Semi-automated counting of complex varves through image autocorrelation

Maximillian Van Wyk de Vries; Emi Ito; Mark Shapley; Guido Brignone

doi:10.1017/qua.2021.10

Semi-automated counting of complex varves through image autocorrelation

Maximillian Van Wyk de Vries, Emi Ito, Mark Shapley, Guido Brignone

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

Annual resolution sediment layers, known as varves, can provide continuous and high-resolution chronologies of sedimentary sequences. In addition, varve counting is not burdened with the high laboratory costs of geochronological analyses. Despite a more than 100-year history of use, many existing varve counting techniques are time consuming and difficult to reproduce. We present countMYvarves, a varve counting toolbox which uses sliding-window autocorrelation to count the number of repeated patterns in core scans or outcrop photos. The toolbox is used to build an annually-resolved record of sedimentation rates, which are depth-integrated to provide ages. We validate the model with repeated manual counts of a high sedimentation rate lake with biogenic varves (Herd Lake, USA) and a low sedimentation rate glacial lake (Lago Argentino, Argentina). In both cases, countMYvarves is consistent with manual counts and provides additional sedimentation rate data. The toolbox performs multiple simultaneous varve counts, enabling uncertainty to be quantified and propagated into the resulting age-depth model. The toolbox also includes modules to automatically exclude non-varved portions of sediment and interpolate over missing or disrupted sediment. CountMYvarves is open source, runs through a graphical user interface, and is available online for download for use on Windows, macOS or Linux at https://doi.org/10.5281/zenodo.4031811.

Original language	English (US)
Pages (from-to)	89-100
Number of pages	12
Journal	Quaternary Research
Volume	104
DOIs	https://doi.org/10.1017/qua.2021.10
State	Published - Nov 13 2021

Bibliographical note

Funding Information:
This work is supported by the National Science Foundation under Grant No. EAR-1714614, coordinated by Lead PI Maria Beatrice Magnani.

Funding Information:
Maximillian Van Wyk de Vries was supported by a University of Minnesota College of Science and Engineering fellowship. We acknowledge the critical role played by the logistical and design expertise of Ryan O'Grady and Anders Noren of the Continental Scientific Drilling Facility (CSDF) in all planning and field phases of this research. Kristina Brady Shannon, Jessica Heck, Alex Stone and Rob Brown seamlessly coordinated core processing and analytical activities at CSDF. We thank Matias Romero, Anastasia Fedotova, Cristina San Martín, Guillermo Tamburini-Beliveau, Alexander Schmies and Shanti Penprase for their help with core recovery or processing, and all Spanish-speaking team members for their critical contribution of language skills. We particularly thank capitán Alejandro Jaimes for his expert ship handling under challenging conditions, and for sharing his incisive knowledge of Lago Argentino during the coring cruise. Finally, we would like to thank editor Peter Langdon and reviewers Jack Ridge and Arndt Schimmelmann for insightful comments and suggestions.

Publisher Copyright:
Copyright © University of Washington. Published by Cambridge University Press, 2021.

Keywords

Varves
Sedimentation rate
Patagonia
Core chronology
Image analysis
Autocorrelation
countMYvarves
Paleolimnology
Sedimentology
Lakes

Continental Scientific Drilling Facility tags

GCO
HERD

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10.1017/qua.2021.10

https://www.cambridge.org/core/article/semiautomated-counting-of-complex-varves-through-image-autocorrelation/C64207A8550E0991CEDA2FB16E301E93

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title = "Semi-automated counting of complex varves through image autocorrelation",

abstract = "Annual resolution sediment layers, known as varves, can provide continuous and high-resolution chronologies of sedimentary sequences. In addition, varve counting is not burdened with the high laboratory costs of geochronological analyses. Despite a more than 100-year history of use, many existing varve counting techniques are time consuming and difficult to reproduce. We present countMYvarves, a varve counting toolbox which uses sliding-window autocorrelation to count the number of repeated patterns in core scans or outcrop photos. The toolbox is used to build an annually-resolved record of sedimentation rates, which are depth-integrated to provide ages. We validate the model with repeated manual counts of a high sedimentation rate lake with biogenic varves (Herd Lake, USA) and a low sedimentation rate glacial lake (Lago Argentino, Argentina). In both cases, countMYvarves is consistent with manual counts and provides additional sedimentation rate data. The toolbox performs multiple simultaneous varve counts, enabling uncertainty to be quantified and propagated into the resulting age-depth model. The toolbox also includes modules to automatically exclude non-varved portions of sediment and interpolate over missing or disrupted sediment. CountMYvarves is open source, runs through a graphical user interface, and is available online for download for use on Windows, macOS or Linux at https://doi.org/10.5281/zenodo.4031811.",

keywords = "Varves, Sedimentation rate, Patagonia, Core chronology, Image analysis, Autocorrelation, countMYvarves, Paleolimnology, Sedimentology, Lakes",

author = "{Van Wyk de Vries}, Maximillian and Emi Ito and Mark Shapley and Guido Brignone",

note = "Funding Information: This work is supported by the National Science Foundation under Grant No. EAR-1714614, coordinated by Lead PI Maria Beatrice Magnani. Funding Information: Maximillian Van Wyk de Vries was supported by a University of Minnesota College of Science and Engineering fellowship. We acknowledge the critical role played by the logistical and design expertise of Ryan O'Grady and Anders Noren of the Continental Scientific Drilling Facility (CSDF) in all planning and field phases of this research. Kristina Brady Shannon, Jessica Heck, Alex Stone and Rob Brown seamlessly coordinated core processing and analytical activities at CSDF. We thank Matias Romero, Anastasia Fedotova, Cristina San Mart{\'i}n, Guillermo Tamburini-Beliveau, Alexander Schmies and Shanti Penprase for their help with core recovery or processing, and all Spanish-speaking team members for their critical contribution of language skills. We particularly thank capit{\'a}n Alejandro Jaimes for his expert ship handling under challenging conditions, and for sharing his incisive knowledge of Lago Argentino during the coring cruise. Finally, we would like to thank editor Peter Langdon and reviewers Jack Ridge and Arndt Schimmelmann for insightful comments and suggestions. Publisher Copyright: Copyright {\textcopyright} University of Washington. Published by Cambridge University Press, 2021.",

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doi = "10.1017/qua.2021.10",

language = "English (US)",

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AU - Van Wyk de Vries, Maximillian

AU - Ito, Emi

AU - Shapley, Mark

AU - Brignone, Guido

N1 - Funding Information: This work is supported by the National Science Foundation under Grant No. EAR-1714614, coordinated by Lead PI Maria Beatrice Magnani. Funding Information: Maximillian Van Wyk de Vries was supported by a University of Minnesota College of Science and Engineering fellowship. We acknowledge the critical role played by the logistical and design expertise of Ryan O'Grady and Anders Noren of the Continental Scientific Drilling Facility (CSDF) in all planning and field phases of this research. Kristina Brady Shannon, Jessica Heck, Alex Stone and Rob Brown seamlessly coordinated core processing and analytical activities at CSDF. We thank Matias Romero, Anastasia Fedotova, Cristina San Martín, Guillermo Tamburini-Beliveau, Alexander Schmies and Shanti Penprase for their help with core recovery or processing, and all Spanish-speaking team members for their critical contribution of language skills. We particularly thank capitán Alejandro Jaimes for his expert ship handling under challenging conditions, and for sharing his incisive knowledge of Lago Argentino during the coring cruise. Finally, we would like to thank editor Peter Langdon and reviewers Jack Ridge and Arndt Schimmelmann for insightful comments and suggestions. Publisher Copyright: Copyright © University of Washington. Published by Cambridge University Press, 2021.

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N2 - Annual resolution sediment layers, known as varves, can provide continuous and high-resolution chronologies of sedimentary sequences. In addition, varve counting is not burdened with the high laboratory costs of geochronological analyses. Despite a more than 100-year history of use, many existing varve counting techniques are time consuming and difficult to reproduce. We present countMYvarves, a varve counting toolbox which uses sliding-window autocorrelation to count the number of repeated patterns in core scans or outcrop photos. The toolbox is used to build an annually-resolved record of sedimentation rates, which are depth-integrated to provide ages. We validate the model with repeated manual counts of a high sedimentation rate lake with biogenic varves (Herd Lake, USA) and a low sedimentation rate glacial lake (Lago Argentino, Argentina). In both cases, countMYvarves is consistent with manual counts and provides additional sedimentation rate data. The toolbox performs multiple simultaneous varve counts, enabling uncertainty to be quantified and propagated into the resulting age-depth model. The toolbox also includes modules to automatically exclude non-varved portions of sediment and interpolate over missing or disrupted sediment. CountMYvarves is open source, runs through a graphical user interface, and is available online for download for use on Windows, macOS or Linux at https://doi.org/10.5281/zenodo.4031811.

AB - Annual resolution sediment layers, known as varves, can provide continuous and high-resolution chronologies of sedimentary sequences. In addition, varve counting is not burdened with the high laboratory costs of geochronological analyses. Despite a more than 100-year history of use, many existing varve counting techniques are time consuming and difficult to reproduce. We present countMYvarves, a varve counting toolbox which uses sliding-window autocorrelation to count the number of repeated patterns in core scans or outcrop photos. The toolbox is used to build an annually-resolved record of sedimentation rates, which are depth-integrated to provide ages. We validate the model with repeated manual counts of a high sedimentation rate lake with biogenic varves (Herd Lake, USA) and a low sedimentation rate glacial lake (Lago Argentino, Argentina). In both cases, countMYvarves is consistent with manual counts and provides additional sedimentation rate data. The toolbox performs multiple simultaneous varve counts, enabling uncertainty to be quantified and propagated into the resulting age-depth model. The toolbox also includes modules to automatically exclude non-varved portions of sediment and interpolate over missing or disrupted sediment. CountMYvarves is open source, runs through a graphical user interface, and is available online for download for use on Windows, macOS or Linux at https://doi.org/10.5281/zenodo.4031811.

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KW - Image analysis

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KW - Paleolimnology

KW - Sedimentology

KW - Lakes

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ER -

Semi-automated counting of complex varves through image autocorrelation

Abstract

Bibliographical note

Keywords

Continental Scientific Drilling Facility tags

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