A depth-variant seismic wavelet extraction method for basis pursuit inversion with impedance trend constraint

The seismic images produced by pre-stack depth migration show more accurate subsurface structures than time images, resulting in a growing need for depth-domain inversion. However, due to the strong non-stationarity exhibited by depth-domain seismic data, time-domain inversion methods based on the convolutional model cannot be directly applied in the depth domain. To address this issue, we have developed a method for extracting a depth-variant seismic wavelet, which is then combined with a non-stationary convolutional model to enable direct inversion of the depth-domain acoustic impedance. First, we extend the Morlet wavelet to the depth domain and propose an orthogonal matching pursuit spectral decomposition method using the depth-domain Morlet wavelet. We then investigate the waveforms and wavenumber spectra similarities between the depth-domain Morlet wavelet and depth-domain Ricker wavelet and extract depth-variant Ricker wavelets from the depth-wavenumber spectrum. We add a depth-domain impedance trend constraint to the conventional basis pursuit inversion to enhance the lateral continuity of the inversion results. Then, we attain direct inversion of the depth-domain acoustic impedance. Tests of synthetic and field data demonstrate that the proposed method achieves high-accuracy inversion results while maintaining high computational efficiency, highlighting our approach's effectiveness and strong reservoir characterization potential.