Multiscale Stepwise Learning Network for Seismic Impedance Inversion

Due to the limitations of seismic data resolution, conventional inversion methods are commonly limited by the band-limited nature of seismic data, noise interference, and insufficient resolution for subtle geological targets. This study proposes a continuous wavelet transform (CWT)-assisted multiscale stepwise learning (MSL) workflow for seismic impedance inversion. The proposed workflow transforms seismic traces into time–frequency representations using CWT and progressively learns different frequency components through convolutional modules with different kernel sizes. A seismic forward-modeling constraint is further introduced to improve the consistency between the predicted impedance and the observed seismic response at non-well locations. The network takes a single seismic trace represented by CWT coefficients in the 1–80 Hz frequency range and a corresponding low-frequency initial impedance model as inputs, and outputs the acoustic-impedance trace at each time sample. The main methodological novelty is the progressive learning of different frequency components using frequency-dependent convolutional kernels. The method is evaluated on a 3D seismic dataset from the Sichuan Basin, China, using leave-one-well-out validation involving seven wells. In addition, we provide detailed ablation experiments and quantitative analysis. Across all seven blind-well tests, the proposed method consistently achieves the highest mean correlation coefficient (0.8509) and the lowest mean MSE (0.0636) among the compared methods. These results indicate that the proposed workflow can effectively integrate multiscale seismic information and provide effective support for seismic impedance inversion.