Markov Model Applied to the Earthquake Magnitude in the Aegean Region of Turkey

Earthquakes are described as seismic activity that occurred in the region and caused loss and
great destruction. While estimations of earthquakes are crucial for reducing losses, accurate assessments of
earthquakes are not achievable. Many analytical models are proposed for representing the process of
earthquake occurrence. Some researchers use empirical observations to predict earthquakes, others use
physical models to understand how earthquakes happen, and still, others use statistical analysis to study
patterns in seismicity. To estimate future environmental events realized, stochastic methods are often used
for statistical analysis. The theory of Markov processes can be applied in various fields because the Markov
property is very intuitive: if we know the past and present of a system, then the future development of the
system is only determined by its present state. So the history of the system does not play a role in its future
development. This property is called a memoryless property. In this study, the Markov chain is applied to
estimate earthquake parameters in the Aegean Region of Turkey. Magnitude classification was done and
with the use of discrete-time Markov chains, the transition probabilities of this sequence were studied.