Abstract: Because of the complexity of the lithologic characteristics and structures, the Mesozoic igneous rocks of the Bohai Bay area is difficult to identify. Based on the abundant data of cores, lateral cores and rock sections, and rock-electricity relationship, five logging curves of GR,CNL,DEN,AC, and R_d were selected. Using the principal component analysis, five comprehensive variables including F₁-F₅ were constructed. F₁ and F₂ were used to replace the original five variables efficiently, as their proportion account for 81.4%. According to the principal component information, combining with the Bayes discriminant method, the model for quantitative interpretation of different lithology was established, and nine types of volcanic rocks in the study area were identified. A back evaluation of the core data was carried out. The results show that this method can identify the rock composition and the rock structure better than cross-plotting. This method is an efficient one for volcanic lithology identification in the areas with less data of FMI and ECS.

Key words: volcanic rocks, principal component analysis, Bayes discriminant, quantitative identification, Bohai Bay area