Multichannel Closed-Loop Seismic Acoustic Impedance Estimation With Nonlinear Correction

Acoustic impedance (AI) is an important parameter for seismic reservoir characterization. Traditional algorithms can obtain AI whereas the resolution is open to improvement. Single-channel supervised algorithms can characterize seismic data accurately to achieve high-resolution AI at the cost of a large volume of labels. In the case of limited labels, the single-channel closed-loop (SCCL) algorithm based on linear convolution forward modeling can provide some help while the horizontal continuity can be enhanced. To further improve the horizontal continuity, we propose a multichannel closed-loop (MCCL) AI estimation algorithm for seismic data with limited single-channel labels. By masking multichannel output with ones at the well locations and zeros at nonwell locations, we properly use available single-channel labels for network training. A patching strategy is also used to enlarge labeled data. Besides, the used linear convolution operator cannot fully characterize complicated features in post-stack profiles, so we design a nonlinear correction procedure to capture refined features. Nonstationary examples of the Marmousi-II model with five single-channel labels quantitatively demonstrate the effectiveness of the proposed MCCL algorithm with nonlinear correction. It achieves superiority in terms of the recovered signal-to-noise ratio (S/N) when compared to SCCL with/without nonlinear correction and MCCL. The obtained AI profile of field data using the MCCL algorithm with nonlinear correction is of high resolution and horizontal continuity visually. Quantitative assessments at the well locations underscore the proposed algorithm’s ability to improve AI estimation performance.