Abstract
Unsupervised learning techniques provide a way of investigating scientific data based on automated generation of statistical models. Because these techniques are not dependent on a priori information, they provide an unbiased method for separating data into distinct types. Thus they can be used as an objective method by which to identify data as belonging to previously known classes or to find previously unknown or rare classes and subclasses of data. Hidden Markov model based unsupervised learning methods are particularly applicable to geophysical systems because time relationships between classes, or states of the system, are included in the model. We have applied a modified version of hidden Markov models which employ a deterministic annealing technique to scientific analysis of seismicity and GPS data from the southern California region. Preliminary results indicate that the technique can isolate distinct classes of earthquakes from seismicity data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baum, L. E. (1972), An Inequality and Associated Maximization Technique in Statistical Estimation for Probabilistic Functions of Markov Processes, Inequalities 3, 1-8.
Baum, L. E. and Egon, J. A. (1967), An Inequality with Applications to Statistical Estimation for Probabilistic Functions of a Markov Process and to a Model for Ecology, Bull. Am. Meteorol. Soc. 73, 360-363.
Baum, L. E. and Petric, T. (1966), Statistical Inference for Probabilistic Functions of Finite State Markov Chains, Ann. Math. Stat. 37, 1554-1563.
Baum, L. E. Pe-Erie, T., Soules, G., and Weiss, H. (1970), A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains, Ann. Math. Soc. 41, 164-171.
Baum, L. E. and Sell, G. R. (1968), Growth Functions for Transformations on Manifolds, Pac. J. Math. 27, 211-227.
Briggs, P., Press, F., and Guberman, S. A. (1977), Pattern Recognition Applied to Earthquake Epicenters in California and Nevada, Geol. Soc. Am. Bull. 88, 161-173.
Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977), Maximum Likelihood from Incomplete Data via the EM Algorithms, J. Roy. Stat. Soc. 39, 1-38.
Duda, R. O. and Hart, P. E., Pattern Classification and Scene Analysis (John Wiley and Sons, New York, 1973).
Fayyad, U. M., Djorgovski, S. G., and Weir, N. (1996), From Digitized Images to Online Catalogs —Data Mining a Sky Survey. AI Mag. 17, 51-66.
Fayyad, U. M. and Smyth, P. (1999), Cataloging and Mining Massive Datasets for Science Data Analysis, J. Comput. Graph. Stat. 8, 589-610.
Fukunaga, K., Introduction to Statistical Pattern Recognition (Academic Press, New York, 1990).
Press, F. and Allen, C. (1995), Patterns of Seismic Release in the Southern California Region, J. Geophys. Res. 100, 6421-6430.
Rabiner, L. R. (1989), A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, P. IEEE 77, 257-286.
Reasenberg, P. (1985), Second-order Moment of California Seismicity, 1969-1982, J. Geophys. Res. 90, 5479-5496.
Stolorz, P. and Cheeseman, P. (1998), Onboard Science Data Analysis: Applying Data Mining to Science-directed Autonomy, IEEE. Intell. Syst. App. 13, 62-68.
Ueda, N. and Nakano, R. (1998), Deterministic Annealing EM Algorithm, Neural Networks 11, 271-282.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer Basel AG
About this chapter
Cite this chapter
Granat, R., Donnellan, A. (2002). A Hidden Markov Model Based Tool for Geophysical Data Exploration. In: Matsu’ura, M., Mora, P., Donnellan, A., Yin, Xc. (eds) Earthquake Processes: Physical Modelling, Numerical Simulation and Data Analysis Part II. Pageoph Topical Volumes. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-8197-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-0348-8197-5_7
Publisher Name: Birkhäuser, Basel
Print ISBN: 978-3-7643-6916-3
Online ISBN: 978-3-0348-8197-5
eBook Packages: Springer Book Archive