DAStatFormer: A Hybrid Multibranch Transformer with Statistical Feature Integration for DAS-Based Pattern Recognitions

# DAStatFormer: A Hybrid Multibranch Transformer with Statistical Feature Integration for DAS-Based Pattern Recognitions
Source: [https://arxiv.org/abs/2606.00081](https://arxiv.org/abs/2606.00081)
[View PDF](https://arxiv.org/pdf/2606.00081)

> Abstract: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 $\\Phi$\-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[this https URL](https://github.com/MichelD-git/DAStatFormer)

## Submission history

From: Michel Dione \[[view email](https://arxiv.org/show-email/ca05c4a0/2606.00081)\] \[via CCSD proxy\] **\[v1\]**Fri, 22 May 2026 13:58:37 UTC \(5,851 KB\)