Abstract
With the expansion of oil and gas exploration into deep and complex reservoirs, the prestack amplitude versus angle (AVA) inversion technique faces challenges due to amplitude attenuation and phase distortion caused by formation absorption effects, which limit the accuracy of seismic attribute characterization. To address the limitations of existing compensation methods, particularly poor noise robustness and insufficient lateral continuity, we propose a dictionary learning-based AVA feature restoration method for prestack gathers. First, local AVA features extracted from well-log data are used to construct a training dataset using a sliding time window, and the K-Singular Value Decomposition (K-SVD) algorithm is used to train an overcomplete dictionary that sparsely represents attenuation-free signals. Subsequently, the dictionary learning process is embedded into the absorption compensation objective function, where dictionary atoms and sparse coefficients are alternately optimized via orthogonal matching pursuit (OMP) algorithm and gradient descent (GD) algorithm to achieve effective signal-noise separation. Synthetic tests show that, compared with conventional methods, the proposed approach restores weak reflection energy, compensates for angle-dependent amplitude distortion, and exhibits reduced dependence on Q-model accuracy with markedly improved noise robustness. Field data applications demonstrate the advantages of the proposed method in improving lateral continuity and restoring AVA responses under complex geological conditions, providing data-driven support for high-precision prestack elastic-parameter inversion.