Rapid Joint Detection and Classification with Wavelet Bases via Bayes Theorem
Rapid Joint Detection and Classification with Wavelet Bases via Bayes Theorem
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基于贝叶斯定理的小波基快速联合检测和分类
DOI：
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
D. Manolakis
中科院分区：
文献类型：
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作者：
P. Gendron;J. Ebel;D. Manolakis
离散小波变换（DWT）目前正被用于地震事件检测和分类在新英格兰地区。小波变换形成了一个新的基础集，用于从数据流中挑选出地震事件的重要特征：时间、能量和第一个、峰值和最后一个波形的主导周期。这些事件的分类，从他们的功能到以下类别之一，地震，区域地震，近震，采石场爆炸，和假触发器，是完成与条件类密度来自训练数据。在波士顿学院韦斯顿天文台的新英格兰地震台网（NESN）上对该算法进行了检测和分类性能测试。这种检测算法表现出的检测的可能性比STA/LTA在典型的宽带网络的约束下，在任意条件下，在NESN站的两倍。通过这种方法分类的地震事件实现了大约70%的正确识别率相对于人类观众在广泛的数据测试集。
The discrete wavelet transform (DWT) is currently being used for seismic-event detection and classification in the New England region. The DWT forms a new basis set for picking out, from a data stream, important features of a seismic event: time, energy, and predominant period of the first, peak, and last waveforms. Classification of these events from their features into one of the following classes, teleseisms, regional earthquakes, near earthquakes, quarry blasts, and false triggers, is accomplished with conditional class densities derived from training data. This algorithm is tested for detection and classification performance on the New England Seismic Network (NESN) of Weston Observatory of Boston College. This detection algorithm exhibits a likelihood of detection two times greater than STA/LTA under typical wideband network constraints in arbitrary conditions at NESN stations. Classification of seismic events via this method achieves an approximately 70% correct identification rate relative to a human viewer over a broad range of data test sets.