An Improved EMD Denoising Framework for Underwater Distributed Acoustic Sensing Signals by Exploring Permutation Entropy

This paper presents a novel denoising framework designed to address the issue of unknown noise interference in Distributed Acoustic fiber Sensing (DAS) signals. The proposed method combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Permutation Entropy (PE) with Wavelet Threshold Denoising (WTD). The CEEMDAN algorithm is utilized to decompose the DAS signal into multiple Intrinsic Mode Functions (IMFs), followed by the calculation of the PE value for each IMF. IMFs with a PE value exceeding 0.5 undergo denoising through wavelet transform, and are subsequently reconstructed. Our approach exhibits strong performance in analyzing DAS signals with seismic events, highlighting its effectiveness and reliability through its high signal-to-noise ratio.