ANS-coded high-ratio data compression for a distributed acoustic sensing system

Distributed acoustic sensing (DAS) based on phase-sensitive optical time-domain reflectometry (φ-OTDR) enables existing telecommunication fibers to function as large-scale vibration sensor arrays, offering significant potential for applications such as earthquake monitoring and pipeline safety. However, this technique generates massive volumes of originally acquired signals during continuous monitoring, posing severe challenges for transmission, storage, and real-time processing. Most existing compression methods operate on demodulated signals, which cannot fundamentally reduce the raw data volume and often fail to preserve critical phase characteristics and low-frequency details, leading to noise sensitivity and degraded reconstruction accuracy. This paper proposes an entropy coding approach based on asymmetric numeral systems (ANS) that directly compresses the originally acquired DAS signals. Without sacrificing spatial resolution, the proposed method significantly improves compression efficiency and signal fidelity. Experimental results show that it achieves a maximum compression ratio of 76.9, with a Pearson correlation coefficient (PCC) ≥ 0.95. For low-frequency signals at 0.1 Hz, it maintains a compression ratio of 73, a time-domain SNR of 76.44 dB, and a reconstruction latency of 0.187 s. In practical engineering scenarios involving four types of construction-related external disturbance events (excavator movement, drilling rig operation, manual excavation, and compactor machine operation), the method achieved an average compression ratio of 74.23 and an average compression-reconstruction time of 93 ms, while the reconstructed signals reached SNRs of 64.670 dB, 51.631 dB, 45.677 dB, and 29.280 dB, respectively. These findings demonstrate that the proposed method not only overcomes the limitations of demodulated-signal compression but also preserves both phase information and low-frequency features, offering a highly efficient and robust solution for large-scale, long-term DAS monitoring under complex real-world conditions.