Deep-Learning-Enabled High-Fidelity Absorbance Spectra from Distorted Dual-Comb Absorption Spectroscopy for Gas Quantification Analysis

Abstract

Dual-comb absorption spectroscopy has been a promising technique in laser spectroscopy due to its intrinsic advantages over broad spectral coverage, high resolution, high acquisition speed, and frequency accuracy. However, two primary challenges, including etalon effects and complex baseline extraction, still severely hinder its implementation in recovering absorbance spectra and subsequent quantification analysis. In this paper, we propose a deep learning enabled processing framework containing etalon removal and baseline extraction modules to obtain absorbance spectra from distorted dual-comb absorption spectroscopy. The etalon removal module utilizes a typical U-net model, and the baseline extraction module consists of a modified U-net model with physical constraint and an adaptive iteratively reweighted penalized least squares method serving as refinement. The training datasets combine experimental baselines and simulated gas absorption with different concentrations, fully exploiting prior information on gas absorption features from the HITRAN database. In the simulated and experimental test, the CO2 absorbance spectrum covering 25 cm–1 shows high consistency with the HITRAN database, of which the mean absolute error is less than 1% of the maximum absorbance value, and the retrieved concentration has a relative error under 2%, outperforming traditional approaches and indicating the potential practicality of our data processing framework. Hopefully, with a larger network volume and proper datasets, this processing framework can be extended to precise quantification analysis in more comprehensive applications such as atmospheric measurement and industrial monitoring.