Fault identification is a critical task in 3D seismic interpretation, directly influencing the efficiency and accuracy of reservoir characterization and drilling decisions. However, traditional methods rely heavily on manual experience and high-quality annotated data, making it difficult to adapt to the demands of processing massive amounts of seismic data. To address this, we introduce a novel self-supervised learning (SSL) paradigm specifically designed for geological feature learning, which for the first time unifies masked voxel reconstruction, 3D patch contrastive learning, and multimodal attribute joint contrast into a coherent multi-task pre-training framework. This framework is uniquely tailored to leverage the spatial continuity and physical attributes of seismic data, enabling the model to learn transferable, geologically meaningful representations without any manual labels. It leverages unlabeled seismic data to learn geologically meaningful feature representations and leverages a transfer learning mechanism to achieve high-precision fault identification with small sample sizes. Experiments on multiple public and field-measured datasets, including F3 and SEAM Phase I, demonstrate that this method achieves key metrics such as intersection over union (IoU) and F1-scores of 0.76 and 0.87, respectively, significantly outperforming traditional attribute analysis (IoU = 0.49) and supervised deep learning models (IoU = 0.72). Furthermore, the method remains robust in areas with low signal-to-noise ratios (average confidence > 85%) and consistently estimates fault strike and dip (average absolute error ≤ 3.5°). This research provides an effective solution for reducing reliance on manual annotation and improving the reliability of automated fault interpretation in complex tectonic areas.