A VMD-LMS filtering approach for DAS noise suppression

Abstract Fiber-optic distributed acoustic sensing (DAS) has rapidly emerged as a groundbreaking technology, offering extensive coverage and exceptional sensitivity. It has wide applications in sectors such as oil/gas pipeline monitoring, perimeter security, and geological exploration. However, the performance of DAS systems is often limited by signal-to-noise ratio (SNR) degradation caused primarily by ultra-long-distance monitoring, optical transmission loss, and harsh environmental conditions. In this study, we propose a concept for enhancing SNR in DAS systems using a variational mode decomposition-least mean squares (VMD-LMS) algorithm that integrates VMD with Autostep-LMS filtering to improve SNR through digital signal processing. We demonstrate the denoising performance of the VMD-LMS adaptive filtering algorithm through simulations and experiments. The results unequivocally demonstrate that the algorithm can enhance spectral SNR by up to 48.8 dB for DAS signals. Moreover, the proposed VMD-LMS algorithm achieves an effective noise floor of −65.8 dB rad 2 Hz ‒1 over a sensing range spanning approximately 97 km. This SNR enhancement technique demonstrates significant potential for widespread application in real-time monitoring of ultra-long-distance DAS systems. By employing VMD-LMS post-processing, it achieves performance improvements without increasing system complexity.