Abstract
Acoustic impedance (AI) inversion method based on the assumption of spatially invariant seismic wavelets has been widely applied in reservoir prediction. In reality, seismic wavelets are relatively stable but also exhibit subtle spatial variations, particularly when inconsistencies arise between seismic data and reservoir heterogeneity. To address this issue, we propose an alternate iterative inversion method of AI and wavelet based on sparse constraints. This method assumes that both the wavelet matrix and the reflection coefficients are sparse. Based on the Lp norm, we establish objective functions for wavelet estimation and AI inversion with sparse regularization constraints of different forms, ensuring the stability of the inversion results during the iterative process. In the inversion process, the statistical wavelet is used as the initial wavelet input to obtain the AI; then, the AI results are used to update the wavelet, and the two parameters are iteratively updated in this way. Through this alternating iteration, spatial variations of the wavelet are effectively captured, eliminating discrepancies in waveform and frequency characteristics of seismic data in the horizontal direction due to nonimpedance variations. Finally, the effectiveness of the proposed method is validated through model testing and application to actual data. The proposed method can invert impedance results that are more consistent with logging data, and the estimated wavelets synthesized seismogram show a better match with the well bypass. In particular, the proposed method is more applicable to the case where it is difficult to use a single wavelet to calibrate all wells simultaneously during the well-seismic calibration in the work area with multiple well data.
Paper Information
Lian Zhao, Danping Cao, Yuqiang Zhang, Zhidi An, Kai Lin and Xiaotao Wen. Alternate Iterative Inversion of Acoustic Impedance and Wavelet Based on Sparse Constraints. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63, 3530566. https://doi.org/10.1109/TGRS.2025.3530566.
