Objective Distributed acoustic sensor (DAS) is an optical instrument that employs optical fibers as sensors to detect acoustic vibrations. DAS based on phase-sensitive optical time-domain reflectometry (phi-OTDR) have been widely applied in various fields, such as perimeter security, pipeline monitoring, geological exploration, and transportation due to their strong anti-interference ability, high sensitivity, long sensing distance, and distributed measurement capability. Currently, research on phi-OTDR systems mainly focuses on system performance optimization, noise reduction, and improvement of pattern recognition efficiency. In addition to the system structures and algorithms, the selection of key parameters in the phi-OTDR system also exerts a significant impact on the demodulation performance of the system. How to achieve the best performance of the system based on existing hardware, or how to set appropriate parameters according to specific usage scenarios, is also a problem to be addressed. We study the effects of sampling rate, number of analog to digital converter (ADC) bits, pulse width, magnification of erbium-doped fiber amplifier (EDFA), and vibration positions on the demodulation results of the phi-OTDR system, and quantitatively evaluate the influence of different influential factors on the system performance. Methods To determine the effect of relevant factors on the measurement accuracy of a DAS system based on coherent detection phi-OTDR, we build a simulation model of coherent detection phi-OTDR based on the theory of Rayleigh backscattering in optical fibers. Then vibrational excitation is applied and the corresponding Rayleigh backscattering signals are numerically generated. The vibration information in the fiber is obtained by the IQ demodulation algorithm, and the results of the simulation model are consistent with the theory of coherent detection phi-OTDR system. Based on the built corresponding DAS experimental system, the measurement results show that both the RBS signal and the waveform of the demodulated vibration signal obtained from the experiment are in good agreement with the simulation model. The signal-to-noise ratio (SNR) of the vibration detection evaluation system is defined to evaluate the accuracy of vibration detection, and the distortion evaluation system is defined to assess the discrepancy between the demodulated vibration and the actual vibration. Based on the built phi-OTDR system model, the influence of various factors on the demodulation results is investigated by modifying the parameters of the simulation model, including the sampling rate, number of ADC bits, pulse width, magnification of EDFA, and vibration position. The effect on system performance is quantitatively analyzed with different changes in the influencing factors. Results and Discussions By selecting different influencing factors and comparing their effects on demodulation performance, the results are shown in Fig. 10, Table 1, Fig. 13, Fig. 16, and Fig. 19. The relationship between SNR of vibration detection, distortion, and each influential factor is fitted, with the results shown in Fig. 14, Table 2, Table 3, Fig. 17, Table 4, Table 5, Fig. 20, Table 6, and Table 7. Additionally, typical demodulated vibration waveforms under different influencing factor parameters are demonstrated, and the results are shown in Fig. 11, Fig. 12, Fig. 15, Fig. 18, and Fig. 21. The results reveal that the sampling rate can be less than two times of the acoustic optical modulator (AOM) frequency, and the SNR of vibration detection and distortion does not increase or decrease strictly monotonically with the increasing sampling rate in a wide range. If the ADC quantization bits vary from 8 to 16, no big influence is exerted on the demodulation results. If the optical pulse width is less than 200 ns, the SNR of vibration detection significantly increases with the rising pulse width and decreases double-exponentially with the pulse width. Under noisy conditions, the distortion increases double-exponentially with the pulse width, and the SNR of vibration detection increases in a power rule with the magnification of EDFA. The distortion decreases double-exponentially with the magnification of EDFA under low noisy conditions and the distortion decreases in a power rule with the magnification of EDFA under high noisy conditions. Under noisy conditions, the SNR of vibration detection decreases linearly with the distance between the vibration point and the input end, and the distortion increases in a power rule. The study provides a reference for the parameter selection of DAS systems. Conclusions We develop a model of the coherent detection phi-OTDR system used for DAS based on the theory of Rayleigh backscattering. The IQ demodulation algorithm is employed to demodulate the vibration information in the fiber, and the reliability of the model is verified by measured signals. The influence of key measurement parameters on the vibration demodulation performance is studied, and the demodulation ability of the phi-OTDR system is compared under different sampling rates, numbers of ADC bits, pulse width, magnification of EDFA, and vibration positions. The quantitative effects of relevant factors on the SNR of vibration detection and distortion have been provided, which can serve as a reference for selecting parameters of the DAS systems.
