Home   >   CSC-OpenAccess Library   >    Manuscript Information
Improving Seismic Monitoring System for Small to Intermediate Earthquake Detection
Joe Vivek V, Chandrasekar, N, Srinivas, Y
Pages - 308 - 315     |    Revised - 30-06-2010     |    Published - 10-08-2010
Volume - 4   Issue - 3    |    Publication Date - July 2010  Table of Contents
MORE INFORMATION
KEYWORDS
Feature extraction, Support Vector Machines, Kernels, Seismic signals, Wavelet decomposition Energy
ABSTRACT
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.
CITED BY (3)  
1 Chaubey, A., Chelladurai, H., Lamba, S. S., (2014). Condition monitoring of rotating shaft using virtual instrumentation" 5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014), pp. 557-(1-6). IIT Guwahati, Assam, India.
2 Curilem, M., Huenupan, F., San Martin, C., Fuentealba, G., Cardona, C., Franco, L., Acuña, G., Chacón, M., (2014). Feature Analysis for the Classification of Volcanic Seismic Events Using Support Vector Machines. In Nature-Inspired Computation and Machine Learning (pp. 160-171). Springer International Publishing.
3 Curilem, M., Vergara, J., San Martin, C., Fuentealba, G., Cardona, C., Huenupan, F., Chacón, M., Khan, M. S., Hussein, W. & Yoma, N. B. (2014). Pattern recognition applied to seismic signals of the Llaima volcano (Chile): An analysis of the events' features. Journal of Volcanology and Geothermal Research, 282, 134-147.
1 Google Scholar 
2 Academic Journals Database 
3 Academic Index 
4 CiteSeerX 
5 Socol@r  
6 ResearchGATE 
7 Libsearch 
8 Bielefeld Academic Search Engine (BASE) 
9 Scribd 
10 WorldCat 
11 SlideShare 
12 PDFCAST 
13 PdfSR 
A. Clark, Gregory Rodgers, W. Peter. “Adaptive Prediction Applied to Seismic Event Detection”. Proc. IEEE, v.69: 1166-1168, 1981
A.Ahmed, M.L. Sharma, A. Sharma. “Wavelet Based Automatic Phase Picking Algorithm for 3-Component Broadband Seismological Data” JSEE: Spring and Summer, v. 9, no. 1,2, pp. 15-24, 2007
Abualgla Babiker Mohd, Sulaiman bin Mohd Nor. “Towards a Flow-based Internet Traffic Classification for Bandwidth Optimization” International Journal of Computer Science and Society, v. 3, Issue 2, pp. 146-153, 2009
B.K. Sharma, Kumar Amod, V.M. Murthy. “Evaluation of Seismic Events Detection Algorithms”. Jour. Geol. Soc. India, v.75, pp.533-538, 2010
D. Stearns, Samuel Vortman, J. Luke. “Seismic Event Detection using Adaptive Predictors”. IEEE International conference on Acoustic, Speech and Signal Processing, USA, v.3, pp.1058-1061, 1981
D.J. Houliston, G. Waugh, J. Laughlin. “Automatic Real-Time Event Detection for Seismic Networks”. Computers & Geosciences, v.10: 413-436, 1984
G. Richard, Shiavi, John R. Bourne.(1986): Methods of Biological Signal Processing. In Tzay Y. Young and KingSun Fu, editors, “Handbook of Pattern Recognition and Image Processing”, Academic Press, Orlando, Florida, chapter 22, pp. 545-568 (1986)
G.T. Heydt, A.W. Galli. “Transient power quality problems analyzed using wavelets”. IEEE
H.S. Manjunatha Reddy, K.B. Raja “High Capacity and Security Steganography using Discrete Wavelet Transform” International Journal of Computer Science and Society, v. 3, Issue 6, pp. 462-472, 2009
K. Fretcher, Sharon. “Walsh Transforms in Seismic event Detection”. IEEE Trans. Electromagnetic Compatibility, v.25, 1983
K. Robert, Vincent, Zheng Zhizhen, Shen Ping; Zhang Shaofen. “ Wavelet-Packet Transformation Analysis of Seismic Signals Recorded from a Tornado in Ohio Bull”. Seismological Soc. Amer v. 92, no. 6, pp. 2352-2368, Aug.2002
K.S. Fu. Editor. “Syntactic Pattern Recognition, Applications”. SpringerVerlag, Berlin. Goforth, 1977
K.S.W. Stewart. “Real time detection and location of local seismic events in central California” Bulletin of Seismological Soc. Amer, v. 67, pp. 433-452, 1977 Bulletin of Seismological Soc. Amer, v. 67, pp. 433-452, 1977
Kumar Satish, B.K. Sharma, Sharma Parkhi and M.A. Shamshi. “24 Bit seismic processor for analyzing extra large dynamic range signals for early warnings”. Jour. Scientific and Industrial Res., v.68: 372-378, 2009
Man-Kwan Shan “Discovering Color Styles from Fine Art Images of Impressionism” International Journal of Computer Science and Society, v. 3, Issue 4, pp. 314-324, 2009
Ping An. “Application of multi-wavelet seismic trace decomposition and reconstruction to seismic data interpretation and reservoir characterization”. SEG/New Orleans 2006 Annual Meeting. pp. 973-977, 2006
R. Allen. “Automatic earthquake recognition and timing from single traces”. Bull.
T. Pavlidis. “Structural Pattern Recognition”. SpringerVerlag, Berlin, (1977)
Tom, Herrin, Eugence. “An Automatic Seismic Signal Detection Algorithm based on the Walsh Transform”. Bull. Seismological Soc. Amer., v.71: 1351-1360, 1981
V.Joevivek, T. Hemalatha, K.P. Soman “Determining an Efficient Classification Algorithm for Hyperspectral Image” proc. of ARTCOM (IEEE), pp. 384-386, 2009
W. Freiberger. “An approximate method in signal detection”. Jour. Applied Math, v.20: 373-378, 1963
Mr. Joe Vivek V
Manonmaniam Sundaranar University - India
vjoevivek@gmail.com
Professor Chandrasekar, N
Centre for Geo-Technology - India
Associate Professor Srinivas, Y
Centre for Geo-Technology - India