The field of data mining has evolved from its roots in databases, statistics, artificial intelligence, information theory and algorithms into a core set of techniques that have been applied to a range of problems. Computational simulation and data acquisition in scientific and engineering domains have made tremendous progress over the past two decades. A mix of advanced algorithms, exponentially increasing computing power and accurate sensing and measurement devices have resulted in more data repositories. Advanced technologies in networks have enabled the communication of large volumes of data across the world.This paper aims at further data mining study on scientific data. This paper highlights the data mining techniques applied to mine for surface changes over time (e.g. Earthquake rupture). The data mining techniques help researchers to predict the changes in the intensity of volcanos. This paper uses predictive statistical models that can be applied to areas such as seismic activity , the spreading of fire. The basic problem in this class of systems is unobservable dynamics with respect to earthquakes. The space-time patterns associated with time, location and magnitude of the sudden events from the force threshold are observable. This paper highlights the observable space time earthquake patterns from unobservable dynamics using data mining techniques, pattern recognition and ensemble forecasting. Thus this paper gives insight on how data mining can be applied in finding the consequences of earthquakes and hence alerting thepublic.

Earthquake, data mining techniques, space-time patterns

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