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| Improving Seismic Monitoring System for Small to Intermediate Earthquake Detection
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Full
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Source |
International Journal of Computer Science and Security (IJCSS) |
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Table of Contents |
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Complete Issue PDF(4.51MB) |
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Volume: 4 Issue: 3 |
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Pages: 265-372 |
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Publication
Date: July 2010 |
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ISSN
(Online): 1985-1553 |
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Pages |
308 - 315 |
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Author(s) |
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Published
Date |
10-08-2010 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Feature extraction, Support Vector Machines, Kernels, Seismic signals, Wavelet decomposition Energy |
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| Efficient and successful seismic event detection is an important and challenging issue in many disciplines, especially in tectonics studies and geo-seismic sciences. In this paper, we propose a fast, efficient, and useful feature extraction technique for maximally separable class events. Support vector machine classifier algorithm with an adjustable learning rate has been utilized to adaptively and accurately estimate small level seismic events. The algorithm has been less computation, so that economic impact will be high. Experimental results demonstrate the strength and robustness of the method. |
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Centre for GeoTechnology , Manonmaniam Sundaranar University
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| Joe Vivek V : Colleagues
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| Chandrasekar, N : Colleagues
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| Srinivas, Y : Colleagues
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