基于物理学的神经网络协调了澳大利亚的迁移和构造压力
Thomas Poulet1, Pouria Behnoudfar2
1Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mineral Resources, Kensington, Perth, WA, 6151, Australia. thomas.poulet@csiro.au.
Scientific reports
|December 28, 2023
概括
本研究介绍了ML-SEISMIC,这是一种用于估计构造压力的机器学习方法. 它自主调整应力数据与弹性模型,绕过手动调整以获得更好的地力学洞察力.
科学领域:
- 地质物理学 地质物理学
- 计算地力学计算地力学
- 机器学习应用 机器学习应用
背景情况:
- 应力导向数据对于理解地球的活跃构造力至关重要.
- 世界压力图数据集有助于对应力和位移场的机械模型进行校准.
- 传统的倒置方法需要手动调整地力学特性和边界条件,从而限制效率.
研究的目的:
- 开发一种自动化方法,用弹性模型对应应力导向数据.
- 为了减少对压力逆转中的地力学特性和边界条件的手动调整的依赖.
- 提供材料属性,位移和应力张量的全面分布.
主要方法:
- 引入ML-SEISMIC (用于压力估计的机器学习,集成卫星图像和计算建模).
- 使用基于物理学的深度神经网络方法.
- 将该方法应用于澳大利亚,使用精确的全球导航卫星系统观测.
主要成果:
- ML-SEISMIC自主调整应力导向数据与弹性模型,最大限度地减少手动输入.
- 该方法产生了材料性质,位移和应力张量的全面分布.
- 开发了一个强大的,规模独立的插值框架,确定需要重新解释应力导向的领域.
结论:
- ML-SEISMIC为地力学调查提供了一种简化和强大的过程.
- 该方法显著提升了速度和应力方向观测与物理模型的整合.
- 这种方法有望带来对地球动态过程的新时代洞察力.
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