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大規模な地震後の余震パターンの深層学習
Phoebe M R DeVries1,2, Fernanda Viégas3, Martin Wattenberg3
1Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA. phoebe.devries@uconn.edu.
Nature
|August 31, 2018
まとめ
新しいディープラーニングモデルは ストレス変化を分析することで 地震の余震の位置を正確に予測します この方法は従来のストレス変化モデルを上回り 地震を誘発するメカニズムについて より良い予測と洞察を提供します
科学分野:
- 地震学
- 地理学
- 機械学習
背景:
- 余震は大きな地震による ストレス変化によって引き起こされます
- 経験的な法則は余震の大きさや 崩壊を記述しますが 空間的な予測は困難です
- クーロン欠陥のストレス変化は,余震分布の一般的な,しかし議論の余地のある基準です.
研究 の 目的:
- 余震の位置を予測するための ディープラーニングのアプローチを開発する.
- 断層方向の仮定なしに余震の予測のための静的ストレスベースの基準を特定する.
- 既存の方法と比較して余震予測の精度を改善する.
主な方法:
- ニューラル・ネットワークを使って 13万1千のメインショック・アフターショック・ペアを 訓練した
- モデルを3万以上の メインショック・アフターショックの 独立したデータセットに適用した.
- ディープラーニングの予測と 古典的なクーロン欠陥のストレス変化を比較した.
主要な成果:
- ディープラーニングモデルは,クーロン欠陥ストレス変化 (AUC 0.583) よりも高い精度 (AUC 0.849) を達成した.
- 学習されたパターンは物理的に解釈可能であり,シアストレスのインヴァリアンタとストレスのテンサーは予測変数を説明する.
- 地震の誘発を制御する 重要な物理量
結論:
- ディープラーニングは 余震の位置を予測する より正確な方法を提供します
- この研究は,物理的に有意義な ストレスに基づく地震誘発基準を明らかにしています.
- このアプローチにより,地震のサイクルと地震予測能力の理解が向上します.
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