Preprints
https://doi.org/10.5194/egusphere-2025-6145
https://doi.org/10.5194/egusphere-2025-6145
23 Feb 2026
 | 23 Feb 2026
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

AstroComb(v.1.0): Non-linear, Multi-channel, Probabilistic Cyclostratigraphic Analysis

Iris Fernandes, Klaus Mosegaard, Aske L. Sørensen, Mohammad Youssof, Nicolas Thibault, and Tais W. Dahl

Abstract. We present a new algorithm for constructing floating astronomical timescales with explicit uncertainty estimates from sedimentary sequences. The method integrates probabilistic spectral analysis with inverse geochronological modeling, applied to ultra-high-resolution, multiproxy datasets such as core scanning X-Ray Fluorescence (XRF) elemental records. Our framework does not smooth data or impose layer-to-layer dependency, allowing sedimentation rates to vary abruptly at short stratigraphic length scales. By detecting and statistically constraining Milankovitch cycles preserved in stratigraphic signals, the algorithm seeks a floating age-depth model that can be anchored to astronomical tie points, where available. The resulting timescales enable precise, uncertainty-bounded timing of biostratigraphic zones, geochemical events, and depositional cycles. This approach advances astrochronology by combining cycle detection with formal stratigraphic modelling, while preserving fine-scale depositional variability, offering a reproducible and statistically rigorous framework for dating deep-time records.

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Iris Fernandes, Klaus Mosegaard, Aske L. Sørensen, Mohammad Youssof, Nicolas Thibault, and Tais W. Dahl

Status: open (until 20 Apr 2026)

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Iris Fernandes, Klaus Mosegaard, Aske L. Sørensen, Mohammad Youssof, Nicolas Thibault, and Tais W. Dahl
Iris Fernandes, Klaus Mosegaard, Aske L. Sørensen, Mohammad Youssof, Nicolas Thibault, and Tais W. Dahl
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Short summary
We introduce a new method to more precisely date ancient sediment layers using astronomical time cycles recorded in the rock. Unlike previous tools, it handles complex, multi-channel data without oversimplifying and gives clear estimates of dating uncertainty. This helps future research in better understand Earth’s past climate, biological changes, and geologic events with greater confidence and accuracy.
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