Atmospheric carbon concentration scenario classification based on the fusion of spectral and acoustic modalities

Accurate carbon concentration monitoring is vital for climate change mitigation and environmental management. While laser-induced breakdown spectroscopy (LIBS) offers rapid, non-destructive detection, it is affected by plasma fluctuations and environmental interference. Laser-induced plasma acoustic signals (LIPA) can capture CO₂ molecular characteristics, and in this study, we propose a multimodal LIBS-WLIPA method that fuses spectral and acoustic data for stable classification of four gas scenarios. A wavelet-based WLIPA algorithm was developed to efficiently process noisy acoustic signals, reducing variables by 99% while preserving key information. Using LIBS-WLIPA, we compared six machine learning models and assessed the contributions of LIBS and WLIPA features. Results show that LIBS-WLIPA markedly improves detection accuracy, robustness, and generalization, with Logistic Regression, Random Forest, XGBoost, and CatBoost achieving 97.5% accuracy. This method offers an efficient solution for gas environment classification and expands the application potential of LIPA technology in environmental monitoring.