a modern method to improve of detecting and categorizing mechanism for micro seismic events data using boost learning system

various natural disasters such as floods, fires, earthquakes, etc. have affected human life. detection and classification of large and small earthquakes caused by natural or abnormal events have been always important to earth scientist. one of the most important research challenges in this field is the lack of an effective method for identifying and categorizing various types of seismic events at less important and important levels. based on latest achievements of data mining international institutions such as rexer-kdnugget-gartner and also newest authentic articles, svm, knn, c4.5, mlp are from most important and popular and leading classifiers in data world. therefor in present study, a boost learning system consisting support vector machine algorithms with linear regression, mlp neural network ،c4.5 decision tree and knn near neighbourhood have been utilized in a combined form to detect and categorize micro seismic events. in general, the steps involved in the proposed method are: 1) performing artificial seismic tests, 2) data gathering and analysis, 3) conducting pre-processing and separating training and testing samples, 4) generating relevant models with training samples and detecting and clustering test samples and 5) extracting a cluster with the maximum candidate using boost learning. after simulations, it was observed that the accuracy of proposed boost method to the best answer was about 6.1% higher compare to other methods and the error rate was 0.082% of recalling. accuracy of detection and classification to the best answer were also improved compare to other methods up to 2.31% and 6.34%, respectively.