Earthquake Fingerprints: Extracting Waveform Features for Similarity-Based Earthquake Detection
Earthquake Fingerprints: Extracting Waveform Features for Similarity-Based Earthquake Detection
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DOI：
10.1007/s00024-018-1995-6
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发表时间：
2018-10
影响因子：
2
通讯作者：
K. Bergen;G. Beroza
中科院分区：
文献类型：
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作者：
K. Bergen;G. Beroza
地震学家越来越多地采用数据挖掘和机器学习技术来检测大型地震数据集中的微弱地震信号。这些新方法的检测性能，特别是它们的灵敏度和误检率，取决于波形数据的特征表示的选择。我们之前介绍过指纹和相似性阈值（FAST），这是一种基于波形相似性的地震检测新方法，它使用模式挖掘方法在没有模板波形的情况下检测地震信号。FAST有两个关键步骤：指纹提取和相似性搜索的高效索引。在这项工作中，我们专注于FAST指纹提取：用于将短持续时间波形映射到一组用于检测的特征（称为波形指纹）的方法。我们描述了FAST指纹提取方法，一种针对连续地震数据定制的Waveprint音频指纹方法的数据自适应变化。我们比较的快速指纹提取方法与现有的指纹技术设计的音频识别的性能。为了克服与使用有限或不完整的事件目录来评估检测算法相关的挑战，我们提出了一个框架，用于在基于相似性的盲检测的背景下量化不同指纹提取方法的性能。我们的框架使用计算实验的基准数据集，构造与已知的事件波形，计算指纹有效性的措施。我们使用这个框架表明，在这项工作中考虑的音频指纹识别方案，我们提出的FAST指纹提取方法实现了最一致的性能，区分类似的，低信噪比的地震波形从噪声的波形数据集从北方加州地震台网。
Seismologists are increasingly adopting data mining and machine learning techniques to detect weak earthquake signals in large seismic data sets. The detection performance of these new methods, especially their sensitivity and false detection rate, depends on the choice of feature representation for waveform data. We have previously introduced Fingerprint and Similarity Thresholding (FAST), a new method for waveform-similarity-based earthquake detection that uses a pattern mining approach to detect earthquake signals without template waveforms. FAST has two key steps: fingerprint extraction and efficient indexing for similarity search. In this work, we focus on FAST fingerprint extraction: the method used to map short-duration waveforms to a set of features, called waveform fingerprints, used for detection. We describe the FAST fingerprint extraction method, a data-adaptive variation on the Waveprint audio fingerprinting method tailored for use in continuous seismic data. We compare the performance of the FAST fingerprint extraction method with existing fingerprinting techniques designed for audio identification. To overcome the challenges associated with using limited or incomplete event catalogs to evaluate detection algorithms, we propose a framework for quantifying the performance of different fingerprint extraction methods in the context of blind similarity-based detection. Our framework uses computational experiments on benchmark data sets, constructed with known event waveforms, to compute a measure of fingerprint effectiveness. We use this framework to show that, among the audio fingerprinting schemes considered in this work, our proposed FAST fingerprint extraction method achieves the most consistent performance in distinguishing similar, low signal-to-noise earthquake waveforms from noise in waveform data sets from eight stations in the Northern California Seismic Network.