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    Area of Science:

    • Geophysics and seismology
    • Signal processing
    • Machine learning

    Background:

    • Fiber-optic distributed acoustic sensing (DAS) offers dense, meter-scale seismic measurements.
    • DAS technology enables monitoring in challenging environments like urban, glaciated, and submarine settings.
    • Traditional noise-handling methods are insufficient for DAS data from new environments.

    Purpose of the Study:

    • To develop a novel denoising technique for DAS data.
    • To address challenges posed by spatially incoherent noise in DAS recordings.
    • To improve the applicability of DAS in seismological analysis.

    Main Methods:

    • A self-supervised Deep Learning approach is proposed.
    • The method leverages the spatial density inherent in DAS measurements.
    • No assumptions are made about noise characteristics beyond spatio-temporal incoherence.

    Main Results:

    • The Deep Learning approach successfully removes spatially incoherent noise.
    • Demonstrated effectiveness on both synthetic and real-world DAS data.
    • Performance is robust even when signals are significantly below the noise level.

    Conclusions:

    • The proposed method enhances DAS data quality for seismological applications.
    • It facilitates the incorporation of DAS into conventional data processing workflows.
    • This technique opens new possibilities for analyzing seismic signals in complex environments.