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Seismic impedance inversion has become a common approach in reservoir prediction. At present, the critical issue in the application of seismic inversion is its low computational efficiency, especially in 3D. To improve the computational efficiency, we have developed an inversion method derived from the proximal objective function optimization algorithm. Our inversion method calculates each unknown parameter in the model vector, one by one during iteration. Compared with routine gradient-dependent inversion algorithms, such as the iteratively reweighted least-squares (IRLS) algorithm, our inversion method has lower computational complexity as well as higher efficiency. In addition, to obtain a sparse reflectivity series, a long-tailed Cauchy distribution is used as the a priori constraint. The weak nonlinear problem owing to the introduction of Cauchy sparse constraint is addressed by taking advantage of reweighting strategy. Results of synthetic and real data tests illustrate that the proposed inversion method has higher computational efficiency than IRLS algorithm, and its inversion accuracy remains the same.

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Poststack seismic impedance inversion is an effective approach for reservoir prediction. Due to the sensitivity to noise and the oscillation near the bed boundary, Gaussian distribution constrained seismic inversion is unfavorable to delineate the subtle-reservoir and small-scale geologic features. To overcome this shortcoming, we have developed a new method that incorporates a priori knowledge in the seismic inversion through a preconditioning impedance model using the adaptive edge-preserving smoothing (Ad-EPS) filter. The Ad-EPS filter preconditioned impedance model for a blocky solution makes the formation interfaces and geologic edges more precise and sharper in the inverted impedance results and keeps the inversion procedure robust even if random noise exists in the seismic data. Furthermore, compared with the conventional EPS filter, the Ad-EPS filter is able to resolve thick and thin geologic features through window size scanning, which is used to find the best-fitting window size for each sample to be filtered. The results of numerical examples and real seismic data test indicate that our inversion method can suppress noise to obtain a “blocky” inversion result and preserve small geologic features.