A Hidden Markov Model Based Tool for Geophysical Data Exploration

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.