Abstract
Geophysics research has been faced with a growing need for automated techniques with which to process large quantities of data. A successful tool must meet a number of requirements: it should be consistent, require minimal parameter tuning, and produce scientifically meaningful results in reasonable time. We introduce a hidden Markov model (HMM)-based method for analysis of geophysical data sets that attempts to address these issues. Our method improves on standard HMM methods and is based on the systematic analysis of structural local maxima of the HMM objective function. Preliminary results of the method as applied to geodetic and seismic records are presented.
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© 2003 Springer-Verlag Berlin Heidelberg
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Granat, R.A. (2003). A Method of Hidden Markov Model Optimization for Use with Geophysical Data Sets. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J.J., Zomaya, A.Y. (eds) Computational Science — ICCS 2003. ICCS 2003. Lecture Notes in Computer Science, vol 2659. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44863-2_88
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DOI: https://doi.org/10.1007/3-540-44863-2_88
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