Autogenic signals in sedimentary organic carbon records

Abstract:

Chemical reactions near the sediment-water interface (diagenesis) help determine the composition and properties of sediment deposits. Prior work has shown that sedimentation rate (SR) is a key parameter in diagenesis as it dictates the amount of time that particulates react with solutes supplied from the overlying water. For instance, particulate organic carbon (OC) degrades when exposed to oxidants (e.g. O₂, NO₃^-, SO₄^2-) available in porewater, so the total amount of OC that is buried (burial efficiency) is determined by the oxidant exposure time (OET) of OC. Many diagenetic models treat sedimentation as a purely deterministic process, but this assumption may be problematic as autogenic and/or stochastic behaviors, like the Sadler effect, are observed across systems. Autogenic influences on diagenesis may create geochemical signals that are a meaningful component of the variability observed in stratigraphy, but such effects remain poorly constrained.

Here, we couple 1-D models of reactive-transport and stochastic sedimentation to simulate stratigraphic variations in the concentration and isotopic composition of OC. Our results imply that stochastic sedimentation creates variability in OC records and that the magnitude of this variability depends on the SR distributionand the relative timescales of reaction and transport. When the average SR and initial OC content are held constant, the shape of SR distribution modulates the burial efficiency by altering the OET distribution. We find that systems with periods of erosion have higher average burial efficiencies than systems where temporal gaps are solely due to hiatuses. Our model produces white noise in OC records that transitions to red noise at a higher frequency that depends on a characteristic diagenetic length-scale. So, similar patterns in real data may reflect coupling between autogenic dynamics and diagenesis and be used as a novel paleoenvironmental proxy. Our model does not capture all of the features in real data, which reflect a mix of unknown external forcing and poorly constrained internal dynamics. Nevertheless, our approach can be used to generate a mechanistic null hypothesis for analyzing sedimentary records.