A Machine Learning Approach for Improving the Detection Capabilities at 3C Seismic Stations-Reference-Cited by-同舟云学术

A Machine Learning Approach for Improving the Detection Capabilities at 3C Seismic Stations

Published:2012-09-20 Issue:3-5 Volume:171 Page:395-411
ISSN:0033-4553
Container-title:Pure and Applied Geophysics
language:en
Short-container-title:Pure Appl. Geophys.

Author:

Riggelsen Carsten,Ohrnberger Matthias

Publisher

Springer Science and Business Media LLC

Subject

Geochemistry and Petrology,Geophysics

Link

http://link.springer.com/content/pdf/10.1007/s00024-012-0592-3.pdf

Reference22 articles.

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2. T.C. Bache, S.R. Bratt, H.J. Swanger, G.W. Beall, and F.K. Dashiell. Knowledge-based interpretation of seismic data in the intelligent monitoring system. October, 83(5):1507–1526, 1993.

3. T.C. Bache, S.R. Bratt, J. Wang, R. M. Fung, C. Kobryn, and J.W. Given. The intelligent monitoring system. Bulletin of the Seismological Society of America, 80(6):1833–1851, 1990.

4. M. Beyreuther, R. Carniel, and J. Wassermann. Continuous hidden markov models: Application to automatic earthquake detection and classification at Las Canadas caldera, Tenerife. Journal of Volcanology and Geothermal Research, 176:513–518, 2008.

5. J. Bilmes. What HMMs Can Do. IEICE - Transactions on Information and Systems, 2006.

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