"Adaptive Signal-to-Noise Ratio Guided Sparse Representation for Distributed Acoustic Sensing Signal Enhancement Simulations"

"Distributed Acoustic Sensing (DAS) has emerged as a paradigm-shifting remote sensing modality for high-resolution geophysical monitoring, offering unprecedented spatiotemporal density for subsurface imaging and hazard detection. However, DAS remote sensing data are inherently contaminated by complex spatiotemporal noise and coherent fading, which severely degrade the Signal-to-Noise Ratio (SNR) and compromise quantitative interpretation. Crucially, traditional denoising paradigms often inadvertently suppress effective geophysical signals during noise removal, causing ``signal leakage'' and amplitude distortion that hinder accurate inversion. To address this critical challenge in quantitative remote sensing, we propose the Adaptive Signal-to-Noise Ratio-Guided Sparse Representation (ASRE) algorithm. Unlike conventional approaches that focus solely on noise suppression, ASRE introduces a fidelity-restoration mechanism designed to recover effective signal components erroneously discarded into the noise profile. The algorithm integrates Sequential Generalized K-SVD for adaptive dictionary learning, Stein\u2019s Unbiased Risk Estimate (SURE) for dynamic threshold optimization, and reweighted sparse enhancement to deeply exploit intrinsic signal-noise disparities. Simulation results on 50 Hz sine waves and 25 Hz Ricker wavelets demonstrate that ASRE significantly boosts the performance of eight classical denoisers, increasing SNR by up to 4.54 dB and Peak Signal-to-Noise Ratio (PSNR) by 3.48 dB, effectively preserving waveform fidelity. Comprehensive field validations on helical cables with varying winding coefficients further confirm its engineering efficacy in near-surface remote sensing. Under hammer impact and sinusoidal excitation, ASRE improved SNR by 4\u20136 dB and PSNR by 2\u20134 dB, achieving optimal performance with a winding coefficient of 6. As a robust, plug-and-play post-processing module, ASRE offers a promising solution for high-fidelity DAS signal reconstruction, facilitating more accurate quantitative geophysical analysis."