Data Release for "Data-Driven Optimization and 2D Modal Decomposition Reveal Fine-Scale Stratigraphic Architecture and Sedimentary Cyclicity: A New 2D-SVMD-PLO Framework"

To overcome the challenges of spatial discontinuity and subjective parameter selection inherent in conventional time–frequency analysis and trace-by-trace modal decomposition methods in seismic sedimentology, this study proposes a novel parameter-adaptive framework: two-dimensional successive variational mode decomposition based on the polar lights optimizer (2D-SVMD-PLO). In this framework, the minimization of the two-dimensional information entropy of the decomposed intrinsic mode function (IMF) set serves as the objective function, while the PLO is employed to achieve data-driven, adaptive optimization of the key parameters in 2D-SVMD. This design ensures both the lateral continuity of the decomposition results and the objectivity of parameter determination. Applications to synthetic and field seismic datasets demonstrate that 2D-SVMD-PLO not only suppresses random and high-energy coherent noise effectively but also extracts laterally continuous signals with clear geological significance. Furthermore, calibration with co-located well-log data confirms that the frequency-attribute sections generated by this method accurately capture the cyclic variations of sedimentary successions. Collectively, these results highlight that 2D-SVMD-PLO provides an objective and efficient tool for transforming seismic data into high-resolution stratigraphic cycle sections, thereby offering a robust pathway for fine-scale characterization of complex, thinly interbedded reservoirs and for advancing quantitative sequence stratigraphic analysis.