Detection of organic carbon in shale by laser induced breakdown spectroscopy and Raman spectroscopy combined with partial least squares methods

Abstract

In this study, data fusion of laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy was used to characterize the total organic carbon content (TOC) of shale samples in Ordos Basin. The powder samples are pressed into pellets for LIBS and Raman analysis. Partial least squares calibration method was used for quantitative analysis. By comparing the quantitative results of the two techniques and the quantitative results of different spectral fusion strategies, it is found that the fusion strategy of LIBS and Raman spectral data can improve the prediction ability of the model. The model based on PLS latent variable fusion strategy has better fitting effect, its coefficient of determination (R2) for the calibration sets is 0.9915, the coefficient of determination for the prediction sets is 0.9574 and the root mean square error of the prediction set is 0.5580 wt%. The model has better generalization ability, and the root-mean-square-error-of-cross-validation (RMSECV) of the model is 0.5252 wt%. This study shows that data fusion of LIBS and Raman spectra can achieve rapid analysis of total organic carbon content in shale samples, and may be beneficial to carbon storage research in depleted gas shale.