Phase-sensitive optical time-domain reflectometry ($φ$-OTDR) is widely used in large-scale distributed acoustic sensing (DAS) because it provides distributed spatiotemporal monitoring over long sensing distances. Its field performance can still deteriorate because of polarization-induced fading (PIF), local signal degradation, and strong environmental interference. This study develops a Sagnac-assisted enhanced $φ$-OTDR sensing architecture and a standardized benchmark framework for engineering-oriented DAS event recognition. The Sagnac interferometer provides a continuous phase response that supplements fading-prone observations in the $φ$-OTDR channel, and heterogeneous signal alignment is achieved using a cross-correlation procedure implemented on an FPGA platform. The benchmark protocol compares conventional feature-engineering methods, probabilistic shallow classifiers, single-branch deep models, and dual-branch fusion models under consistent data partitioning, preprocessing, and metric definitions. Experiments on a 10-km sensing fiber with six representative acoustic event classes show that the dual-branch fusion model provides the most favorable trade-off among the evaluated methods, reaching 89.79\% accuracy, 89.83\% macro-F1, and a nuisance alarm rate of 5.00\% on the balanced test set. The results also show that channel grouping strongly affects dual-branch evaluation, indicating that deployment-oriented conclusions should be based on accuracy, macro-F1, nuisance alarm rate, false negative rate, and latency rather than accuracy alone. This work provides a physically motivated enhancement strategy for $φ$-OTDR-based DAS and a reproducible benchmark protocol for future fusion-oriented sensing research. The implementation and scripts for reproducing the DAS event-recognition experiments are publicly available at https://github.com/wawa-abc/das.

翻译：相位敏感光时域反射计（$φ$-OTDR）因其在长传感距离上实现分布式时空监测的能力，被广泛应用于大规模分布式声波传感（DAS）。然而，由于偏振诱导衰落（PIF）、局部信号退化及强环境干扰，其现场性能仍可能恶化。本研究开发了一种萨尼亚克辅助增强型$φ$-OTDR传感架构，以及面向工程化DAS事件识别的标准化基准框架。萨尼亚克干涉仪提供连续相位响应，可补充$φ$-OTDR通道中易受衰落影响的观测数据，并通过FPGA平台实现的互相关流程实现异构信号对齐。该基准协议在一致的数据划分、预处理和指标定义下，比较了传统特征工程方法、概率浅层分类器、单分支深度模型与双分支融合模型。基于10公里传感光纤的六类代表性声学事件实验表明，双分支融合模型在评估方法中提供了最优权衡：在均衡测试集上达到89.79%的准确率、89.83%的宏F1分数及5.00%的滋扰报警率。结果还显示，通道分组显著影响双分支评估，表明面向部署的结论应基于准确率、宏F1、滋扰报警率、漏报率及延迟等综合指标，而非单一准确率。本研究为基于$φ$-OTDR的DAS提供了物理驱动的增强策略，并为未来面向融合的传感研究提供了可复现的基准协议。复现DAS事件识别实验的代码及脚本已公开于 https://github.com/wawa-abc/das。