## 1 Introduction  
In the exploration of complex hydrocarbon reservoirs and subtle reservoirs, enhancing seismic resolution is essential [1]. This can be achieved through multiple approaches, including data acquisition, processing [2, 3], and post-stack inversion techniques. As a core method for quantitative reservoir characterization, post-stack seismic inversion significantly improves thin-bed identification and reservoir prediction accuracy by transforming seismic data into physical property parameters such as impedance. With the development of multi-physics integrated exploration [4], interdisciplinary methods like gravity and electromagnetic inversion have become important supplements to seismic inversion [5]. Post-stack seismic inversion is inherently ill posed, requiring regularization constraints to stabilize the solution [6]. The selection of regularization terms should align with the desired characteristics of the inversion results. Specifically, L2-norm (minimizing the sum of squared values) regularization is suitable for Gaussian-distributed solutions; L1-norm(minimizing the sum of absolute values) [6; 7] or Lp-norm \(0<p\leq 2\)[8; 9] constraints apply to sparse signals; Tikhonov regularization [10] enforces smoothness; and total variation regularization [11] preserves blocky structures. Moreover, advanced techniques like maximum entropy and Cauchy constraints [6] can also be implemented based on geological requirements. However, these constraints primarily optimize vertical structures in single-trace inversion, which faces inherent limitations--a trade-off between sparsity weight and noise suppression: reduced weights enhance resolution but weaken noise attenuation, whereas stronger denoising causes signal oversmoothing. Furthermore, single-trace inversion may introduce spurious high-frequency components when layer thickness falls below one-quarter wavelength, necessitating spatial constraints to ensure solution reliability. Multitrace inversion extends single-trace methods by adding lateral constraints, optimizing both vertical and horizontal structures through regularization, alongside data fidelity and sparsity constraints. Key developments include the following: [PERSON] and [PERSON] [12] used Markov-Bernoulli fields for 2D reflectivity; [PERSON] and [PERSON] [13] combined sparse priors with seismic data in Bayesian deconvolution; and [PERSON] and [PERSON] [14] modeled trace correlations via Markov fields. [PERSON] and [PERSON] [15] introduced sparse blind deconvolution; [PERSON] [16] preserved blocky features with total variation. [PERSON] [17] implemented frequency-domain sparse inversion. [PERSON] [18] enhanced continuity with predictive filtering. [PERSON] [19] used block-sparse learning for reflectivity. [PERSON] [20] improved accuracy with structural constraints. [PERSON] [21] and [PERSON] [22] outperformed sparse spike inversion using data-driven constraints. [PERSON] [23] and [PERSON] [24] optimized reflectivity pulses via simulated annealing. [PERSON] [25], [PERSON] [26], and [PERSON] [27] integrated dip and fault data. [PERSON] [28] and [PERSON] [29] developed adaptive operators. [PERSON] [30] and [PERSON] [31] employed deep learning for geological constraints in Bayesian inversion, reducing non-uniqueness while maintaining geological consistency. Current multitrace structural-constrained inversion methods predominantly utilize spatial information solely for initial model construction [26]. Although the initial model inherently embodies lateral constraints, these constraints either remain inactive during inversion or merely prevent excessive deviation from the initial model, without effectively exploiting intertrace spatial relationships and structural features in seismic data [30]. Consequently, such approaches essentially remain single-trace inversions.