Abstract
Monitoring methane leakage and emission is essential for mitigating global climate change and ensuring industrial safety. Single-photon LiDAR systems provide exceptional sensitivity to weak optical returns through single-photon detector. When combined with a multi-pulse modulation scheme, the system enables simultaneous extraction of ambient-background depth and methane-absorption information, leading to improved imaging performance under weak-return conditions. However, such systems suffer from two primary distortion mechanisms: waveform distortion caused by detector dead time under high photon count-rate conditions and spectral distortion induced by rapid wavelength tuning of the laser source. To address these challenges, this work proposes a concentration–response function calibration method and a multi-parameter inversion approach that incorporates both calibration and correction. The proposed framework enables accurate retrieval of methane column concentration and depth information across a wide dynamic range of photon count-rates, significantly improving system robustness and reliability. Furthermore, a count-rate model for multi-pulse single-photon LiDAR in gas-cloud imaging is developed to optimize the matching between the detector dead time and the laser repetition rate. Experimental results demonstrate a ranging precision of 13.3 mm and measurement accuracies of 1300 ppm·m and 80 ppm·m for integration times of 10 ms and 10 s, respectively. Methane imaging at a distance of 22 m using a 368.4 µW laser and a resolution of 100 × 100 pixels, further verifies the system's capability, achieving a background-limited noise floor of 2012 ppm·m and reliable detection of methane.
| Original language | English |
|---|---|
| Article number | 120919 |
| Journal | Measurement: Journal of the International Measurement Confederation |
| Volume | 271 |
| DOIs | |
| State | Published - 28 Apr 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
Keywords
- Dead time correction
- Gas imaging
- IRF calibration
- LiDAR
- Single-photon detection
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