NDLI: Detection and identification of seismic P-Waves using Artificial Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Kaur, K. Wadhwa, M. Park, E.K.
Copyright Year	2013
Description	Author affiliation: Dept. of Inf. Technol., South Univ., Virginia Beach, VA, USA (Wadhwa, M.) \|\| Dept. of Electr. & Comput. Eng., Old Dominion Univ., Norfolk, VA, USA (Kaur, K.) \|\| Dept. of Grad. Studies, California State Univ., Chico, CA, USA (Park, E.K.)
Abstract	Detection and identification of seismic P-Wave is useful in event location and event detection. This involves an intensive amount of pattern recognition. For the recognition of seismic phases, no probabilistic distribution model performs as well as Artificial Neural Network(ANN). Back Propagation Neural Network (BPNN) was applied for the automatic detection and identification of local and regional seismic P-Waves. For a set of three-component seismic data, four attributes were used as input to the ANN: Degree of Polarization (DOP), Auto Regression Coefficient (ARC), Ratio between Short time average and Long time average (STA/LTA) and Ratio of Vertical power to Total power (RV2T). These four attributes were calculated in the frequency band of 1-8 Hz with a 2 second moving window. The results of preliminary training and testing with a set of various local and regional earthquake recordings show that the ANN achieved 95% correct rate of P-Wave detection and identification. 90% of the P-Waves were detected with a maximum deviation of 0.1 sec from correct manual pick up.
Starting Page	1
Ending Page	6
File Size	505825
Page Count	6
File Format	PDF
ISSN	21614407
e-ISBN	9781467361293
DOI	10.1109/IJCNN.2013.6707117
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Earthquakes Training Testing Manuals Seismic waves
Content Type	Text
Resource Type	Article

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