Semi-automated Correlation of Silurian Well-Logs Across Gotland, Sweden, Using Dynamic Time Warping With Barycenter Averaging

Arts, Michiel; Sopher, Daniel; Calner, Mikael; Erlström, Mikael; Samant, Rohit; Cramer, Bradley D.; Da Silva, Anne-Christine

doi:10.2110/001c.147894

Article (Scientific journals)

Semi-automated Correlation of Silurian Well-Logs Across Gotland, Sweden, Using Dynamic Time Warping With Barycenter Averaging

Arts, Michiel; Sopher, Daniel; Calner, Mikael et al.

2025 • In The Sedimentary Record, 23 (1)

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https://hdl.handle.net/2268/348637

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10.2110/001c.147894

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Keywords :

Barycenter Averaging; Dynamic Time Warping; gamma-ray well logs; Silurian; subsurface correlation; Geology; Earth-Surface Processes; Stratigraphy; Paleontology

Abstract :

[en] Studies of the Silurian sequence on Gotland have significantly advanced our understanding of Silurian climate dynamics, with much of the research focusing on small outcrops and short cores. Gotland has an extensive network of abandoned oil and gas wells. Most of these include gamma-ray well logs, which have not yet been fully utilised for their stratigraphic value. While the Ordovician succession has been successfully correlated using these well logs, the correlation for the Silurian succession has yet to be fully realised. The present study addresses some of the limitations of correlations performed using Dynamical Time Warping (DTW) by combining DTW with Barycenter Averaging (DBA). This enables a semi-automated correlation of the Silurian well-logs over a ~60 km-long transect and the subdivision of gamma-ray log logs into parts that can be linked with their lithostratigraphic surface counterparts. Additionally, we tracked changes in gamma-ray logs associated with these subsurface units, providing new insights into biogeochemical events (e.g., δ13C excursions) previously mapped only at the surface of Gotland. Our results demonstrate the application of a DTW/DBA-based workflow for stratigraphic correlation and highlight the potential to integrate subsurface well-log data to refine Silurian stratigraphy on Gotland.

Disciplines :

Earth sciences & physical geography

Author, co-author :

Arts, Michiel ; Université de Liège - ULiège > Geology

Sopher, Daniel; Iowa City, United States

Calner, Mikael; Lund University, Iowa City, United States

Erlström, Mikael; Lund University, Iowa City, United States

Samant, Rohit; University of Münster Iowa, Iowa, United States

Cramer, Bradley D.; School of Earth, Environment, and Sustainability, University of Iowa, Iowa City, United States

Da Silva, Anne-Christine ; Université de Liège - ULiège > Geology

Language :

English

Title :

Semi-automated Correlation of Silurian Well-Logs Across Gotland, Sweden, Using Dynamic Time Warping With Barycenter Averaging

Publication date :

2025

Journal title :

The Sedimentary Record

eISSN :

1543-8740

Publisher :

SEPM Society for Sedimentary Geology

Volume :

Issue :

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://thesedimentaryrecord.scholasticahq.com/article/147894-semi-automated-correlation-of-silurian-well-logs-across-gotland-sweden-using-dynamic-time-warping-with-barycenter-averaging

Funding text :

The FNRS-PDR T.0051.19 grant financially supported Michiel Arts. Anne-Christine Da Silva acknowledges the \u201CConseil Universitaire de la recherche et la valorisation,\u201D. Rohit Samant has been funded by the Deutsche Forschungsgemeinschaft (VL96/9-1, grant no. 527307807). We want to thank an anonymous reviewer for their comprehensive feedback, which greatly improved this manuscript. Mathias Sinnesael is thanked for his feedback on a preliminary edition of the manuscript. We further acknowledge the Workshop on Machine Learning Approaches for Automating Geological Correlation (November\u2013December 2022), hosted by David De Vleeschouwer and Ewa Burwicz-Galerne at the MARUM Institute, Bremen, where the discussions provided the foundation for the ideas developed in this manuscript.Rohit Samant has been funded by the Deutsche Forschungsgemeinschaft (VL96/9-1, grant no. 527307807). We want to thank an anonymous reviewer for their comprehensive feedback, which greatly improved this manuscript. Mathias Sinnesael is thanked for his feedback on a preliminary edition of the manuscript. We further acknowledge the Workshop on Machine Learning Approaches for Automating Geological Correlation (November\u2013December 2022), hosted by David De Vleeschouwer and Ewa Burwicz-Galerne at the MARUM Institute, Bremen, where the discussions provided the foundation for the ideas developed in this manuscript.

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