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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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    Area of Science:

    • Geophysics and Sensor Technology
    • Signal Processing and Data Compression

    Background:

    • Distributed Acoustic Sensing (DAS) using phase-sensitive optical time-domain reflectometry (φ-OTDR) offers large-scale vibration monitoring capabilities.
    • Massive data volumes from continuous DAS monitoring present significant transmission, storage, and processing challenges.
    • Existing compression methods for DAS data often degrade signal fidelity and fail to preserve critical phase and low-frequency information.

    Purpose of the Study:

    • To develop a novel compression method for raw DAS signals that overcomes the limitations of existing techniques.
    • To improve compression efficiency and signal fidelity for large-scale DAS monitoring.
    • To ensure preservation of phase characteristics and low-frequency details in compressed DAS data.

    Main Methods:

    • Implementation of an entropy coding approach utilizing asymmetric numeral systems (ANS).
    • Direct compression of originally acquired DAS signals, bypassing demodulation.
    • Validation through experimental results under various conditions, including low-frequency signals and practical engineering scenarios.

    Main Results:

    • Achieved a maximum compression ratio of 76.9 with a Pearson Correlation Coefficient (PCC) ≥ 0.95.
    • Maintained a compression ratio of 73 for 0.1 Hz signals with a time-domain SNR of 76.44 dB and low reconstruction latency (0.187 s).
    • Demonstrated high average compression ratios (up to 74.23) and signal-to-noise ratios (SNRs) for various construction disturbance events.

    Conclusions:

    • The proposed ANS-based entropy coding method effectively compresses raw DAS signals without sacrificing spatial resolution or critical signal characteristics.
    • This approach overcomes the limitations of demodulated-signal compression, preserving phase information and low-frequency features.
    • The method provides a highly efficient and robust solution for large-scale, long-term DAS monitoring in complex environments.