TY - JOUR

T1 - Dip-Guided Poststack Inversion via Structure-Tensor Regularization

AU - Корчуганов, Владислав Дмитриевич

AU - Дучков, Антон Альбертович

AU - Голубева, Маргарита Сергеевна

PY - 2026/2/6

Y1 - 2026/2/6

N2 - Seismic inversion is an established technique for quantitative reservoir characterization, providing estimates of subsurface elastic properties such as acoustic impedance. Since conventional inversion is typically performed independently on each trace, lateral instability of the solution remains a major challenge. A common stabilization strategy relies on horizontal-gradient penalization, which suppresses speckle noise under the assumption of horizontal stratification. However, in complex geological settings with abrupt lateral variations, such approaches may introduce secondary artifacts and oversmoothing. In this study, we propose a dip-guided regularization technique based on structure–tensor total variation (STV). The proposed regularizer incorporates local structural orientation by constructing structure tensors directly from the evolving impedance model and guiding smoothing along dominant geological directions. In contrast to conventional neighbor-trace penalization, this formulation preserves steeply dipping layers and fault-related discontinuities, yielding more stable and geologically consistent inversion results. Unlike existing structure-oriented inversion methods, the proposed approach does not require pre-computation of structural attributes from the seismic volume, as the regularization constraint is updated in situ at each iteration. On synthetic data, the proposed method reduces the RMSE by 53%, increases the correlation coefficient from 0.95 to 0.99, and improves SSIM from 0.83 to 0.89, while preserving sharp layer boundaries. On a field dataset, STV improves the correlation from 0.84 to 0.90 and reduces the RMSE by 19.4%, resulting in enhanced structural fidelity and clearer reservoir compartment delineation.

AB - Seismic inversion is an established technique for quantitative reservoir characterization, providing estimates of subsurface elastic properties such as acoustic impedance. Since conventional inversion is typically performed independently on each trace, lateral instability of the solution remains a major challenge. A common stabilization strategy relies on horizontal-gradient penalization, which suppresses speckle noise under the assumption of horizontal stratification. However, in complex geological settings with abrupt lateral variations, such approaches may introduce secondary artifacts and oversmoothing. In this study, we propose a dip-guided regularization technique based on structure–tensor total variation (STV). The proposed regularizer incorporates local structural orientation by constructing structure tensors directly from the evolving impedance model and guiding smoothing along dominant geological directions. In contrast to conventional neighbor-trace penalization, this formulation preserves steeply dipping layers and fault-related discontinuities, yielding more stable and geologically consistent inversion results. Unlike existing structure-oriented inversion methods, the proposed approach does not require pre-computation of structural attributes from the seismic volume, as the regularization constraint is updated in situ at each iteration. On synthetic data, the proposed method reduces the RMSE by 53%, increases the correlation coefficient from 0.95 to 0.99, and improves SSIM from 0.83 to 0.89, while preserving sharp layer boundaries. On a field dataset, STV improves the correlation from 0.84 to 0.90 and reduces the RMSE by 19.4%, resulting in enhanced structural fidelity and clearer reservoir compartment delineation.

KW - Lateral continuity constraint

KW - Multichannel seismic inversion

KW - Noise suppression

KW - Reservoir characterization

KW - Structural-orientation regularization

KW - Structure Tensor Total Variation (STV)

U2 - 10.1109/LGRS.2026.3661420

DO - 10.1109/LGRS.2026.3661420

M3 - Article

JO - IEEE Geosciense and Remote Sensing Letters

JF - IEEE Geosciense and Remote Sensing Letters

SN - 1558-0571

ER -