Artificial intelligence (AI) plays a critical role in signal recognition of distributed sensor systems (DSS), boosting its applications in multiple monitoring fields. Due to the domain differences between massive sensors in signal acquisition conditions, such as manufacturing process, deployment, and environments, current AI schemes for signal recognition of DSS frequently encounter poor generalization performance. In this paper, an adaptive decentralized artificial intelligence (ADAI) method for signal recognition of DSS is proposed, to improve the entire generalization performance. By fine-tuning pre-trained model with the unlabeled data in each domain, the ADAI scheme can train a series of adaptive AI models for all target domains, significantly reducing the false alarm rate (FAR) and missing alarm rate (MAR) induced by domain differences. The field tests about intrusion signal recognition with distributed optical fiber sensors system demonstrate the efficacy of the ADAI scheme, showcasing a FAR of merely 4.3% and 0%, along with a MAR of only 1.4% and 2.7% within two specific target domains. The ADAI scheme is expected to offer a practical paradigm for signal recognition of DSS in multiple application fields.

Infrasound detection is important in natural disasters monitoring, military defense, underwater acoustic detection, and other domains. Fiber-optic Fabry–Perot (FP) acoustic sensors have the advantages of small structure size, long-distance detection, immunity to electromagnetic interference, and so on. The size of an FP sensor depends on the transducer diaphragm size and the back cavity volume. However, a small transducer diaphragm size means a low sensitivity. Moreover, a small back cavity volume will increase the low cut-off frequency of the sensor. Hence, it is difficult for fiber-optic FP infrasound sensors to simultaneously achieve miniaturization, high sensitivity, and extremely low detectable frequency. In this work, we proposed and demonstrated a miniaturized and highly sensitive fiber-optic FP sensor for mHz infrasound detection by exploiting a Cr-Ag-Au composite acoustic-optic transducer diaphragm and a MEMS technique-based spiral micro-flow hole. The use of the spiral micro-flow hole as the connecting hole greatly reduced the volume of the sensor and decreased the low-frequency limit, while the back cavity volume was not increased. Combined with the Cr-Ag-Au composite diaphragm, a detection sensitivity of -123.19dBre1rad/μPa at 5 Hz and a minimum detectable pressure (MDP) of 1.2mPa/Hz1/2 at 5 Hz were achieved. The low detectable frequency can reach 0.01 Hz and the flat response range was 0.01–2500 Hz with a sensitivity fluctuation of ±1.5dB. Moreover, the size of the designed sensor was only 12mm×Φ12.7mm. These excellent characteristics make the sensor have great practical application prospects.

Optical fiber distributed acoustic sensing (DAS) based on phase-sensitive optical time domain reflectometry (φ-OTDR) is in great demand in many long-distance application fields, such as railway and pipeline safety monitoring. However, the DAS measurement distance is limited by the transmission loss of optical fiber and ultralow backscattering power. In this paper, a DAS system based on multispan relay amplification is proposed, where the bidirectional erbium-doped fiber amplifier (EDFA) is designed as a relay module to amplify both the probe light and the backscattering light. In the theoretical noise model, the parameters of our system are carefully analyzed and optimized for a longer sensing distance, including the extinction ratio (ER), span number, span length, and gain of erbium-doped fiber amplifiers. The numerical simulation shows that a bidirectional EDFA relay DAS system can detect signals over 2500 km, as long as the span number is set to be more than 100. To verify the effectiveness of the scheme, a six-span coherent-detection-based DAS system with an optimal design was established, where the cascaded acoustic-optic modulators (AOMs) were used for a high ER of 104 dB. The results demonstrate that the signal at the far end of 300.2 km can be detected and recovered, achieving a high signal-to-noise ratio of 59.6 dB and a high strain resolution of 51.8pε/Hz at 50 Hz with a 20 m spatial resolution. This is, to the best of our knowledge, a superior DAS sensing distance with such a high strain resolution.

Optical fiber sensor network has attracted considerable research interests for geoscience applications. However, the sensor capacity and ultra-low frequency noise limits the sensing performance for geoscience data acquisition. To achieve a high-resolution and lager sensing capacity, a strain sensor network is proposed based on phase-sensitive optical time domain reflectometer (φ-OTDR) technology and special packaged fiber with scatter enhanced points (SEPs) array. Specifically, an extra identical fiber with SEPs array which is free of strain is used as the reference fiber, for compensating the ultra-low frequency noise in the φ-OTDR system induced by laser source frequency shift and environment temperature change. Moreover, a hysteresis operator based least square support vector machine (LS-SVM) model is introduced to reduce the compensation residual error generated from the thermal hysteresis nonlinearity between the sensing fiber and reference fiber. In the experiment, the strain sensor network possesses a sensing capacity with 55 sensor elements. The phase bias drift with frequency below 0.1 Hz is effectively compensated by LS-SVM based hysteresis model, and the signal to noise ratio (SNR) of a strain vibration at 0.01 Hz greatly increases by 24 dB compared to that of the sensing fiber for direct compensation. The proposed strain sensor network proves a high dynamic resolution of 10.5 pε·Hz^-1/2 above 10 Hz, and ultra-low frequency sensing resolution of 166 pε at 0.001 Hz. It is the first reported a large sensing capacity strain sensor network with sub-nε sensing resolution in mHz frequency range, to the best of our knowledge.