CO₂Leakage Identification Method Based on Complex Time-Frequency Spectrum of Atmospheric CO₂Variation

It is a challenging problem to monitor atmospheric CO₂leakage due to the complex variation of the atmosphere background. In this research, a new CO₂leakage identification method in the atmosphere based on the complex time-frequency spectrum of atmospheric CO₂variation was proposed. First, the complex continuous wavelet transform (CWT) was utilized to analyze the experimental data without and with CO₂leakage. It was found that CWT could provide distinguished features for atmospheric CO₂leakage 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 CO₂concentration and CO₂variation rate were compared to recognize abnormal leakage. The results indicated that the CWT spectrum of the CO₂variation rate performed better than that of concentration. Moreover, the CWT spectrum of the atmospheric CO₂variation rate with the real-valued wavelet function was also utilized to recognize CO₂leakage. 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 CO₂leakage with the complex CWT of the CO₂variation rate in the atmosphere combined with PCA processing.