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

The seismic images produced by prestack depth migration indicate more accurate subsurface structures than time images, resulting in a growing need for depth-domain inversion. However, due to the strong nonstationarity 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 develop a method for extracting a depth-variant seismic wavelet, which is then combined with a nonstationary convolutional model to enable direct inversion of the depth-domain acoustic impedance (AI). First, we extend the Morlet wavelet to the depth domain and develop 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 AI. Tests of synthetic and field data demonstrate that our method achieves high-accuracy inversion results while maintaining high computational efficiency, highlighting our approach’s effectiveness and strong reservoir characterization potential.