Signal Activity Detection for Fiber Optic Distributed Acoustic Sensing with Adaptive-Calculated Threshold

Area of Science:

• Geophysics
• Signal Processing
• Fiber Optic Sensing

Background:

• Analyzing data streams from fiber optic distributed acoustic sensing (DAS) requires effective signal activity detection to differentiate signals from environmental noise.
• Current methods face challenges in accurately and efficiently calculating detection thresholds without impacting the measured signals, hindering DAS data analysis.

Purpose of the Study:

• To propose a novel signal activity detection method for DAS data streams.
• To address the bottleneck of inaccurate and inefficient threshold calculation in existing methods.
• To improve the accuracy and efficiency of separating signals from environmental noise in DAS measurements.

Main Methods:

• A novel signal activity detection method utilizing an adaptive-calculated threshold is introduced.
• The method analyzes the statistical commonality of time-varying random noise and the short-term energy (STE) of the real-time data stream.
• The threshold is adaptively determined by identifying the upper range of the total STE distribution of noise for ascending STE in the data stream.

Main Results:

• Experiments conducted on simulated and urban field databases demonstrated high average detection accuracies of 97.34% and 90.94%, respectively.
• The proposed method exhibited high efficiency, consuming only 0.0057 seconds for a 10-second data stream.
• The adaptive threshold effectively distinguished between signal and noise in complex environments.

Conclusions:

• The proposed adaptive-calculated threshold method offers an accurate and highly efficient solution for signal activity detection in DAS data.
• This advancement overcomes limitations of previous methods, enabling more reliable analysis of DAS measurements.
• The method shows significant potential for applications requiring robust signal-noise separation in real-time data streams.