A Machine Learning Approach for Improving the Detection Capabilities at 3C Seismic Stations
A Machine Learning Approach for Improving the Detection Capabilities at 3C Seismic Stations
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DOI：
10.1007/s00024-012-0592-3
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发表时间：
2014-03
影响因子：
2
通讯作者：
C. Riggelsen;M. Ohrnberger
中科院分区：
文献类型：
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作者：
C. Riggelsen;M. Ohrnberger
我们应用并评价了一种最新的机器学习方法用于地震波形的自动分类。该方法依靠动态贝叶斯网络(DBN)和监督学习来提高3C地震台的检测能力。时频分解提供了所需的信号特征的基础，以便推导出定义典型的“信号”和“噪声”模式的特征。每个模式类由DBN建模，指定时频平面中派生特征的相互关系。随后，使用先前标记的地震数据段来训练模型。现在可以将DBN模型与之进行比较，以便确定新传入的地震波形段是信号还是噪声的可能性。由于地震台的噪声特征在时间上变化平稳(季节变化和人为影响)，我们的方法适应了与噪声类别相关的DBN模型的连续适应。考虑到难以获得真实数据(地面真实)的黄金标准，概念和评价的验证是通过基于国际监测站、BOSA和LPAZ的3C地震数据进行的实验进行的。
We apply and evaluate a recent machine learning method for the automatic classification of seismic waveforms. The method relies on Dynamic Bayesian Networks (DBN) and supervised learning to improve the detection capabilities at 3C seismic stations. A time-frequency decomposition provides the basis for the required signal characteristics we need in order to derive the features defining typical “signal” and “noise” patterns. Each pattern class is modeled by a DBN, specifying the interrelationships of the derived features in the time-frequency plane. Subsequently, the models are trained using previously labeled segments of seismic data. The DBN models can now be compared against in order to determine the likelihood of new incoming seismic waveform segments to be either signal or noise. As the noise characteristics of seismic stations varies smoothly in time (seasonal variation as well as anthropogenic influence), we accommodate in our approach for a continuous adaptation of the DBN model that is associated with the noise class. Given the difficulty for obtaining a golden standard for real data (ground truth) the proof of concept and evaluation is shown by conducting experiments based on 3C seismic data from the International Monitoring Stations, BOSA and LPAZ.