Metre‐scale cycles in shallow water carbonate successions: Milankovitch and stochastic origins

Abstract

Metre-scale cycles are a common feature in Precambrian and Phanerozoic shallow water carbonate successions, and astronomically forced changes in sea-level (Milankovitch cycles) may have been an important driver controlling their deposition. Nevertheless, the degree to which potentially low amplitude astronomically paced sea-level oscillations may have controlled carbonate accumulation in deep time is unclear. In this study, a stochastic model of carbonate accumulation demonstrates how metre-scale exposurebound sequences can be generated under conditions of random sea-level change. These sequences have characteristic durations close to Milankovitch cycles, despite the absence of any astronomical control on their formation. Metre-scale sequences with sub-Milankovitch (millennial-scale) durations can also be generated by the model, potentially shedding light on the origin of sub-Milankovitch sequences such as those recorded on the Middle Triassic Latemar platform of Northern Italy. Sensitivity tests demonstrate how shallow water carbonates may be very sensitive to weak (i.e. low amplitude) astronomically forced sea-level oscillations. Notably, strong statistical evidence (P < 0Á01) for astronomical cycles can be preserved in modelled successions even when astronomical forcing contributes <1% of the sea-level variance on million year timescales. Taken together, metre-scale cycles with Milankovitch-scale durations in ancient carbonate successions may reveal very little about the amplitude, or even the existence, of astronomical forcing as a sea-level driver.

Buffered, Incomplete, and Shredded: The Challenges of Reading an Imperfect Stratigraphic Record

“…This is particularly true for unchannelized strata, for example, some deep marine records, where our understanding of the source and magnitude of stochastic surface fluctuations relative to background deposition rates is in its infancy. It also holds for nonclastic depositional settings, for example, carbonate systems, where biogeochemical processes also have a stochastic component that results in stratigraphic hiatuses and autogenic fluctuations in aggradation rates (Kemp & Van Manen, ; Kim et al, ; Purkis et al, ).…”

Assessing Milankovitch forcing in disconformity‐prone cyclic shallow‐water carbonates, Upper Jurassic (Kimmeridgian), Adriatic Platform, Croatia

“…Obliquity also may be overestimated as it is spread across the three obliquity components. Nevertheless, non‐Milankovitch processes (Algeo & Wilkinson, 1988; Wilkinson et al ., 1996; Kemp & Manen, 2019) contribute an average of ca 35% of the power (range 20 to 50%). Extra (non‐Milankovitch) beats are favoured where the sea‐level variance on the platform is <1.5 m (i.e.…”

“…the platform is flooded to very shallow depths each time), but extra beats are relatively insensitive to sedimentation rate (Kemp et al ., 2016). Non‐Milankovitch signal in the record also could be due to small, random sea‐level fluctuations that were not driven by Milankovitch forcing of climate and sea‐level but which form metre‐scale cycles (Kemp & Manen, 2019).…”

Interplay of eustatic, tectonic and autogenic controls on a Late Devonian carbonate platform, northern Canning Basin, Australia

“…Distinguishing between orbital forcing, local tectonic and autogenic responses in carbonate platforms is difficult because the processes all involve repeated, high‐frequency changes in accommodation and operate on overlapping timescales (e.g. Bosence et al., 2009; De Benedictis et al, 2007; Kemp & Van Manen, 2019; Tucker & Garland, 2010; Westphal et al, 2010). Differentiation of these processes using numerical approaches requires careful consideration of the temporal and periodic form of the responses, but can be enhanced by information on spatial scales of the responses.…”

“…Although it is difficult to distinguish unequivocally the signals of each mechanism, quantitative analyses of field and core data and forward stratigraphic modelling provide a basis for analysing the interplay of the various controls (e.g. Burgess, 2006Burgess, , 2016De Benedictis et al, 2007;Hill, Wood, Curtis, & Tetzlaff, 2012;Kemp & Van Manen, 2019;Meyers, 2019).…”