Estimation of the dolomite content of carbonate rock outcrops based on spectral knowledge and machine learning - Wei, Wei; Shao, Yanlin; Hu, Zhonggui; Wang, Qing; Deng, Fan; Huang, Yu; Zhao, Kunpeng (Frontiers in Earth Science, 2024)
Accurately predicting of dolomite content in carbonate rocks is crucial for optimizing oil and gas exploration and production strategies. Hyperspectral data surpassing traditional mineral content prediction methods in efficiency, precision, cost-effectiveness, and non-destructiveness, is extensively applied in mineral recognition and content prediction. However, feature selection and extraction remain challenging for hyperspectral data. This study proposes a prediction method for dolomite content based on field hyperspectral data. This method comprises three key stages: data preprocessing, feature selection, and model construction. Outlier analysis and continuum transformations are included in the preprocessing stage. In the feature selection phase, an advanced hybrid approach is adopted, integrating spectral knowledge with machine learning to facilitate efficient and precise feature selection. Initially, the spectral characteristics of dolomite minerals were utilized for preliminary feature selection. Subsequently, further feature selection was conducted by the Random Forest (RF) feature importance. During the model construction phase, the Support Vector Regression (SVR) algorithm was employed to develop the predictive model. Furthermore, the study involves a comparison of eight input variables and four machine learning algorithms. Experimental results demonstrate that our method achieves the highest accuracy, with a test R² value of 0.91 and a root-mean-square error (RMSE) of only 0.122. This method is practical and efficient, providing precise quantitative data for field geologists in identifying outcrop mineral distribution and understanding reservoir characteristics, offering significant practical value for geological surveys and mineral exploration.