Is One Sample Enough? Testing the Importance of Lateral Sedimentary Variability in Cyclostratigraphy
Abstract Cyclostratigraphic studies of sedimentary rocks traditionally sample assuming that one sample per sedimentary horizon is sufficient. But is one sample enough? This is important to address because if two or more measurements per horizon improve data quality, then sampling schemes should stri...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-07-01
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| Series: | Geochemistry, Geophysics, Geosystems |
| Online Access: | https://doi.org/10.1029/2024GC012087 |
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| Summary: | Abstract Cyclostratigraphic studies of sedimentary rocks traditionally sample assuming that one sample per sedimentary horizon is sufficient. But is one sample enough? This is important to address because if two or more measurements per horizon improve data quality, then sampling schemes should strike a balance between sampling resolution (stratigraphically) and bedding variation (laterally). This study aims to address this fundamental question by statistically comparing the results from data sets based on individual versus multiple measurements per stratigraphic horizon. Using magnetic susceptibility as our proxy, which can be readily measured in situ for such a study, we evaluate both field‐based (KT‐10R) and laboratory‐based (MFK2‐FA Kappabridge) susceptibility data and compare their results. For the Guanmenshan Formation, a Paleoproterozoic (ca. 2.16 Ga) platform carbonate of North China craton, we find broad agreement between the two means of measurement. But the KT‐10R field meter, with multiple measurements per bed, shows increased statistical significance in identifying Milankovitch cycles. This dual comparison between lab‐ and field‐based methods and single versus multiple measurements per bed demonstrates that measuring one sample per bed reduces accuracy in determining the true average proxy value of a bed. Thus, averaging the natural variation in composition along a stratigraphic layer (spatial resolution)—typically ignored when only one sample is taken—may be as important as the precision of measurement or the sampling interval. Our results suggest that n = 2 samples/measurements per bed are better than just n = 1, and results are best for n = 3 per layer. |
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| ISSN: | 1525-2027 |