## 1 Introduction  
With the rapid development of industry and agriculture, along with the increasing consumption of fossil fuels, the emissions of greenhouse gases (GHGs) such as carbon dioxide (CO\({}_{2}\)), methane (CH\({}_{4}\)), and nitrous oxide (N\({}_{2}\)O) have been steadily rising, accelerating the process of global warming and causing severe impacts on ecosystems and human health [1, 2, 3]. Among these, CO\({}_{2}\) has the highest concentration of all GHGs. In 2023, global CO\({}_{2}\) emissions increased by 0.1% compared to 2022, reaching a historic high of 35.8 Gt [4]. Against this backdrop, the importance of high-precision gas concentration inversion technology is becoming increasingly prominent. This technology plays a crucial scientific and practical role in the development of environmental protection policies, emission reduction strategies, and industrial process control. From a policy perspective, the acquisition of high-precision gas data directly supports the implementation of international climate agreements such as the Paris Agreement. In industrial applications, this technology enables the accurate monitoring of GHG emissions during production processes, optimizing production methods and reducing emissions. High-precision gas concentration inversion technology, through the use of advanced measurement instruments and data processing algorithms, can precisely measure the concentration changes of GHGs, providing critical data support for addressing climate change [5; 6; 7]. Currently, the main methods for measuring GHG concentrations include in situ and remote sensing measurements. In situ measurement instruments, such as non-dispersive infrared gas analyzers [8] and gas chromatographs [9], offer high measurement accuracy. However, in practical applications, these instruments face issues such as limited measurement range, long time consumption, and the inability to perform continuous measurements. Remote sensing measurement methods, on the other hand, enable long-distance continuous monitoring. Remote sensing technologies mainly include tunable diode laser absorption spectroscopy (TDLAS) [10], differential absorption lidar (DIAL) [11], differential optical absorption spectroscopy (DOAS) [12], passive Fourier transform infrared spectroscopy (p-FTIR) [13], and imaging Fourier transform spectroscopy (IFTS) [14; 15; 16], etc. Among these, TDLAS, DIAL, and DOAS technologies use lasers as light sources, offering the advantage of high sensitivity. However, they face challenges in terms of complex optical path deployment, a narrow spectral measurement range, and the limited number of gases that can be measured simultaneously. The p-FTIR technology scans the spectrum by controlling the movable mirror, offering high spectral resolution, but it requires the precise control of the movable mirror, and its stability and real-time performance are relatively poor. In contrast, IFTS instruments have a simpler measurement process, higher structural stability, and advantages such as a high throughput and a wide spectral measurement range, making them effective tools for measuring GHG concentrations [17; 18; 19]. However, during the detection process of the target gas with IFTS instruments, the acquired spectra are easily influenced by external environmental factors, leading to poor spectral consistency and making it difficult to achieve the high-precision concentration inversion of the target gas. In the process of infrared gas spectral quantitative analysis, machine learning methods such as partial least squares regression (PLSR) and support vector regression (SVR) are commonly used for modeling. [PERSON] et al. used a PLSR model to analyze the absorption spectra of CO-N\({}_{2}\)O mixtures and C\({}_{2}\)H\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\)O mixtures, achieving single-component concentration measurements in multi-gas mixtures, with better accuracy than standard multilinear regression [20]. [PERSON] et al.