A Neural Network for the Detection of Astronomical Periodicities in Geologic Records

Astronomically controlled variations in the climate induce cyclic trends in the sedimentary process if being effectively registered by the depositional process, are easy to detect in shallow marine carbonate rocks. One of the main difficulties to be solved in order to choose among the registered periodicities in the conversion from the spatial (i.e. recurrent variations along the strata sequence) to the temporal domains of the astronomically induced frequencies present in the rock record. We discuss here how this problem can be circumvented by teaching a neural net to recognize armonics in the signal.