Application of wavelet transform and principal component analysis in mineral oil's 3D fluorescence spectra compression

摘要

Wavelet transform combined with principal component analysis (WT-PCA) is designed and applied in mineral oil''s 3D fluorescence spectra compression. At the first stage, WT is used to improve fluorescence information quality. Through lots of experiments, it is found that wavelet basis function db3 does well in eliminating spectral noise and irrelevant redundancy in 3D fluorescence spectra. The compressed scores (CS) and the recovery scores (RS) are used to evaluate noise-inhibiting effect of WT. At the second stage, PCA is used in data compression, using data compression ratio and the root mean square error (RMSE) as compression criterions. The WT-PCA method is applied in 10 kinds of spectra, CS and RS are above 90%. At the same cumulative variance (98%), compression ratio is improved by 1.25∼2.33 times compared to PCA used only. Its RMSE is less than 3.8%. The main characteristic peaks in the reconstructed and original spectra are almost the same, and their correlation coefficients are higher than 0.9, a high degree of linear correlation considering noise or redundancy eliminated. So, this method achieves a good compression effect. It is meaningful and profitable that pre-filtering irrelevant information by WT has ensured the PCA works better with correct and reliable result.