The Incomplete Record of Autogenic Processes Sets Limits on Signal Detectability

Abstract

Spectral analysis is a central tool regularly used by the scientific community to identify the presence of periodic processes within a time series of information, as spectral peaks at an imposed periodicity can be differentiated from internal (autogenic) variance. In scientific disciplines, such as seismology, the time series of information is of high temporal resolution. Hence, although temporal gaps are present, they do not impact the overall noise structure, meaning that the full spectrum of autogenic variance can be reconstructed. However, power spectra generated from stratigraphic information are affected by temporal incompleteness due to varying episodes of erosion and geomorphic stasis, which generate gaps over a range of scales. This removes information related to the natural and autogenic variability present within sediment‐transport systems, which makes it challenging to accurately reconstruct the structure and strength of paleo‐surface processes, which defines the detectability of past environmental signals. We explore how incompleteness impacts the temporal structure of autogenic noise within power spectra, and how this influences the detectability of spectral spikes related to environmental signals. We utilize a sediment flux time series from a physical rice pile and progressively degrade these data to mimic varying degrees of stratigraphic incompleteness. We find that incompleteness strongly influences the timescales and spectral structure of autogenic noise evident, and can render signals over all periodicities undetectable within a highly incomplete time series. This offers the ability to confidently justify the interpretation of subtle environmental signals from field measurements and understand the records that may best preserve paleoenvironmental variability.