物探与化探2011，Vol.35Issue(5)：634-638,642,6.
支持向量机与微电阻率成像测井识别火山岩岩性
THE APPLICATION OF SVM AND FMI TO THE LITHOLOGIC IDENTIFICATION OF VOLCANIC ROCKS
张莹 1潘保芝2
作者信息
- 1. 广东海洋大学信息学院,广东湛江524088
- 2. 吉林大学地球探测科学与技术学院,吉林长春130026
- 折叠
摘要
Abstract
From the viewpoint of chemical composition categorization and structure classification of rocks, an effective method was proposed to identify the lithology of volcanic rocks by using logging data. On the one hand, the conventional logging data could be obtained by core wafer identification. Thus, after processing the data with Support Vector Machines (SVM) method of statistical theory, we could get the lithologic type of the volcanic rocks, which are classified according to the chemical composition of rocks. On the other hand, the volcanic rocks can be classified as volcanic lava, pyroclastic lava and pyroclastic rock according to the rock structure. Typical formation micro-resistivity imaging logging (FMI) image mode can be concluded by establishing the corresponding relationship between FMI images and lithology of volcanic rocks with different structures. As a result, the lithologic type of the volcanic rock classified by rock structure can be determined. Finally, by combining these two kinds of lithology, the ultimate rock lithology can be determined, too. In this paper, the authors presented a novel method to identify the lithology of volcanic rocks by combining SVM processed logging data and FMI image mode.关键词
支持向量机/地层微电阻率成像测井/火山岩岩性识别Key words
Support Vector Machines/formation micro-resistivity imaging logging/lithologic identification of volcanic rock分类
天文与地球科学引用本文复制引用
张莹,潘保芝..支持向量机与微电阻率成像测井识别火山岩岩性[J].物探与化探,2011,35(5):634-638,642,6.
