Super‐Efficient Cross‐Correlation (SEC‐C): A Fast Matched Filtering Code Suitable for Desktop Computers
Nader Shakibay Senobari, G. J. Funning, Eamonn Keogh, Yan Zhu, Chin‐Chia Michael Yeh, Zachary Zimmerman, Abdullah Mueen · Seismological Research Letters · 2018
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 crosscorrelation method is commonly used for analyzing seismic data, e.g. to detect repeating or similar seismic waveform signals, earthquake swarms, foreshocks, aftershocks, low-frequency earthquakes and non-volcanic 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) methoda frequency domain method that optimizes computations using the overlap-add method, vectorization and fast normalizationis not only more time efficient than existing frequency domain methods when run on the same number of CPU threads, but is also more memory efficient than time domain methods.For example, using 30 channels of data with a sample rate of 50 Hz and 30 templates, each with durations of 8 seconds, SEC-C uses only 2.3 GB of memory while other frequency domain codes use three times more and parallelized time domain codes use ~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 does not require any preprocessing of the 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-theshelf desktop machines.