Depositional Stage Subdivision and Quantitative Characterization of Gravity-Flow Deposits Using Wavelet Synchrosqueezed Transform

The stage subdivision of gravity-flow deposits is crucial for identifying deep-water reservoirs. Traditional methods relying on visual core observation or macroscopic well log correlation are often subjective and lack the resolution to decipher dynamic fluid evolution. In this study, we introduced the Wavelet Synchrosqueezed Transform (WSST) to process a multi-log reconstructed curve from the Daluhu Area in the Dongying Depression. Compared to the traditional Continuous Wavelet Transform (CWT), WSST effectively eliminates spectral smearing, reassigning energy into highly focused energy ridges within the 0.5–2.5 m scale window to provide objective mathematical boundaries for depositional stages. Four quantitative parameters—Time-Frequency Concentration (TFC), Instantaneous Bandwidth (IBW), Energy Evolution Gradient (EEG), and Ridge Center Offset (RCO)—were extracted from the WSST matrix. Results show that each parameter serves a distinct geological purpose: EEG spikes accurately locate basal erosional scour surfaces; TFC characterizes the rhythmic amalgamation of gravity flow pulses; IBW captures the high-frequency algorithmic noise unique to massive homogeneous sandstones; and RCO tracks the macroscopic energy decay trajectory of individual flow events. This method offers a highly automated and objective tool for characterizing the hydrodynamic evolution of gravity flows, provided it is properly calibrated with core data to avoid algorithmic ambiguity.