Spectral decomposition predicts the distribution of steep slope fans in the rift basin of eastern China

Abstract

Deep reservoirs associated with gravity-flows are garnering considerable attention. Predicting reservoirs deposited by nearshore subaqueous fans is challenging and often underreported in seismic sedimentology analysis. Utilizing post-stack seismic attributes is a quick and straightforward method for quantitatively characterizing these reservoirs. However, reservoir prediction deteriorates when dealing with complex sedimentary volumes and intricate tectonic development. Spectral decomposition (SD) offers an alternative approach to optimize the seismic data. The frequency-dependent S-transform (ST) holds great potential in seismic interpretation. SD based on the ST was employed in the seismic sedimentary characterization of steep slope complex fan reservoirs. Three fourth-order sequence stratigraphic boundaries and three complex fans were ideally shown on seismic frequency decomposition profiles. A 20 Hz seismic sedimentology analysis frequency was determined by comparing three spectral decomposition results following the well-seismic reflection analysis. The internal architectures of fan deltas and the individual outlines of nearshore subaqueous fans were more distinguishable in 20-Hz frequency decomposition data than in full-frequency data. The progradation direction of steep slope fans can be better recognized in frequency decomposition profiles compared to full-frequency seismic data. Three factors influence the seismic sedimentary characterization and prediction of steep slope fans when employing SD. The ability of the ST to preserve phase is crucial for improving the imaging quality of the amplitude attribute. Sedimentary mechanisms control the sedimentary features of steep slope fans, impacting the imaging of seismic attributes. While channelized fan deltas can be better identified, unchannelized nearshore subaqueous fan deposits, which exhibit more heterogeneous sedimentary characteristics, present limitations. The unique volcanic evolution is another factor that impacts the image of the root-mean-square (RMS) attribute. Despite demonstrating excellent local adaptability in signal analysis, the S-transform cannot fully compensate for the combined effects of faults and sedimentary heterogeneity in nearshore subaqueous fans.