固体地球科学におけるデータ主導の発見のための機械学習
Karianne J Bergen1,2, Paul A Johnson3, Maarten V de Hoop4
1Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA.
まとめ
固体地球の地質科学は,複雑な地下プロセスを理解する上で課題に直面しています. 機械学習は,増加したデータを分析し,地球科学の発見のためのコンピュータシミュレーションを改善することによって,進歩を加速させる有望なアプローチを提供します.
科学分野:
- 固体地球地球科学
- 地理学
- 地力学
背景:
- 地球の行動を理解することは 極めて重要ですが 複雑で多面的なプロセスによって 妨げられています
- 地球の地下を直接観測することは 極めて限られている.
- データの可用性やコンピュータによるシミュレーションの進歩は 研究の新たな道を開きます
研究 の 目的:
- 固体地球科学における機械学習アプリケーションの現状をレビューする.
- 科学的発見を加速させるための課題と機会を特定する.
- この分野を拡大し,進めるための勧告を提供すること.
主な方法:
- 地球科学における機械学習に関する既存の文献とケーススタディのレビュー.
- データベースのアプローチと高度なシミュレーションの可能性を分析する.
- 現在の能力と将来の研究方向の統合
主要な成果:
- 機械学習 (ML) は,地球科学の進歩において重要な役割を果たす準備ができています.
- より多くのデータと洗練されたシミュレーションを MLと組み合わせれば 地球の複雑なシステムの理解を深めることができます
- MLを統合するには,データ処理とモデルの解釈性に関する特定の課題に取り組む必要があります.
結論:
- 機械学習は,固体地球科学における固有の複雑性と観測上の限界を克服するために不可欠です.
- 進歩を加速するために,ML技術のさらなる開発と適用が推奨されます.
- 分野間の協力と戦略的投資は 地球科学における機械学習の潜在能力を 十分に発揮するための鍵となります
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