Matched filter detection of microseismicity in long beach with a 5200-station dense array

Zefeng Li, Zhigang Peng, Xiaofeng Meng, Asaf Inbal, Yao Xie, Dan Hollis, Jean Paul Ampuero

Research output: Contribution to journalConference articlepeer-review


We use a waveform matched filter technique to detect microseismicity recorded by a 5200-station array in the Long Beach area around the Signal Hill oil reservoir. Because the computation cost increases linearly with number of stations, it is challenging to apply the technique to this large data set. Here we conduct a series of synthetic tests to explore how to best perform waveform detection with a large array. First we show that a large array can successfully detect very small signals, even they are totally buried in the background noise with very low signal to noise ratios. However, when only part of the array records the event, direct stacking of correlation-coefficients (CCs) for all stations results in lower mean CCs. Finally, given the temporal sparsity of events we can decrease the data volume of cross-correlation by a large amount without sacrificing detection reliability. The proposed strategy can efficiently cut down the computational cost and can be used to detect seismic events in similar types of dataset.

Original languageEnglish
Pages (from-to)2615-2619
Number of pages5
JournalSEG Technical Program Expanded Abstracts
StatePublished - 2015
Externally publishedYes
EventSEG New Orleans Annual Meeting, SEG 2015 - New Orleans, United States
Duration: 18 Oct 201123 Oct 2011


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