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Quantitative Analysis of Paleomagnetic Sampling Strategies
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  • Facundo Sapienza,
  • Leandro Cesar Gallo,
  • Yiming Zhang,
  • Bram Vaes,
  • Mathew Domeier,
  • Nicholas L Swanson-Hysell
Facundo Sapienza
University of California, Berkeley

Corresponding Author:[email protected]

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Leandro Cesar Gallo
The Center for Earth Evolution and Dynamics
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Yiming Zhang
University of California, Berkeley
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Bram Vaes
Utrecht University
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Mathew Domeier
University of Oslo
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Nicholas L Swanson-Hysell
University of California, Berkeley
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Abstract

Sampling strategies used in paleomagnetic studies play a crucial role in dictating the accuracy of our estimates of properties of the ancient geomagnetic field. However, there has been little quantitative analysis of optimal paleomagnetic sampling strategies and the community has instead defaulted to traditional practices that vary between laboratories. In this paper, we quantitatively evaluate the accuracy of alternative paleomagnetic sampling strategies through numerical experiment and an associated analytical framework. Our findings demonstrate a strong correspondence between the accuracy of an estimated paleopole position and the number of sites or independent readings of the time-varying paleomagnetic field, whereas larger numbers of in-site samples have a dwindling effect. This remains true even when a large proportion of the sample directions are spurious. This approach can be readily achieved in sedimentary sequences by distributing samples stratigraphically, considering each sample as an individual reading. However, where the number of potential independent sites is inherently limited the collection of additional in-site samples can improve the accuracy of the paleopole estimate (although with diminishing returns with increasing samples per site). Where an estimate of the magnitude of paleosecular variation is sought, multiple in-site samples should be taken, but the optimal number is dependent on the expected fraction of outliers. We provide both analytical formulas and a series of interactive Jupyter notebooks allowing optimal sampling strategies to be derived from user-informed expectations.
03 Jul 2023Submitted to ESS Open Archive
08 Jul 2023Published in ESS Open Archive