多変量解析を用いたボーリング孔での断層の区間判定と岩盤区分 : 瑞浪超深地層研究所における深層ボーリング孔での事例

Abstract

岩盤中の断層区間は, 地下の地質環境や地盤の工学的性能を評価する際の重要な要素の一つである. しかしながら, ボーリング調査では様々な制約により, 常に同じ品質や量のデータが確保できるとは限らず, 諸条件によって評価結果が異なる可能性がある. そうした評価結果の差異は, 地質環境を理解していく過程での不確実性の増大につながり, その後の計画立案時における適切な意思決定を難しくするだけでなく, 直接的な施工のリスク要因にもなる. そのため本研究では, 岐阜県瑞浪市でのボーリング調査データを用いて, 使用する変数を明確な基準で選択した上で, 多変量解析( 主成分分析およびクラスタリング)を適用した. その結果, 客観的な基準により, 岩盤を高精度で区分し, 断層区間を適切に判定できるようになった. これにより, 一つの調査項目のみに注目した従来の解析に比べ, 多種情報を一度に扱う多変量解析は有効な手法であることが実証された.

Faults with a crush zone can strongly affect the mechanical, geochemical, and hydrological properties of a rock mass. Because of this, fault zones are treated as essential elements for evaluating the underground geological environment and the engineering performance of rocks. Because of the limitations to borehole investigations, it is not always possible to obtain sufficient, high-quality geological data. In addition, the evaluation of results may differ depending on various factors such as geological conditions and skill of the engineer or geologist. Such uncertainty can lead to difficulty in evaluation and understanding of the geological environment at depth and in the decision-making and planning of underground construction, which, as a result, may increase potential risks during construction. To reduce the uncertainty, this study proposes a data selection method using multivariate analysis composed of principal component analysis and a clustering method using data from a deep borehole investigation in the Mizunami Underground Research Laboratory (Mizunami City, Gifu, Central Japan). Utilizing this method and the analyses, the rocks could be accurately classified depending upon their geological characteristics. It was also possible to discriminate subtle differences in the rockmass. Furthermore, the location and width of fault zones were determined objectively. Accordingly, multivariate analysis, in considering the variety of data from different sources, proved to be more effective than traditional methods that analyze items individually. Moreover, a method to rank the variables used in the principal component analysis was developed using a logical and quantitative index that can arrange the variables in their order of importance. The proposed method developed in this study can provide useful geological and engineering information for 3-D geological modeling, construction of underground structures and groundwater flow analysis.