level crossing approach in spatial and temporal dependence in earthquake

in recent years, a vast majority of researches have been devoted to the study of seismic data that contain information about the complex events that lead to the earthquake . we live in a world where random processes are ubiquitous. although the random values of a stochastic process at different times may be independent random variables, but in most cases they are considered to indicate complicated statistical correlations. so, over the past decade, several different methods have been introduced to study the properties of the process. spatial and temporal fluctuations of earthquake form time series. the purpose of the application of statistical mechanics is to describe the behavior of the time series that can help to better understanding of the stochastic processes. after that, we lie in an effort to reproduce or predict some experimental facts with extraction of useful information. an important question is what is the probability of obtaining or losing a certain level of return at different time intervals? for the first time, jenson the inverse method in turbulence, and simonsen et al. presented the inverse statistics to apply on similar financial data to answer the similar questions. inverse statistics suggests the inverting of the structure function equation, and instead of considering the average moments of distance between two points, gives the difference value between these two points. in the inverse statistics method is used of another popular technique called the level crossing method (lc). in the level crossing method no scaling feature is explicitly required and this is the main advantage of this technique for estimating the statistical information of the series. level crossing based on stochastic processes that grasp the scale dependence of the time series.