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    This study optimized distributed acoustic sensing (DAS) using graphics processing units (GPUs) for faster data processing. GPU parallel computing achieved a maximum acceleration ratio of 86.01 for cross-correlation calculations.

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    Area of Science:

    • Geophysics
    • Signal Processing
    • High-Performance Computing

    Background:

    • Distributed acoustic sensing (DAS) is crucial for monitoring.
    • Traditional DAS processing can be computationally intensive.
    • Optimizing processing speed is essential for real-time applications.

    Purpose of the Study:

    • To accelerate the processing of distributed acoustic sensing data.
    • To investigate the efficiency of graphics processing unit (GPU) parallel computing for DAS.
    • To analyze the impact of GPU parameters on processing performance.

    Main Methods:

    • Implemented moving window cross-correlation calculations on a GPU.
    • Utilized parallel computing capabilities of GPUs.
    • Analyzed the effect of thread number per block on multiprocessor utilization.
    • Evaluated acceleration across different computational scales.

    Main Results:

    • Achieved significant speedup in cross-correlation calculations using GPU parallelization.
    • Identified optimal GPU thread configurations for enhanced multiprocessor utilization.
    • Demonstrated a maximum acceleration ratio of 86.01.
    • Confirmed the scalability of the GPU-based approach.

    Conclusions:

    • GPU-based parallel processing offers substantial acceleration for DAS.
    • Optimizing GPU thread configurations maximizes computational efficiency.
    • This method enables faster, more efficient analysis of acoustic sensing data.