## 1 Introduction
Second, factors such as resolution limitations, interference from adjacent formations, and tuning effects on amplitude and frequency further contribute to the ambiguity in the geological interpretation of seismic attributes [44; 45]. For example, tuning effects can result in sand bodies of varying thicknesses displaying similar amplitude and frequency values across seismic attributes. Additionally, a pronounced amplitude response may originate from the target sand body or may be influenced by interference from neighboring geological formations [46]. Given the limitations and ambiguities associated with seismic attributes, numerous scholars have consistently focused their research efforts on two key areas aimed at improving the interpretive accuracy of these attributes. The first area emphasizes the development of novel seismic attributes [47], while the second explores methodologies for integrating multiple seismic attributes [48; 49]. In regions lacking drilling data, the combination of seismic waveform inversion techniques with intelligent analysis of multiple seismic attributes--constrained by sedimentary models--proves advantageous. Artificial intelligence approaches are particularly effective for addressing nonlinear challenges, such as the quantitative relationships between petrophysical parameters and seismic attributes. In the field of seismic waveform inversion and multimodal intelligent analysis, researchers have proposed a data-model co-driven seismic impedance inversion method [50]. Leveraging deep learning techniques, this approach identifies concealed fluvial channels in seismic images, overcoming the limitations of traditional model-driven methods. The inversion resolution is improved by 30% and the identification accuracy of hidden geological features (e.g., channels) exceeds 85% [51]. By integrating AI-generated initial impedance models with nonlinear modeling capabilities and physical constraints, the method optimizes low-frequency information in conventional inversion, achieving high-resolution seismic imaging [52]. Another study developed a multimodal deep learning framework for predicting reservoir parameters by fusing multimodal logging data (e.g., conventional logs and dynamic resistivity imaging) [53]. Applied to carbonate reservoirs in the Gaoshit-Moxi block (Sichuan Basin), this method enhances porosity and permeability prediction accuracy by 25% compared to traditional approaches, addressing challenges in heterogeneous reservoir characterization [54]. AI-driven advancements including data-driven modeling, multimodal fusion, and automation significantly improve the precision and efficiency of geophysical exploration [55]. Applications span seismic inversion, reservoir prediction, and complex hydrocarbon reservoir development, offering innovative solutions for subsurface resource exploration [56]. In the field of computer science, artificial intelligence technologies, particularly those advancing through deep learning, are evolving rapidly. By actively employing machine learning methods to probe into the inherent complex features and patterns of seismic attributes, researchers can attain a more comprehensive and nuanced understanding of seismic data. The application of deep learning in geophysical research demonstrates significant potential for future advancements [57]. As a result, the integration of these attributes provides an objective and holistic depiction of the variations in geophysical responses and patterns across diverse lithological strata [58; 59]. Self-Organizing Maps (SOM), an unsupervised learning technique for clustering and high-dimensional visualization, is an artificial neural network inspired by the human brain's signal processing characteristics. This method, introduced by Professor [PERSON] of the University of Helsinki, Finland [60; 61], has become the most widely utilized self-organizing neural network approach. Known for its robust self-organization and visualization capabilities, the SOM algorithm has gained widespread application and extensive research interest.