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| Titel |
Soft computing analysis of the possible correlation between temporal and energy release patterns in seismic activity |
| VerfasserIn |
Anthony Konstantaras, Emmanouil Katsifarakis, Xristos Artzouxaltzis, John Makris, Filippos Vallianatos, Martin Varley |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250040531
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| Zusammenfassung |
| This paper is a preliminary investigation of the possible correlation of temporal and energy release patterns of seismic activity involving the preparation processes of consecutive sizeable seismic events [1,2]. The background idea is that during periods of low-level seismic activity, stress processes in the crust accumulate energy at the seismogenic area whilst larger seismic events act as a decongesting mechanism releasing considerable energy [3,4].
A dynamic algorithm is being developed aiming to identify and cluster pre- and post- seismic events to the main earthquake following on research carried out by Zubkov [5] and Dobrovolsky [6,7]. This clustering technique along with energy release equations dependent on Richter’s scale [8,9] allow for an estimate to be drawn regarding the amount of the energy being released by the seismic sequence.
The above approach is being implemented as a monitoring tool to investigate the behaviour of the underlying energy management system by introducing this information to various neural [10,11] and soft computing models [1,12,13,14]. The incorporation of intelligent systems aims towards the detection and simulation of the possible relationship between energy release patterns and time-intervals among consecutive sizeable earthquakes [1,15].
Anticipated successful training of the imported intelligent systems may result in a real-time, on-line processing methodology [1,16] capable to dynamically approximate the time-interval between the latest and the next forthcoming sizeable seismic event by monitoring the energy release process in a specific seismogenic area.
Indexing terms: pattern recognition, long-term earthquake precursors, neural networks, soft computing, earthquake occurrence intervals
References
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[2] Eneva M. and Ben-Zion Y.: ‘Techniques and parameters to analyze seismicity patterns associated with large earthquakes’, Geophysics Res., vol. 102, pp. 17785-17795, 1997a
[3] Habermann R. E.: ‘Precursory seismic quiescence: past, present and future’, Pure Applied Geophysics, vol. 126, pp. 279-318, 1988
[4] Matthews M. V. and Reasenberg P. A.: ‘Statistical methods for investigating quiescence and other temporal seismicity patterns’, Pure Applied Geophysics, vol. 126, pp. 357-372, 1988
[5] Zubkov S. I.: ‘The appearance times of earthquake precursors’, Izv. Akad. Nauk SSSR Fiz. Zemli (Solid Earth), No. 5, pp. 87-91, 1987
[6] Dobrovolsky I. P., Zubkov S. I. and Miachkin V. I.: ‘Estimation of the size of earthquake preparation zones’, Pageoph, vol. 117, pp. 1025-1044, 1979
[7] Dobrovolsky I. P., Gershenzon N. I. And Gokhberg M. B.: ‘Theory of electrokinetic effects occurring at the final stage in the preparation of a tectonic earthquake’, Physics of the Earth and Planetary Interiors, vol. 57, pp. 144-156, 1989
[8] Richter C. F.: ‘Elementary Seismology’, W.H.Freeman and Co., San Francisco, 1958
[9] Choy G. L. and Boatwright J. L.: ‘Global patterns of radiated seismic energy and apparent stress’, Journal of Geophysical Research, vol. 84 (B5), pp. 2348-2350, 1995
[10] Haykin S.: ‘Neural Networks’, 2nd Edition, Prentice Hall, 1999
[11] Jang J., Sun T. and Mizutany E.: ‘Neuro-fuzzy and soft computing’, Prentice Hall, Upper Saddle River, NJ, 1997
[12] Konstantaras A., Varley M.R., Vallianatos F., Collins G. and Holifield P.: ‘Detection of weak seismo-electric signals upon the recordings of the electrotelluric field by means of neuron-fuzzy technology’, IEEE Geoscience and Remote Sensing Letters, vol. 4 (1), 2007
[13] Konstantaras A., Varley M.R., Vallianatos F., Collins G. and Holifield P.: ‘Neuro-fuzzy prediction-based adaptive filtering applied to severely distorted magnetic field recordings’, IEEE Geoscience and Remote Sensing Letters, vol. 3 (4), 2006
[14] Maravelakis E., Bilalis N., Keith J. and Antoniadis A.: ‘Measuring and Benchmarking the Innovativeness of SME’s: a three dimensional Fuzzy Logic Approach’, Production Planning and Control Journal, vol. 17 (3), pp. 283-292, 2006
[15] Bodri B.: ‘A neural-network model for earthquake occurrence’, Geodynamics, vol. 32, pp. 289-310, 2001
[16] Skounakis E., Karagiannis V. and Vlissidis A.: ‘A Versatile System for Real-time Analyzing and Testing Objects Quality’, Proceedings-CD of the 4th International Conference on “New Horizons in Industry, Business and Education” (NHIBE 2005), Corfu, Greece, pp. 701-708, 2005 |
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