Li Jiarui, Zhang Hongyuan, Lu Minjian, Wei Haoyun, Li Yan
Opt Express. 2022 Aug 1;30(16):29598-29610. doi: 10.1364/OE.465106.
Impulsive stimulated Brillouin spectroscopy (ISBS) plays a critical role in investigating mechanical properties thanks to its fast measurement rate. However, traditional Fourier transform-based data processing cannot decipher measured data sensitively because of its incompetence in dealing with low signal-to-noise ratio (SNR) signals caused by a short exposure time and weak signals in a multi-peak spectrum. Here, we propose an adaptive noise-suppression Matrix Pencil method for heterodyne ISBS as an alternative spectral analysis technique, speeding up the measurement regardless of the low SNR and enhancing the sensitivity of multi-component viscoelastic identification. The algorithm maintains accuracy of 0.005% for methanol sound speed even when the SNR drops 33 dB and the exposure time is reduced to 0.4 ms. Moreover, it proves to extract a weak component that accounts for 6% from a polymer mixture, which is inaccessible for the traditional method. With its outstanding ability to sensitively decipher weak signals without spectral a priori information and regardless of low SNRs or concentrations, this method offers a fresh perspective for ISBS on fast viscoelasticity measurements and multi-component identifications.
由于脉冲受激布里渊光谱（ISBS）测量速度快，在研究材料力学性能方面发挥着关键作用。然而，传统的基于傅里叶变换的数据处理方法无法灵敏地解析测量数据，因为它无法处理由短曝光时间导致的低信噪比（SNR）信号以及多峰光谱中的微弱信号。在此，我们提出一种用于外差式ISBS的自适应噪声抑制矩阵束方法，作为一种替代的光谱分析技术，该方法能够在不考虑低信噪比的情况下加快测量速度，并提高多组分粘弹性识别的灵敏度。即使信噪比下降33 dB且曝光时间缩短至0.4 ms，该算法对甲醇声速的测量精度仍能保持在0.005%。此外，它能够从聚合物混合物中提取出占比6%的微弱成分，而传统方法无法做到这一点。该方法具有出色的能力，无需光谱先验信息，且无论信噪比低或浓度低，都能灵敏地解析微弱信号，为ISBS在快速粘弹性测量和多组分识别方面提供了全新的视角。
