基于物理学的深度学习量化了地震结构和低中心确定性的传播不确定性
Ryoichiro Agata1, Kazuya Shiraishi2, Gou Fujie2
1Japan Agency for Marine-Earth Science and Technology, 3173-25, Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, 2360001, Japan. agatar@jamstec.go.jp.
Scientific reports
|January 13, 2025
概括
这项研究引入了基于物理的深度学习 (PIDL) 方法来量化地震速度的不确定性,以更准确地确定地震中心. 考虑到这种不确定性,可以显著改善地震源参数分析.
科学领域:
- 地质物理学 地质物理学
- 地震学 地震学
- 机器学习 机器学习
背景情况:
- 精确的地下地震速度结构对于地震源研究至关重要,包括低中心的确定.
- 传统方法往往忽略了地震速度模型中的不确定性,这可能会影响结果.
研究的目的:
- 开发和应用基于物理的深度学习 (PIDL) 方法来量化2D地震速度模型中的不确定性.
- 调查这种不确定性传播对低中心确定性的影响.
主要方法:
- 利用经过对地震调查数据,地震观测和波传播物理学的训练的神经网络组合.
- 实施了PIDL框架来建模地震速度结构并量化相关的不确定性.
主要成果:
- 该方法应用于日本西南部的一次地震,显著减少了低中心确定中的偏差和低估.
- 通过考虑不确定性传播,使得相对于板边界的焦点深度能够进行定量评估.
结论:
- PIDL方法有效量化了地震速度建模中的不确定性及其对低中心确定性的影响.
- 这种方法对改善地球物理逆向问题的有希望,包括地震源参数分析.
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