| 講演要旨(和文) | | 鉱物の組成やテキスチャは,岩石の形成過程を理解する上で定量的な情報を提供する.そのため鉱物をマッピングする手法の開発は,岩石学的に重要である.ここではファン・デ・フーカプレートの玄武岩サンプルの鉱物をマッピングするため,まず薄片上の数種類の元素マップを電子線マイクロプローブアナライザー(EPMA)により推定した.次にEPMAで得られた元素マップから,各鉱物が持つ元素の割合を考慮することで鉱物マッピングを行なうことを試みた.しかし玄武岩のように多数の鉱物が混在する岩石では,数種類の元素を同時に考慮して鉱物を推定する必要がある.つまり,多次元で元素組成を比較する手法の導入が不可欠である.本研究ではEPMAによって得られた数種類の元素マップから,自己組織化マップを用いることで,鉱物マップを作成した.その結果,枕状玄武岩と塊状玄武岩の鉱物組成・テキスチャの違いや,班晶の大きさの分布を解明できた. |
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| | 講演要旨(英文) | | Because mineral textures of rocks as well as their mineral compositions have quantitative information on their formation histories, it is important to develop a technique of the mineral determination and quantitative texture description. To map minerals of pillow and massive basalts of the Juan de Fuca plate, chemical compositional maps on thin sections of the basaltic samples from Integrated Ocean Drilling Program Expedition 301 were constructed via Electron Probe Micro-Analyzer, EPMA. From the chemical compositional maps via EPMA, the mineral map has been estimated by considering the fraction of chemical components of each mineral. For the lithology which contains several kinds of minerals, however, we need to determine mineral map by considering several chemical compositions simultaneously. Therefore we should develop a method to compare the chemical components on multi-dimensional domain. Herein, mineral maps were constructed via an unsupervised neural network called as Self-Organizing Map from the several compositional maps. The classified mineral maps reveal mineralogical and texture differences between pillow basalt and massive flow basalt. Using the classified mineral maps, the fraction of bulk mineral components could be estimated. Furthermore, the digitalized data of the mineral map enable us to quantify size distribution of phenocryst and its fractions. |
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