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

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 m(3)/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.