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.

中文翻译：

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用于气体定量分析的失真双梳吸收光谱的支持深度学习的高保真吸收光谱

双梳吸收光谱由于其光谱覆盖范围广、分辨率高、采集速度快和频率精度高等固有优势，一直是激光光谱学中一项有前途的技术。然而，两个主要挑战，包括标准具效应和复杂的基线提取，仍然严重阻碍其在恢复吸收光谱和随后的定量分析中的实施。在本文中，我们提出了一种支持深度学习的处理框架，其中包含标准具去除和基线提取模块，以从扭曲的双梳吸收光谱中获取吸收光谱。标准具去除模块采用典型的U-net模型，基线提取模块由具有物理约束的修正U-net模型和用作细化的自适应迭代重加权惩罚最小二乘法组成。训练数据集结合了实验基线和不同浓度的模拟气体吸收，充分利用了 HITRAN 数据库中气体吸收特征的先验信息。在模拟和实验测试中，CO2吸收光谱覆盖25 cm–1与HITRAN数据库显示出高度的一致性，其中平均绝对误差小于最大吸光度值的1%，并且检索浓度的相对误差低于2%，优于传统方法，表明我们的数据处理框架的潜在实用性。希望通过更大的网络量和适当的数据集，该处理框架可以扩展到大气测量和工业监测等更全面的应用中的精确量化分析。