Abstract

Distributed acoustic sensing (DAS) is an emerging technology that has gained significance in various domains, including geophysics and security, due to its unique characteristics of long-distance capability, massive sensing points, and large bandwidth. DAS based on coherent detection has been heavily studied in recent years, due to its unique advantages over direct-detection scheme. However, coherent detection DAS usually generates a significant volume of data, since probe signals with wider bandwidth generally have better performance, which presents substantial challenges in data acquisition, storage, transmission and processing, especially for real-time applications. The study of quantization algorithm could be an effective means of addressing these challenges. It should be noted that, due to the impact of quantization noise, signals after quantization exhibit different levels of distortion depending on the quantization bits, resulting in the deterioration of noise floor. This article introduces, to the best of our knowledge, the theory elucidating how quantization bits affect the performance of coherent detection DAS for the first time. It derives a specific formula suitable for quantitative analysis, and the formula's outcomes align with both simulation and experimental results. In addition, this article shows a 50 km long-distance experiment using nonlinear frequency-modulated pulses as the probing signal, further extending the practical application scope of the theory. This article serves as a comprehensive guide for reducing hardware requirements, accelerating data processing to satisfy versatile and demanding application scenarios, and fostering interdisciplinary research.