chengshan Wang5State Key Laboratory of Biogeology and Environmental Geology, China University of GeosciencesState Key Laboratory of Biogeology and Environmental Geology, China University of GeosciencesState Key Laboratory of Biogeology and Environmental Geology, China University of GeosciencesExploration and Development Research Institute of Daqing Oil Field CorporationExploration and Development Research Institute of Daqing Oil Field CorporationState Key Laboratory of Biogeology and Environmental Geology, China University of GeosciencesAbstract Fischer plots are widely used in paleoenvironmental research as graphic representations of sea- and lake-level changes through mapping linearly corrected variation of accumulative cycle thickness over cycle number or stratum depth. Some kinds of paleoenvironmental proxy data (especially subsurface data, such as natural gamma-ray logging data), which preserve continuous cyclic signals and have been largely collected, are potential materials for constructing Fischer Plots. However, it is laborious to count the cycles preserved in these proxy data manually and map Fischer plots with these cycles. In this paper, we introduce an original open-source Python code “PyFISCHERPLOT” for constructing Fischer Plots in batches utilizing paleoenvironmental proxy data series. The principle of constructing Fischer plots based on proxy data, the data processing and usage of the PyFISCHERPLOT code and the application cases of the code are presented. The code is compared with existing methods for constructing Fischer plots.https://doi.org/10.1038/s41598-021-90017-9
collection DOAJ
language English
format Article
sources DOAJ
author Daming Yang
Yongjian Huang
Zongyang Chen
Qinghua Huang
Yanguang Ren
Chengshan Wang
spellingShingle Daming Yang
Yongjian Huang
Zongyang Chen
Qinghua Huang
Yanguang Ren
Chengshan Wang
A python code for automatic construction of Fischer plots using proxy data
Scientific Reports
author_facet Daming Yang
Yongjian Huang
Zongyang Chen
Qinghua Huang
Yanguang Ren
Chengshan Wang
author_sort Daming Yang
title A python code for automatic construction of Fischer plots using proxy data
title_short A python code for automatic construction of Fischer plots using proxy data
title_full A python code for automatic construction of Fischer plots using proxy data
title_fullStr A python code for automatic construction of Fischer plots using proxy data
title_full_unstemmed A python code for automatic construction of Fischer plots using proxy data
title_sort python code for automatic construction of fischer plots using proxy data
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2021-05-01
description Abstract Fischer plots are widely used in paleoenvironmental research as graphic representations of sea- and lake-level changes through mapping linearly corrected variation of accumulative cycle thickness over cycle number or stratum depth. Some kinds of paleoenvironmental proxy data (especially subsurface data, such as natural gamma-ray logging data), which preserve continuous cyclic signals and have been largely collected, are potential materials for constructing Fischer Plots. However, it is laborious to count the cycles preserved in these proxy data manually and map Fischer plots with these cycles. In this paper, we introduce an original open-source Python code “PyFISCHERPLOT” for constructing Fischer Plots in batches utilizing paleoenvironmental proxy data series. The principle of constructing Fischer plots based on proxy data, the data processing and usage of the PyFISCHERPLOT code and the application cases of the code are presented. The code is compared with existing methods for constructing Fischer plots.
url https://doi.org/10.1038/s41598-021-90017-9
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