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The use of cyclostratigraphy to reconstruct the timing of deposition of lacustrine deposits requires sophisticated tuning techniques that can accommodate continuous long-term changes in sedimentation rates. However, most tuning methods use stationary filters that are unable to take into account such long-term variations in accumulation rates. To overcome this problem we present herein a new multiband wavelet age modeling (MUBAWA) technique that is particularly suitable for such situations and demonstrate its use on a 293 m composite core from the Chew Bahir basin, southern Ethiopian rift. In contrast to traditional tuning methods, which use a single, defined bandpass filter, the new method uses an adaptive bandpass filter that adapts to changes in continuous spatial frequency evolution paths in a wavelet power spectrum, within which the wavelength varies considerably along the length of the core due to continuous changes in long-term sedimentation rates. We first applied the MUBAWA technique to a synthetic data set before then using it to establish an age model for the approximately 293 m long composite core from the Chew Bahir basin. For this we used the 2nd principal component of color reflectance values from the sediment, which showed distinct cycles with wavelengths of 10–15 and of ∼40 m that were probably a result of the influence of orbital cycles. We used six independent 40Ar/39Ar ages from volcanic ash layers within the core to determine an approximate spatial frequency range for the orbital signal. Our results demonstrate that the new wavelet-based age modeling technique can significantly increase the accuracy of tuned age models.

The ages of critical periods in the geological record of the Earth have to be determined and attached uncertainties quantified as precisely and accurately as possible. This study tries to find an optimal strategy for sampling, for both biochronology and high-precision U–Pb analysis of zircon from ash beds, and for the selection and statistical treatment of geochronological data. This strategy is applied to the case of the Early–Middle Triassic boundary (EMTB) in the southern Nanpanjiang Basin in South China, aiming to obtain the most accurate age information for this boundary with an associated uncertainty. The EMTB has been located in a sedimentary section near Monggan, Guangxi, South China, which permitted tight correlation between conodont biozones, the carbon isotopic record, and high-precision zircon U–Pb data from volcanic ashes. Two residual maximal horizons were defined using characteristic associations of conodont species, accurately bracketing the EMTB. The lower and upper age limits of these horizons, as well as the first occurrence (FO) of the base Middle Triassic index fossil Chiosella timorensis , were numerically calibrated by high-precision chemical abrasion, isotope dilution, thermal ionization mass spectrometry (CA-ID-TIMS) U–Pb dates. Volcanic zircon in the analyzed ash beds reveals complex system behavior, in particular (i) unresolved lead loss after chemical abrasion, and (ii) pre-eruptive growth and/or recycling of zircon crystals, leading to excess scatter in 206 Pb/ 238 U dates and to violations of the stratigraphic sequence. A sufficiently high number of closely spaced ash samples (N) and of U–Pb analyses per sample (n) promotes recognition of such complications. Two different age models were constructed from weighted mean 206 Pb/ 238 U dates, comparing smoothing cubic spline and Bayesian interpolation approaches. The statistical treatment of the combined datasets allows quantification of the lower and upper limits and associated uncertainties of the two conodont biozones as well as of the FO of C. timorensis . The age for the EMTB is calculated from combined interpolated data at 247.05 ± 0.16 Ma. The age model derived from Bayesian interpolation is considered to be more adapted to the present case, where discontinuous changes in sedimentation rates are expected to be important. The smoothing spline represents a more parsimonious model, producing a simpler solution in terms of smoothness, but the model assumptions are too strong for many geological problems. The additional freedom in constructing the Bayesian interpolating curve presumably leads to more realistic, albeit larger confidence intervals.