I am currently writing up my PhD thesis, and I have shown that even with a sampling resolution of 1 sample per cm, the black shale I am studying shows evidence of brief fluctuations between oxic and anoxic states (probably decades to centuries in duration) in the form of in-situ benthic macrofauna.
The issue is that many of the geochemical sampling techniques I used can only resolve proxy records at a 1 cm scale, due to sample weight requirements (e.g. total lipid extraction for biomarker analysis), and multiple redox oscillations become time-averaged in these samples.
Is it possible to use some sort of model based on Bayesian statistics, to estimate the likely true frequency of oxia/anoxia in a given sampling interval (i.e. using the 1cm scale proxy data and the
Bayesian statistics - based algorithm,viterbi,has been used in the mapping of layering sequences using spatial hidden Markov models to help identify the lithologic units expected during deep driling of cores for oil exploration. Similarly,hydrogeological assessment of aquifer material has been undertaken for expected drill logs forecasting ,again using the viterbi algorithm ,which employs the use of bayesian statistics. You can come up with several papers done in the last fiver years if you type the following: "hidden Markov models in geology" or " spatial hidden Markov modeling of hydrogeological and petroleum characteristics "....
Yes, Bayesian statistics can be used to estimate the frequency of brief geological events. Bayesian methods are particularly useful for estimating the frequency of rare events, such as brief geological events, because they allow for the incorporation of prior information and data that might not normally be available. This type of analysis can be used to better understand the likelihood of an event occurring and to develop more accurate predictions.
Yes it is possible to use a bayesian model to estimate the likely time frequency of Oxia /anoxia in a given sampling interval using the Icm scale proxy data and the
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