机器学习用于固体地球地质科学的数据驱动发现
Karianne J Bergen1,2, Paul A Johnson3, Maarten V de Hoop4
1Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA.
概括
固体地球地质科学在理解复杂的地下过程方面面临挑战. 机器学习提供了一种有前途的方法,通过分析更多的数据和改进地球科学发现的计算机模拟来加速进步.
科学领域:
- 固体地球地质科学
- 地质学
- 地动力学
背景情况:
- 了解地球的行为至关重要,但受到复杂的多层次过程的阻碍.
- 直接观测地球的地表是非常有限的.
- 数据可用性和计算模拟的进步为研究提供了新的途径.
研究的目的:
- 审查固体地球地质科学的机器学习应用的现状.
- 确定加速科学发现的挑战和机遇.
- 为扩大和推进该领域提供建议.
主要方法:
- 对地球科学中的机器学习现有文献和案例研究进行审查.
- 分析数据驱动方法和先进模拟的潜力.
- 综合现有能力和未来的研究方向.
主要成果:
- 机器学习 (ML) 将在推进地球科学方面发挥关键作用.
- 增加数据和复杂的模拟,当与机器学习相结合时,可以增强对地球复杂系统的理解.
- 机器学习的整合需要解决数据处理和模型可解释性的特定挑战.
结论:
- 机器学习对于克服固有的复杂性和固体地球地质科学的观察局限性至关重要.
- 建议进一步开发和应用ML技术以加快进展.
- 跨学科合作和战略投资是实现地球科学ML全部潜力的关键.
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