神经网络自动事件检测的应用用于水库触发的地震性监测网络
Jan Wiszniowski1, Grzegorz Lizurek1, Anna Tymińska1
1Institute of Geophysics, Polish Academy of Sciences, 01-452 Warsaw, Poland.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
结合自动和手动的地震信号检测方法,可显著提高储触发地震 (RTS) 的地震目录完整性. 这种方法可以提高事件检测率高达30%,有助于理解触发过程.
科学领域:
- 地质物理学 地质物理学
- 地震学 地震学
- 地震科学 地震科学 地震科学
背景情况:
- 储触发性地震 (RTS) 网络通常具有有限的站点覆盖,这使得地震检测具有挑战性.
- 不足够的P波数据需要可靠的S波识别来准确地确定事件位置.
- RTS数据集通常很小,需要对有限数据或外部全球数据集进行训练的算法.
研究的目的:
- 为了比较RTS的不同地震信号检测方法的有效性.
- 评估深度学习模型,转移学习,专用神经网络和手动检测.
- 确定在RTS地区增强地震目录的最佳方法.
主要方法:
- 全球深度学习检测模型,应用到RTS数据上的转移学习,专门的RTS神经网络和手动检测的比较.
- 专注于相位检测灵敏度,而不是相位选择准确性和特异性.
- 基于与地震事件位置和相位相关的参数进行评估.
主要成果:
- 转移学习的效率取决于所使用的特定数据库.
- 无论是自动的还是手动的检测方法都不足以进行全面的RTS事件检测.
- 结合自动和手动方法,可显著提高地震事件的检测能力.
结论:
- 自动和手动地震信号检测的结合方法大大提高了RTS目录的完整性.
- 增强的目录涵盖了多达30%的事件,有助于研究触发机制.
- 神经网络探测器对于增加检测到的地震事件数量和促进RTS研究至关重要.
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