In this paper we present some soft computing methodologies for time-series analysis applied to cyclostratigraphy. An application to some stratigraphic signals to detect Earth orbital (Milankovic’) periodicities which are expected to be recorded in Cretaceous shallow water carbonate sequences outcropping in Southern Apennines (Italy), is described. The results obtained with classical spectral analysis techniques, based on the modified periodogram, are compared to the results of our methods based on neural nets and genetic algorithms. The aim of these cross comparisons is to find the most reliable, fast and accurate methodology to identify orbital periodicities in noisy and segmented stratigraphic signals.

Soft Computing Methodologies for Spectral Analysis in Cyclostratigraphy

CIARAMELLA, Angelo;
2001

Abstract

In this paper we present some soft computing methodologies for time-series analysis applied to cyclostratigraphy. An application to some stratigraphic signals to detect Earth orbital (Milankovic’) periodicities which are expected to be recorded in Cretaceous shallow water carbonate sequences outcropping in Southern Apennines (Italy), is described. The results obtained with classical spectral analysis techniques, based on the modified periodogram, are compared to the results of our methods based on neural nets and genetic algorithms. The aim of these cross comparisons is to find the most reliable, fast and accurate methodology to identify orbital periodicities in noisy and segmented stratigraphic signals.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/28632
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