ABSTRACT Inverse Q filtering is a seismic data attenuation compensation technique based on the theory of wavefield downward continuation, designed to improve the resolution of seismic data. Mathematically, it can be formulated as an inverse problem, which can be stably solved using the ‐norm regularization constraint to avoid instability during the inversion process. However, the selection of the regularization parameter critically affects the balance between signal compensation and noise suppression. A small regularization parameter improves signal recovery but may amplify noise, whereas a large one yields sparser results that suppress noise at the cost of weakening signal compensation. To achieve adaptive enhancement of seismic data, we propose a time‐frequency domain signal‐to‐noise ratio‐guided adaptive seismic signal attenuation compensation algorithm. By adaptively tuning the regularization parameter based on the time‐frequency domain signal‐to‐noise ratio, the proposed method effectively mitigates noise amplification and insufficient signal recovery caused by improper parameter selection. Experimental results on both synthetic and field seismic data confirm that the proposed approach significantly outperforms conventional methods in simultaneously enhancing effective signals and suppressing noise amplification, which in turn enhances the reliability of the thin reservoir interpretation.
Zhang et al. (Fri,) studied this question.