変成岩組織と鉱物組成累帯構造からの情報抽出::フォーワードモデルと逆解析  [in Japanese] Information extraction from metamorphic rock textures and compositional zoning of minerals::Forward models and inversion analyses  [in Japanese]

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Author(s)

    • 桑谷 立 Kuwatani Tatsu
    • 国立研究開発法人海洋研究開発機構|国立研究開発法人科学技術振興機構 Japan Agency for Marine-Science and Technology|PRESTO, Japan Science and Technology Agency(JST)

Abstract

<p>変成作用は,地球内部の温度・圧力・化学組成などの条件に応答して進行する化学反応である.変成作用の時間変化を観測することは難しく,最終状態の空間パターン(岩石組織)という独特な情報をいかに読み解くかが,岩石の形成条件やプロセスを理解する鍵となる.変成岩組織には,解釈がシンプルで理論背景が確立しているものから,複数のプロセスが関与して,モデル自体も手探りなものまで様々なクラスがある.本総説では,鉱物の組成累帯構造の熱力学的解析と,反応–破壊カップリング組織の離散要素法モデルを代表例とし,変成岩組織の逆解析とフォーワードモデリングの最近の進展についてまとめる.また,確率的な逆解析が,不定要素の大きい岩石学の問題に有効であることを示す.数値シミュレーションと観測データを統合するデータ同化的なアプローチは,今後,複雑な岩石組織の解読にブレイクスルーをもたらすものと期待できる.</p>

<p>Metamorphism refers to the reactions that proceed in rocks in response to the dynamic environmental factors of the Earth's interior, which include temperature, pressure, and chemical composition. Because it is difficult to obtain time-series data of metamorphic processes directly, it is necessary to extract information from the spatial patterns of the final states of the rocks (e.g., the textures of metamorphic rocks) to understand the conditions and processes of metamorphism. The complications of metamorphic rock textures range widely, from simple problems that can be modeled based on rigid theoretical backgrounds, to complex problems in which several processes interact in nonlinear ways, and forward models themselves are still being developed based on combinations of empirical laws. In this paper, we review recent progress in the analysis and modeling of metamorphic rock textures, with a particular focus on the thermodynamic analysis of zoned minerals and forward modeling of reaction-induced fracturing, employing the distinct element method (DEM). We show that stochastic inversion analyses based on Bayesian inference can be a powerful tool for solving various petrological problems characterized by parameters with undefined values and noise. By effectively using algorithms of machine-learning, the approach of data assimilation, which combines numerical simulations and observed data, is likely to yield a breakthrough in terms of deciphering the complex textures of metamorphic rocks.</p>

Journal

  • The Journal of the Geological Society of Japan

    The Journal of the Geological Society of Japan 123(9), 733-745, 2017

    The Geological Society of Japan

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