Automatic Time Picking for Microseismic Data Based on a Fuzzy C-Means Clustering Algorithm

Time picking is an essential step in microseismic data processing, as the hypocenter location requires the arrival times of P- and/or S-waves. However, it is difficult to obtain arrival times accurately using traditional methods when the signal-to-noise ratio (SNR) of data is low. In this letter, we...

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Vydáno v:IEEE geoscience and remote sensing letters Ročník 13; číslo 12; s. 1900 - 1904
Hlavní autoři: Zhu, Dan, Li, Yue, Zhang, Chao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Akaike information criterion (AIC)
Algorithms
automatic time picks
Clustering
Clustering algorithms
Data analysis
Data points
Data processing
fuzzy C-means clustering (FCM) algorithm
Indexes
Linear programming
Methods
Microseisms
Noise measurement
P-waves
Reliability
S waves
Seismology
short and long time average (STA/LTA) algorithm
Signal processing
Signal to noise ratio
Similarity
Time series
ISSN:1545-598X, 1558-0571
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Shrnutí:Time picking is an essential step in microseismic data processing, as the hypocenter location requires the arrival times of P- and/or S-waves. However, it is difficult to obtain arrival times accurately using traditional methods when the signal-to-noise ratio (SNR) of data is low. In this letter, we propose a new time picking method based on the fuzzy C-means clustering (FCM) algorithm, which can divide microseismic data into two clusters according to the different levels of similarity between the signals and noise. Using the FCM, we can obtain a membership degree matrix that represents the similarity of data. Data points whose values of the membership degree matrix are high show a high level of similarity and we assign these into the signal cluster. We regard the initial time of the signal cluster as the arrival time of data. To verify the reliability of the method, we conduct a large number of tests and give receiver operating characteristic curves with different SNR of signals. Our method is tested on both synthetic and real microseismic signals. Furthermore, we compare the FCM method with the short and long time average algorithm and the Akaike information criterion. The results indicate that our method can pick arrival times precisely even when the SNR of data is as low as -8 dB and the accuracy rate is superior to the other two methods.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2016.2616510