CO2 Leakage Identification Method Based on Complex Time–Frequency Spectrum of Atmospheric CO2 Variation

Abstract

It is a challenging problem to monitor atmospheric CO₂ leakage due to the complex variation of the atmosphere background. In this research, a new CO₂ leakage identification method in the atmosphere based on the complex time–frequency spectrum of atmospheric CO₂ variation was proposed. First, the complex continuous wavelet transform (CWT) was utilized to analyze the experimental data without and with CO₂ leakage. It was found that CWT could provide distinguished features for atmospheric CO₂ leakage by calculating the time–frequency spectrum and modulus of CWT for the cases with a leakage rate from 5 to 25 m³/h. Further, the atmospheric CO₂ concentration and CO₂ variation rate were compared to recognize abnormal leakage. The results indicated that the CWT spectrum of the CO₂ variation rate performed better than that of concentration. Moreover, the CWT spectrum of the atmospheric CO₂ variation rate with the real-valued wavelet function was also utilized to recognize CO₂ leakage. The tests showed that the CWT spectrum with the complex Morlet wavelet demonstrated a more obvious and wider hot spot than that with the real-valued Morlet wavelet. In addition, a pretreatment method with principal component analysis (PCA) was applied to extract the features of original monitoring signals. It was proved that more obvious abnormal signals in the time–frequency spectrum and modulus variation PCA–CWT method could be captured than that from the original CWT analysis, even for a small leakage. Therefore, it is a feasible method to monitor and recognize atmospheric CO₂ leakage with the complex CWT of the CO₂ variation rate in the atmosphere combined with PCA processing.