用统计特征+多分支Transformer提升光纤声学传感的实时识别精度
DAStatFormer: A Hybrid Multibranch Transformer with Statistical Feature Integration for DAS-Based Pattern Recognitions
- 从时域、波形、频域提取24个关键统计特征，大幅压缩数据量
- 多分支注意力融合+自适应门控，实现99.4%分类准确率
- 参数少、推理快，适合大规模实时监控场景
分布式声学传感（DAS）通过光纤实现大范围监测，但其高维时空模式使事件分类极具挑战。现有深度学习方法——卷积网络、循环模型及Transformer变体——或难以捕捉长程依赖，或需以原始DAS矩阵为输入，计算成本过高。本文提出DAStatFormer，一种结合紧凑多域统计特征与门控Transformer网络的混合多分支结构。不直接处理原始信号，而是从时间、波形、频域每通道提取24个方差分析筛选出的属性，使数据规模降低数个数量级，同时保留判别信息。各域分别通过逐步与通道注意力分支处理，由自适应门控机制融合。在开放的Φ-OTDR基准数据集及真实场景DAS数据集上的实验表明，DAStatFormer最高达99.4%准确率，且在真实环境中表现近乎完美，参数量和推理开销显著低于DASFormer与DeepViT。结果证明其适用于可扩展、实时的DAS监测。代码已开源至https://github.com/MichelD-git/DAStatFormer。
原文摘要 · Abstract (English)
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
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