## 1 Introduction  
The primary objective of seismic exploration is to delineate subsurface geological structures and characterize reservoir distributions to support energy resource development [1]. The seismic data is acquired through high-resolution three-dimensional seismic survey utilizing artificial sources to send seismic energy into the earth. This energy reflects off subsurface geological layers and is subsequently recorded by a dense, areal grid of surface receivers. The collected reflection data is then processed to build a 3D digital model of the subsurface geology. As a critical seismic attribute, waveform data encapsulates extensive information about subsurface structures, reservoirs, and hydrocarbon content. Among various analytical methods, waveform shape clustering has become a widely adopted technique for interpreting oil and gas content through seismic attribute analysis [2]. Unlocking the rich reservoir-related information embedded within seismic waveforms can significantly enhance reservoir prediction, improve the accuracy of reservoir characterization, and increase drilling success rates [3, 4]. Traditionally, seismic attribute analysis has relied heavily on linear dimensionality reduction methods, such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) [5]. However, due to the inherently complex and nonlinear relationship between seismic signals and reservoir properties, these linear approaches often fail to accurately capture the geometric structure of high-dimensional data [6]. As a result, features derived from such reductions typically lack the capacity to retain the latent information within the original waveform data and cannot effectively simulate the seismic response of pore fluids [7; 8; 9]. With advancements in seismic exploration technology, there is a pressing need for new approaches capable of extracting abstract seismic attributes, analyzing waveform variation patterns, elucidating the implications of different waveform signatures, and ultimately achieving more accurate geological body identification. In recent years, manifold learning--an essential subfield of nonlinear dimensionality reduction--has shown considerable promise in revealing the intrinsic structure of data with heterogeneous distributions and in suppressing noise by projecting high-dimensional seismic data into a lower-dimensional space [10; 11]. This methodology offers fresh opportunities for seismic waveform feature extraction and optimization. In seismic exploration, earlier studies adopted algorithms such as Local Linear Embedding (LLE) and t-Distributed Stochastic Neighbor Embedding (t-SNE) [12; 13]. While these approaches surpass Principal Component Analysis (PCA) in many aspects, manifold learning techniques inevitably experience information loss during the projection process [14]. Even the more recent nonlinear dimensionality reduction method Uniform Manifold Approximation and Projection (UMAP) is constrained by shallow feature representations and a limited generalization capacity [15]. This limitation causes it to struggle in capturing the deeper information embedded in seismic waveforms, ultimately rendering it less effective for interpretation tasks in geologically complex regions. The extraction of high-quality features is a critical component in clustering seismic waveform data [16]. Deep learning has attracted considerable attention in seismic waveform classification for its hierarchical capacity to learn and represent complex, meaningful features [17; 18]. Compared to traditional methods, deep learning approaches are capable of extracting more advanced and robust representations [19]. A variety of deep learning architectures have been successfully applied to seismic waveform classification by simulating human cognitive processes to interpret data and uncover latent attributes [20; 21]. Among these, the Convolutional Neural Network (CNN) has been applied widely due to its high efficiency in minimizing the operating parameters and enhancing the speed of operation.