Abstract Fault detection represents a crucial task in seismic interpretation, with significant implications for hydrocarbon exploration. Advancements in artificial intelligence have driven widespread adoption of machine learning techniques to tackle complex scientific and engineering problems. CNNs, Transformers, and their adaptive variants have been extensively applied to seismic fault detection, demonstrating promising efficacy across geological exploration applications. However, the large parameter counts and high computational complexity intrinsic to these models severely limit their applicability to resource‐constrained edge devices. With the progressive maturation of horizontal well drilling, data processing demands for the mobile Logging‐While‐Drilling (LWD) systems during drilling operations have become increasingly urgent. To address these requirements, we present LightGEUnet: a lightweight Unet architecture enhanced with two innovative modules–the Grouped Hadamard Product Attention for Multi‐axis (GHPA‐M) for cross‐dimensional feature refinement and Group Aggregation Fusion (GAF) for hierarchical feature integration. The GHPA‐M architecture employs group
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