• ISSN 2097-1893
  • CN 10-1855/P

通过无监督机器学习自动拾取微震事件

Y. K. Chen 李帛珊 唐丽华 孙燕萍 吕春来

引用本文: 李帛珊, 唐丽华, 孙燕萍 译. 2019. 通过无监督机器学习自动拾取微震事件. 世界地震译丛. 50(5): 411-432. doi:10.16738/j.cnkii.ssn.1003-3238.201905002

通过无监督机器学习自动拾取微震事件

doi: 10.16738/j.cnki.issn.1003-3238.201905002
基金项目: 

新疆维吾尔自治区自然科学基金项目(2017D01A66)资助

  • 摘要: 高效的波至拾取在微震和地震数据处理及成像过程中起到重要作用。广泛使用的基于波至拾取算法的短期平均和长期平均比(STA/LTA),对中强度随机环境噪声的敏感程度较低。为了使最先进的波至拾取方法奏效,首先需要对微震数据进行预处理,例如,消除足量的噪声,再由波至拾取法进行分析。为了解决微震或地震事件波至拾取的噪声问题,利用机器学习技术帮助识别微震或地震数据中的地震波形。由于受监督机器学习算法对大量设计好的训练数据具有依赖性,本文利用无监督机器学习算法将时间样本分为两组,即波形点和非波形点。已证明模糊聚类算法可以运用于微震拾取。一组复杂程度不同的合成、真实微震和地震数据集表明,即使在中强背景噪声情况下,该方法在拾取微震事件方面比最先进的STA/LTA方法表现得更稳健。

     

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