The discrimination of tidal- and fluvial-dominated sedimentary systems based on quantifying the degree of serration in gamma-ray logging curves: East China Sea Shelf Basin

Using quantitative methods to distinguish tidal- and fluvial-dominated sedimentary systems is currently a challenge in sedimentological research. This paper establishes and verifies a new quantitative method through the degree of serration of gamma-ray logging curves. This method depends on the fundamentals of core-based and gamma-ray logging pattern interpretation of the depositional environment, using one-dimensional continuous wavelet transform to denoise the initial curve to enhance the effects of tides and rivers processes, and then expands to an analysis of the degree of serration in noise-reduced gamma-ray curves. The results may then be used to constrain a single best-boundary value to distinguish a tidal- or fluvial-dominated area, avoiding subjectivity and uncertainty. A case study of the Pinghu Formation showed that the degree of serration (defined here as DeltaGr) of the fluvial-dominated sediments was less than 10 in the depth dimension and that the average degree of serration (defined here as the average square of DeltaGr) was less than 7 in the layer dimension. Values that deviate from these imply a close relation to tide-dominated sedimentary systems, which provides a quantitative basis for the interpretation of sedimentary environments in both the depth and layer dimensions in the Pingbei Slope. Seismic attribute-based sedimentary analysis, relative sea-level changes, and the basin architecture of the Pingbei Slope clarify the reason for these changes and verify that this method is reliable. However, owing to the limitations of logging and core samples, the criteria remain an approximation of the actual situation. This study may, however, enable the discrimination of other similar sedimentary systems dominated by tidal and fluvial processes and therefore provide a further constraint on the reconstruction of the filling of basins.