Abstract
Abstract. Sedimentary provenance is a powerful tool for reconstructing convergent margin evolution. However, single mineral approaches, like detrital zircon, have struggled to track sediment input from mafic and metamorphic sources. Detrital rutile complements detrital zircon datasets by offering a path forward in sedimentary provenance reconstructions where metamorphic terranes are potential source regions. However, U–Pb geochronology in rutile can be difficult due to low uranium concentrations and incorporation of common Pb, and multiple workflows are currently in use. Here, we investigate U–Pb and trace element data reduction, processing, and common Pb correction workflows using new detrital rutile U–Pb geochronology and trace element geochemistry results from the Late Cretaceous to Eocene Central Sakarya and Sarıcakaya basins in Anatolia. A significant number of analyses were rejected (54 %) due to signal intensity limitations, namely low U, low Pb, anomalous signal, and inclusions. We identify this as a universal limitation of large-n detrital rutile studies and recommend the systematic reporting of the amount of discarded analysis and the processes for rejection in all studies using detrital rutile U–Pb geochronology. Additionally, we show that (1) the 208Pb and 207Pb common Pb reduction schemes produce similar age distributions and can be used interchangeably, while (2) the Stacey–Kramers distance is a suitable metric for quantifying U–Pb discordance, but a discordance filter is not recommended. (3) Instead, filtering U–Pb data by a power law function based on the corrected date uncertainty is appropriate. (4) The exclusion of low uranium concentration rutile biases date distributions and favors pelitic-derived, higher Zr-in-rutile temperature, and higher U–Pb concordance grains. (5) Paired U–Pb and trace elements can be used to evaluate potential bias in U–Pb data rejection, which reveals that data rejection does not bias the provenance interpretations. Finally, (6) The signature of sediment recycling can be identified through U–Pb dates and Zr-in-rutile temperatures. To better navigate the complexity of detrital rutile datasets and to facilitate the standardization of data reporting approaches, we provide open-access code as Jupyter notebooks for data processing and analysis steps, including common Pb corrections, uncertainty filters, discordance calculations, and trace element analysis.
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