In sedimentology, stratigraphic sequences and cycles are ordered by time spans and physical scales such as thickness, and bounded by discontinuities including unconformities or flooding surfaces.
Song Man Hyok, a section head at the Faculty of Earth Science and Technology, has proposed a spectral analysis method based on wavelet transform (WT) maxima and used it as a quantitative tool to identify multiorder stratigraphic boundaries and cycles in well log data.
The proposed spectral analysis is based on quantitative analysis of the center frequencies and resolutions of Gaussian wavelets in time and frequency, and it uses a combination of the WT maxima based on both the first order Gaussian wavelet with a high time resolution and the seventh order Gaussian wavelet with a high frequency resolution.
WT maxima spectra, which can characterize the evolution of WT maxima across scales and periods along WT maxima lines concerned with sequence boundaries, are used to detect dominant spectral peaks corresponding to the time-period domain WT maxima and to determine WT maxima spectral slopes.
The WT maxima spectral slopes are helpful for discriminating sequence boundaries from intrasequence cyclic variations in well log data, and the time-period domain WT maxima are used to relate the detected boundaries to relevant cycles.
He has introduced interval WT maxima spectra and stationary indexes related to the WT maxima spectra as indicators that could be used for hierarchical ordering of the boundaries and cycles.
Application of the proposed method to well log data shows that this method is efficient in identifying multi-order sequences that relate well to the Milankovitch cycles.
The detailed information about this is found in his paper “Spectral Analysis Based on Wavelet Transform Maxima: Identification of Multi-order Stratigraphic Boundaries and Cycles” presented to the SCI Journal “Mathematical Geosciences”.
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