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首页> 外文期刊>Pure and Applied Geophysics >Earthquake Fingerprints: Extracting Waveform Features for Similarity-Based Earthquake Detection
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Earthquake Fingerprints: Extracting Waveform Features for Similarity-Based Earthquake Detection

机译：地震指纹：提取基于相似性的地震检测的波形特征

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摘要

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.
机译：地震师越来越多地采用数据挖掘和机器学习技术来检测大地震数据集中的弱地震信号。这些新方法的检测性能，尤其是它们的灵敏度和假检测率，取决于波形数据的特征表示的选择。我们之前引入了指纹和相似度阈值（快速），一种用于波形相似性的地震检测方法，使用模式挖掘方法来检测没有模板波形的地震信号。快速有两个关键步骤：指纹提取和高效索引的相似性搜索。在这项工作中，我们专注于快速指纹提取：用于将短持续时间波形映射到一组特征的方法，称为波形指纹，用于检测。我们描述了快速指纹提取方法，对图定制的波纹音频指纹方法的数据适应性变化，用于连续地震数据。我们将快速指纹提取方法与专为音频识别设计的现有指纹提取方法的性能进行比较。为了克服与使用有限或不完整的事件目录相关的挑战来评估检测算法，我们提出了一种用于量化不同指纹提取方法在基于盲相似性的检测的背景下的性能的框架。我们的框架使用具有已知事件波形构成的基准数据集的计算实验，以计算指纹效率的量度。我们使用此框架来表明，在这项工作中考虑的音频指纹线程中，我们所提出的快速指纹提取方法在区分与八个站的波形数据集中的噪声中区分类似的低信噪决地震波形的最正常的性能在北加州地震网络。

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