通过使用可见度图算法分析加利福尼亚和合成系列的余震
Alejandro Muñoz-Diosdado1, Ana María Aguilar-Molina1, Eric Eduardo Solis-Montufar1
1Unidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Mexico City 07340, Mexico.
Entropy (Basel, Switzerland)
|February 26, 2025
概括
可见度图算法 (VGA) 通过将时间序列转换为网络来揭示地震动态. 在地震后,在真实和合成数据中观察到的网络参数显著下降,表明有放松机制.
科学领域:
- 地质物理学和地震学.
- 复杂系统分析 复杂系统分析
- 时间序列分析时间序列分析.
背景情况:
- 传统的地震分析往往错过了微妙的相关性和动态.
- 可见度图算法 (VGA) 提供了一种新的方法,通过将时间序列转换成复杂的网络来分析时间序列.
- 了解地震活动,特别是地震前后的行为,对于预测至关重要.
研究的目的:
- 用可见度图算法分析南加州的地震活动.
- 在地震发生之前和之后调查网络属性的变化.
- 将真实地震数据与由弹模块模型生成的合成地震率进行比较.
主要方法:
- 应用可见度图算法 (VGA) 来将地震时间序列转换为网络表示.
- 在发生重大地震之前和之后使用窗户方法构建的地震系列.
- 从来自地震数据的复杂网络中计算了k-M斜率和平均度.
主要成果:
- 在南加州发生重大地震后,观察到k-M斜率和平均度的显著下降.
- 通过弹模块模型生成的合成地震性序列的这些网络参数也出现了类似的下降.
- 结果表明,弹模块模型捕捉了类似于真实地震余震的放松动态.
结论:
- 可见度图算法 (VGA) 对于分析大地震前后的地震事件非常有效.
- 观察到的网络参数下降表明地震后的放松机制.
- VGA可能为改善地震行为的理解和潜在预测提供有价值的趋势.
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