A methane telemetry system for 1653 nm DFB laser based on TDLAS‐WMS technology is developed in this article. The focus tunable lens is used as the collimating system of the telemetry device to solve the problem that the telemetry device cannot be dynamically adjusted under different detection environments. Experimental results show that the root mean square error was 7.6205 and the theoretical limit of detection (LoD) was 1.473 parts per million (ppm) while the optimal integration time reaches 30 s. The near‐infrared CH4 telemetry system is suitable for different detection environments of natural gas leakage and has good detection performance and stable and reliable operation.

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A distribution feedback laser sensor for high precision and high sensitivity detection of the hydrogen sulfide in associated gas from oil fields is developed in this paper. Tunable diode laser absorption spectroscopy and wavelength modulation spectroscopy and were utilized for the H 2 S concentration detection in the oilfield. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is used to remove the noise from the spectral signal. Particle swarm optimization-least squares support vector machine (PSO-LSSVM) model is applied for concentration prediction. The root mean square error of long-term stability is 0.323. Allan-Werle deviation analysis shows that the theoretical limit of detection is 71 parts per billion while the optimal integration time reaches 96 s. The sensor is of great significance for realtime detection of hydrogen sulfide concentration in associated gas in oil fields.

In this article, a field deployable sensor was developed using a self-developed 4.58-µm continuous wave quantum cascade laser (CW-QCL) for the simultaneous detection of carbon monoxide (CO) and nitrous oxide (N2O), both of which have strong fundamental absorption bands in this waveband. The sensor is based on tunable diode laser absorption spectroscopy (TDLAS) technology, which combined a multi-pass gas cell (MPGC) with a 41 m optical path length to achieve high-precision detection. Meanwhile, the particle swarm optimization-kernel extreme learning machine (PSO-KELM) algorithm was applied for CO and N2O concentration prediction. In addition, the self-designed board-level QCL driver circuit and harmonic signal demodulation circuit reduce the sensor cost and size. A series of validation experiments were conducted to verify the sensor performance, and experiments showed that the concentration prediction results of the PSO-KELM algorithm are better than those of the commonly used back propagation (BP) neural networks and partial least regression (PLS), with the smallest root mean square error (RMSE) and linear correlation coefficient closest to 1, which improves the detection precision of the sensor. The limit of detection (LoD) was assessed to be 0.25 parts per billion (ppb) for CO and 0.27 ppb for N2O at the averaging time of 24 and 38 s. Field deployment of the sensor was reported for simultaneous detection of CO and N2O in the air.

For the first time, a diode-pumped actively Q-switched Nd:YVO4/RbTiOPO4 (RTP) intracavity Raman laser at 1.49 µm was demonstrated to the best of our knowledge. Experimentally, a dual-end diffusion-bonded YVO4–Nd:YVO4–YVO4 crystal was employed as the laser medium to generate 1.34 µm laser radiation, and an RTP crystal as the Raman medium to enable the frequency conversion, by which radiation at 1.49 µm was achieved successfully. With an incident pump power of 10.4 W, an average output power of 502 mW was obtained at a pulse repetition rate of 15 kHz, corresponding to a conversion efficiency of 4.8%.