Simulation Analysis of Unmanned Aerial Vehicle-Based Laser Remote Sensing for Methane Point Source Traceability and Leakage Quantification

Current methods for the high-precision real-time monitoring and parameter inversion of industrial methane point source leakage are insufficient. This research introduces a novel laser-based methane leakage monitoring approach for deployment on an unmanned aerial vehicle platform. An enhanced two-dimensional integral Gaussian diffusion model paired with a point sampling technique is employed to simultaneously determine the leakage rate and source location, integrating a genetic algorithm and an interior point penalty function algorithm for optimization. Simulations incorporating observational error sources are performed to quantitatively assess the accuracy of leakage parameter inversion under diverse errors, demonstrating the scheme’s viability. The accuracy of leakage parameter inversion achieved by the algorithm across various point sampling methods, gas plume characteristics, and wind speeds was examined, validating the assessment under multivariable influences in real observations. The proposed methodology was compared with two other leakage inversion optimization techniques, demonstrating its efficiency in addressing wind speed and directional effects. This study offers a practical method with significant implications for monitoring and quantifying industrial methane point source leakages.