Distributed Acoustic Sensing (DAS) enables large-scale monitoring through optical fibers, but its high dimensionality and complex spatio-temporal patterns make event classification demanding. Existing deep learning approaches-CNNs, recurrent models, and Transformer variants-either fail to capture long-range dependencies or require processing raw DAS matrices at prohibitive cost. We propose DAStatFormer, a hybrid multibranch Transformer that combines compact multidomain statistical features with Gated Transformer Networks. Instead of raw signals, we extract 24 ANOVA-selected attributes per channel from the temporal, waveform, and spectral domains, reducing data size by orders of magnitude while preserving discriminative information. Each domain is processed via dedicated step-wise and channel-wise attention branches, fused by an adaptive gating mechanism. Experiments on the open $Φ$-OTDR benchmark and a real-scenario DAS dataset show that DAS-tatFormer achieves up to 99.4% accuracy and near-perfect real-world performance, while using significantly fewer parameters and lower inference cost than models such as DASFormer and DeepViT. These results demonstrate its suitability for scalable, real-time DAS-based monitoring. We release our code at https://github.com/MichelD-git/DAStatFormer

翻译：分布式声学传感（DAS）通过光纤实现大规模监测，但其高维度和复杂的时空模式使得事件分类极具挑战性。现有的深度学习方法——CNN、循环模型及Transformer变体——要么无法捕获长程依赖关系，要么需要以高昂成本处理原始DAS矩阵。我们提出DAStatFormer，一种将紧凑的多域统计特征与门控Transformer网络相结合的混合多分支Transformer。该方法不直接使用原始信号，而是为每个通道提取时域、波形域和频域中经ANOVA筛选的24种属性，在保留判别性信息的同时将数据量降低数个数量级。每个域通过专用的逐步骤和逐通道注意力分支进行处理，并由自适应门控机制融合。在公开Φ-OTDR基准测试和真实场景DAS数据集上的实验表明，DAStatFormer实现了高达99.4%的准确率和接近完美的实际场景性能，同时使用的参数量和推理成本均显著低于DASFormer和DeepViT等模型。这些结果证明了其适用于可扩展的实时DAS监测。我们已在https://github.com/MichelD-git/DAStatFormer 公开发布代码。