Application of dimensionality reduction technique to improve geophysical log data classification performance in crystalline rocks

• Two dimentionally reduction methods: PCA, LDA are presented.
• Three classifiers is addressed: SVM, BPNN and RBFN.
• Combining dimensionality reduction methods and classifiers is discussed.
• Satisfactory results were obtained through PCA and LDA.
• BPNN was found more accurate than SVM and RBFN.

In this study, dimensionality reduction technique to improve geophysical log data classification performance in crystalline rocks is presented. In fact, in complex geological situations such as the study area in context, more complex nonlinear functional behaviors exist for well log classification purpose; thus posing challenges in accurate identification of log curves for this purpose. Dimensionality reduction (DR) using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used here to reduce the dimensionality of the original log set of Chinese Continental Scientific Drilling Main Hole to a convenient size, and then feed the reduced-log set into the classifiers. Three classifiers were addressed, namely, Support vector Machines, Feed forward Back Propagation and Radial Basis Function Neural Networks in the classification of metamorphic rocks. The strategy of combining dimensionality reduction methods and classifiers was demonstrated and discussed. The results showed that the reduced log sets found from DR can separate the metamorphic rocks types better or almost as well as the original log set. Therefore LDA and PCA can be suitable to be performed before geophysical well log data classification in the context of crystalline rocks.