## 1 Introduction  
With the gradual advancement of oil and gas exploration and development from conventional structures to concealed and complex areas, reservoir characterization has become increasingly crucial for boosting development efficiency and managing geological risks [1]. Especially in tectonic zones caused by faults and abrupt lithological changes, accurately delineating reservoir boundaries, faults, and heterogeneity zones remains a key technical issue for improving the oil and gas discovery rate and development benefits [2]. Seismic attribute analysis has emerged as a critical bridge between seismic data and geological structure, playing an essential role in reservoir prediction, tectonic interpretation,and sedimentary system analysis [3; 4; 5]. The typical structural attributes include curvature, coherence, variance, phase, and ant tracking. Among these, coherence attributes are particularly valued for the ability to detect spatial discontinuity [6; 7; 8]. Thus, they are considered as vital tools to identify faults, channels, and other subsurface discontinuities [9; 10]. Coherence is essentially a similarity measure to capture lateral inconsistencies in seismic events due to variations of tectonics and strata. Since the introduction of the first-generation coherence algorithm by [PERSON] and [PERSON] [11], the technique has undergone extensive development. The second-generation methods incorporated various similarity metrics, such as structural similarity and energy consistency [12; 13], while the third-generation approaches based on structure tensors and eigenvalue decomposition significantly improved sensitivity to local structure [14; 15]. Tensor-based methods [16; 17] further enhanced the ability to characterize the geometric shape of events. Additional improvements, such as dynamic time warping [18] and a series of post-processing techniques [19; 20; 21; 22; 23; 24; 25; 26; 27; 28], to a certain extent, not only contribute to noise suppression but also clarify boundaries. To further expand the coherence attributes, various strategies have been proposed in terms of model trace construction, frequency fusion, and azimuth enhancement. [PERSON] et al. [29] introduced the concept of \"super-traces\" combined with adjacent traces using fourth-order statistics to detect subtle features. [PERSON] and [PERSON] [30] fused coherence slices across different frequency bands using RGB techniques for multi-scale geological body identification. Building on this, [PERSON] [31], inspired by [PERSON] [32], applied multi-spectral covariance matrices and eigenvalue decomposition, overcoming the three-frequency limitation of traditional RGB fusion. [PERSON] [33] proposed coherence calculations based on data divided by azimuth to assist with velocity analysis. [PERSON] [34] integrated geologically steered complex coherence [35] with instantaneous phase [36] to produce a phase-guided coherence volume for 3D discontinuity detection. [PERSON] [37] further introduced weighted functions and dip-scanning strategies to enhance breakpoint localization and noise robustness. [PERSON] [38] proposed conducting geological-oriented coherence attribute analysis based on intelligent high-resolution seismic processing and validated the reliability of the high-resolution and structure-guided coherence computation using drilling engineering data. In recent years, deep learning-based fault detection methods have also made certain progress [39; 40; 41]. Nevertheless, they remain limited by high dependence on labeled data and insufficient ability to cross-regional generalization. However, conventional coherence-based methods still face two fundamental challenges when applied to structurally complex settings. First, most approaches rely on fixed-direction windows or symmetric weighting schemes, which lack adaptability to variations in reflector orientation. This limitation becomes particularly pronounced near fault bends, flexures, or stratigraphic unconformities, where structural boundaries often become blurred or misaligned.