基于主成分分析法与Bayes判别法组合应用的火山岩岩性定量识别:以渤海海域中生界为例

渤海海域中生界火山岩岩石类型复杂多样,且同一岩性受岩石成分、结构差异的影响,因此岩石物理响应特征存在较大差异,为岩性识别增加了难度。本文通过对研究区大量取心资料、壁心资料以及薄片资料的岩电分析,优选出对岩性响应敏感的自然伽马(GR)、补偿中子(CNL)、密度(DEN)、声波时差(AC)以及原状地层电阻率(Rd)等5条曲线。基于主成分分析法,构建了F_1—F_5共5个综合变量,其中F_1和F_2方差贡献率占总贡献率的81.4%,可作为两个主成分有效地反映5个变量的信息。针对主成分信息,利用Bayes判别法,构建了不同岩性的定量解释模型,对研究区9种火山岩进行了岩性识别。对取心井段的回判结果显示,基于主成分分析与Bayes判别的联合识别方法较常规交会图法在岩石成分及结构的识别精度方面均有较大程度的提高。该方法的提出对研究区成像测井、元素测井资料缺少井段的火山岩岩性识别具有重要借鉴作用。

Quantitative Identification of Volcanic Lithology Based on Comprehensive Principal Component Analysis and Bayes Discriminant Method: A Case Study of Mesozoic in Bohai Bay

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.