## 1 Introduction
The alternative approach employs stochastic simulation, wherein variogram analysis quantifies spatial variability to guide optimal correlated sample selection for interwell reservoir parameter estimation, generating probabilistic outputs [23]. While this technique enhances vertical resolution, it inherently compromises lateral resolution and introduces significant stochasticity [24; 25; 26]. Additionally, the methodology requires evenly distributed sampling points to ensure statistical validity. A critical limitation inherent in both seismic inversion methodologies lies in their over-reliance on well-derived data for high-frequency component estimation, while inadequately exploiting seismic information through integrated interpretation. Although seismic data's narrow effective bandwidth inherently prevents direct high-frequency signal acquisition, its lateral amplitude variations systematically encode depositional environment evolution. Congenetic depositional systems manifest analogous stratigraphic architectures whose spatial transitions exhibit intrinsic correlations with waveform morphologies [27; 28]. This causal relationship enables high-frequency component estimation through the systematic analysis of seismic waveform lateral variability. Attribute fusion technology enables enhanced extraction of latent information within datasets, significantly improving the reliability of complex reservoir characterization. This methodology bifurcates into two fundamental paradigms: (1) algorithmic synthesis through mathematical transformations including composite attribute generation, principal component analysis (PCA), multivariate linear regression [29; 30], and cluster analysis; (2) visual transformation techniques employing chroma-luminance binarization or RGB trichromacy to decode reservoir heterogeneity through enhanced data visualization [31; 32]. The mathematical fusion approach, alternatively conceptualized as attribute optimization, frequently incorporates a priori well-derived constraints. In contrast, image-based visualization techniques operate independently of well data, proving particularly effective in highlighting reservoir anomalies through spatial pattern recognition--a capability exceeding conventional mathematical frameworks [33; 34; 35]. Intelligent seismic fusion fundamentally extends linear fusion principles through machine learning-driven nonlinear operators, implementing PCA and cluster analysis as essential preprocessing steps for dimensionality reduction [36; 37; 38]. Optimal algorithm selection requires a systematic evaluation of reservoir characterization objectives against available sample density. Current methodologies are stratigraphically categorized into supervised (well-constrained) and unsupervised (data-driven) fusion architectures [39]. Supervised techniques demonstrate superior performance through seismic-well log co-learning mechanisms, though their efficacy remains spatially constrained to well-penetrated zones and exhibits a positive correlation with well density. While seismic attributes effectively amplify specific geophysical signatures to enhance lithofacies discrimination, this spectral emphasis paradoxically induces information attenuation in complementary domains. Such inherent selectivity fundamentally limits their capacity for holistic stratigraphic representation, as substantiated in seismic interpretative practice [40]. Moreover, seismic attributes face a pervasive challenge in seismic interpretation, namely the inherent ambiguity associated with these attributes [41]. This ambiguity predominantly manifests in two aspects. First, distinct geological phenomena can present highly similar seismic attribute responses. For instance, strong amplitude responses may indicate the presence of unconformities, carbonate rocks, igneous intrusions, coal seams, and other geological features, leading to uncertainties in interpreting seismic attributes [42; 43].