LAUSR

Natural gas leakage occurs frequently due to aging pipes and other factors, but is challenging to detect. In this article, a new, robust method for nondestructive natural gas microleakage detection was proposed. It combines a crop growth model with a convolutional neural network (CNN) approach to quantitatively detect underground natural gas leakage using unmanned aerial vehicle (UAV) hyperspectral imagery. The environmental stress on wheat was used as an indicator to reflect the intensity of natural gas leakage. First, a crop growth model (simple algorithm for yield, SAFY) was used to simulate the growth of wheat, and the environmental stress factor in the model was used to construct the natural gas stress index (K_gs). Subsequently, CNN models were used to estimate the K_gs value with a hyperspectral image as the input. Finally, the CNN estimated K_gs was used to detect the natural gas leakage in the study area. Results showed that the SAFY model K_gs value could effectively identify natural gas leakage, with statistically significant differences (p-value < 0.05) among three leakage levels. Furthermore, compared to a single spectral index, K_gs had superior robustness throughout the wheat growth period. The CNN-1D model with InceptionV2 architecture exhibited the best accuracy in estimating K_gs, with a robust nRMSE of 6.9%. Overall, the combined CNN and SAFY models could accurately detect natural gas leakage, and this approach is more robust than traditional spectral index-based methods. This article provides a new method for nondestructive detecting of natural gas microleakage.