根据ETAS模型,对地震序列参数的时空表征和对新疆强烈余震的预测
Ke Li1, Maofa Wang2, Huiguo Zhang1
1College of Mathematics and System Science, Xinjiang University, Urumqi, Xinjian, China.
PloS one
|May 16, 2024
概括
综合嵌套拉普拉斯算法 (INLA) 通过流行型余震序列 (ETAS) 模型改进了地震余震预测. 这种方法提高了对强烈余震及其时间在新疆地震地区的预测.
科学领域:
- 地质物理学 地质物理学
- 地震学 地震学
- 计算统计学 计算统计学
背景情况:
- 地震余震带来重大风险,需要准确的预测模型.
- 流行病类型的余震序列 (ETAS) 模型被广泛使用,但可能是计算密集的.
- 地震活动的区域差异需要量身定制的余震分析.
研究的目的:
- 将集成嵌套拉普拉斯算法 (INLA) 应用于ETAS模型,用于分析新疆的地震序列.
- 研究ETAS模型参数的时间演变及其对余震特征的影响.
- 将INLA配备的ETAS模型的预测准确度与MCMC等传统方法进行比较.
主要方法:
- 在ETAS模型中应用集成嵌套拉普拉斯算法 (INLA) 进行参数估计.
- 对不同新疆地区地震序列的时间变化的ETAS模型参数的分析.
- 使用估计的ETAS模型参数预测强烈的余震.
主要成果:
- 在新疆,在余震触发和减弱能力方面发现了显著的区域差异.
- 埃塔斯模型参数,特别是Ms7.3霍坦地震序列的ETAS模型参数,在主震后15天内显示出显著的时间变化.
- 与MCMC算法相比,INLA配备的ETAS模型在预测早期余震事件及其精确时间方面表现出卓越的准确性.
结论:
- INLA为ETAS模型参数估计和余震预测提供了更有效,更准确的方法.
- 了解时间参数变化对于详细分析地震序列至关重要.
- 这些发现突出了区域地震特点,并提高了新疆余震预测的可靠性.
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