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Sparse Seismic Impedance Inversion Constrained by CP Tensor Decomposition
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DOI：10.1109/lgrs.2026.3718286.png)
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Abstract
En 中文
Sparse seismic impedance inversion has been widely applied in hydrocarbon reservoir prediction; however, existing methods exhibit limited robustness to noise. To address this issue, this letter proposes a sparse impedance inversion method constrained by CANDECOMP/PARAFAC (CP) tensor decomposition. Unlike conventional sparse impedance inversion methods, the proposed approach exploits the nonlocal structural similarity information from the seismic records through CP tensor decomposition and incorporates such information into the inversion via a regularization term. This strategy is expected to reduce the interference of noise in the inversion results and may enhance the noise resistance of the inversion. The CP tensor decomposition constraint and the anisotropic total variation (ATV) sparsity constraint are incorporated into the objective function. The resulting optimization problem is solved using the alternating direction method of multipliers (ADMMs) and a Sylvester equation solver. Results from synthetic models and field data indicate that the proposed method has the potential to improve the noise robustness of inversion results.
Keywords:
Alternating direction method of multipliers (ADMMs)
seismic impedance inversion
tensor constraint
total variation constraint
Journal
I
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4.4
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585
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0