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首页> 外文期刊>Seismological research letters >Super-Efficient Cross-Correlation (SEC-C): A Fast Matched Filtering Code Suitable for Desktop Computers
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Super-Efficient Cross-Correlation (SEC-C): A Fast Matched Filtering Code Suitable for Desktop Computers

机译：超高效的交叉相关（SEC-C）：适用于台式计算机的快速匹配过滤代码

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摘要

We present a new method to accelerate the process of matched filtering (template matching) of seismic waveforms by efficient calculation of (cross-) correlation coefficients. The cross-correlation method is commonly used to analyze seismic data, for example, to detect repeating or similar seismic waveform signals, earthquake swarms, foreshocks, aftershocks, low-frequency earthquakes (LFEs), and nonvolcanic tremor. Recent growth in the density and coverage of seismic instrumentation demands fast and accurate methods to analyze the corresponding large volumes of data generated. Historically, there are two approaches used to perform matched filtering one using the time domain and the other the frequency domain. Recent studies reveal that time domain matched filtering is memory efficient and frequency domain matched filtering is time efficient, assuming the same amount of computational resources. We show that our super-efficient cross-correlation (SEC-C) method-a frequency domain method that optimizes computations using the overlap-add method, vectorization, and fast normalization-is not only more time efficient than existing frequency domain methods when run on the same number of central processing unit (CPU) threads but also more memory efficient than time domain methods in our test cases. For example, using 30 channels of data with a sample rate of 50 Hz and 30 templates, each with durations of 8 s, SEC-C uses only 2.3 GB of memory whereas other frequency domain codes use three times more and parallelized time-domain codes use similar to 30% more. We have implemented a precise, fully normalized version of SEC-C that removes the mean of the data in each sliding window, and thus can be applied to raw seismic data. Another strength of the SEC-C method is that it can be used to search for repeating seismic events in a concatenated stack of individual event waveforms. In this use case, our method is more than one order of magnitude faster than conventional methods. The SEC-C method does not require specialized hardware to achieve its computation speed; instead it exploits algorithmic ideas that are both time- and memory-efficient and are thus suitable for use on off-the-shelf desktop machines.
机译：我们提出了一种通过有效计算（交叉）相关系数来加速地震波形的匹配滤波（模板匹配）的过程。互相关方法通常用于分析地震数据，例如，以检测重复或类似地震波形信号，地震群，留脚袋，余震，低频地震（LFES）和非贷款震颤。近期震动仪器密度和覆盖率的增长需要快速准确的方法来分析产生的相应大量数据。从历史上看，使用两种方法用于使用时域和另一个频域执行匹配的过滤器。最近的研究表明，时间域匹配过滤是记忆有效和频域匹配的滤波是时间效率，假设相同数量的计算资源。我们表明我们的超高效互相关（SEC-C）方法 - 使用重叠添加方法，矢量化和快速归一化优化计算的频域方法 - 不仅比运行时的现有频域方法更多的时间效率在相同数量的中央处理单元（CPU）线程上，而且在测试用例中的时间域方法也有更多的内存效率。例如，使用具有50 Hz和30个模板的采样率的30个数据通道，每个持续时间为8 S，SEC-C仅使用2.3 GB的存储器，而其他频域代码使用三倍并并行化的时域代码使用类似于30％。我们已经实现了一种精确的，完全归一化的SEC-C，它消除了每个滑动窗口中的数据的平均值，因此可以应用于原始地震数据。 SEC-C方法的另一个强度是它可用于搜索在各个事件波形的连接堆叠中重复地震事件。在此用例中，我们的方法比传统方法快于一个数量级。 SEC-C方法不需要专门的硬件来实现其计算速度;相反，它利用了时间和内存高效的算法思想，因此适用于搁板的桌面机器。

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