File:Categorizing of micro seismic events.pdf

English: 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. 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 neighborhood 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 preprocessing and separating training and testing samples, 4) generating relevant models with training samples and detecting and categorizing 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.

1. Introduction

People face a variety of natural disasters in their life such as earthquakes, floods, fires and volcanoes from the past until now (Monadi and et al, 2012; soleymani and et al, 2014). These disasters have impacted human life and imposed irreparable damage on them. It would be possible to reduce the damages and prevent from many more serious damages with correct analysis of seismic events. Various methods are used to forecast the occurrence of earthquake events such as mathematical modeling, ionosphere analysis and studying animals' behavior (Abraham, 2005; Hadjimichael and et al, 2002). Some these methods only utilize from a single feature and are not able to use different features in earthquake forecasting. Therefore, earthquake events cannot be correctly forecasted through analyzing only a feature (Shahbahrami, 2017). Nowadays, methods related to computer science can play important roles in different felids. Data mining techniques (Larose, 2014) and machine learning techniques (Zaki and Meira, 2014) can be very useful in detection and classifying micro seismic events. In the other side, boost learning is being commonly used in many applications related to data mining including forecast, detection and classification and so on and is very effective in providing optimum and desirable output. Hence in present study, micro seismic events have been detected and then classified by combining machine learning techniques including support vector machine with linear regression [9, 10], MLP Neural Network (HOU Rui, ZHANG Bi-xi, 2014) and C4.5 decision tree and (Ross Quinlan, 2014) KNN (Lio and et al, 2013). Near neighborhood. The rest of present study is as follow: the literature review of study has been provided in section 2. Section (3) presents the suggested model with a description proposed architecture. In sections (4) and (5) the results have been represented and section (6) has been allocated to conclusion and future recommendations.

2. Literature Review Kai et al (2014) utilized from particle swarm optimization algorithm (an algorithm of data mining) to forecast seismic events. They applied forecasting process on the seismic database and created a significant improvement (Cai and et al, 2014), Landerb et al (2015) investigated the relationship between sea depth, volcano and earthquake using geophysical approaches of data mining and proposed a method to forecast the occurrence of these seismic events [15]. Mark Last et al (2016) emphasized on forecasting seismic events in Israel and its neighbors. They conducted their predictions by combining time series methods and data mining approaches (LANDGREBE AND R. D. MULLER, 2015) Inshuman et al (2017) utilized from a model named MOIDIS to estimate and forecast seismicity on the ice surface. They focused on the experiments of 2015 in their study (Anshuman and et al, 2017). Asad-alah Shahbahrami et al (2017) utilized from a set of machine learning algorithms on Hazard dataset to forecast earthquake events. They concluded that SVM algorithm can forecast and classify seismic events with an acceptable accuracy.

3. Data gathering and analysis In present study, the limited experimental seismic method has been used to carry out related experiments. The used tools and devices are as follow: • The SPseise3 seismograph with sensors (geophones) connected to the device at a distance of 2.5 meters from each other. • A calibrated 20 Kg weight. • Calibrated meter. The environmental conditions of experiment included a natural environment in a land with rugged soil and asymmetric geometry and in three steps as follow: • In an area without slope • In an area with positive slope: in this step, the sensor is located in a point higher than landing level of the weight. • In an area with negative slope: in this step, the sensor is located in a point lower than landing level of the weight.

The experiment was carried out by creating hit and shake on the ground through leaving 20kg weight from heights of 0.5m, 1m and 1.5m, in a way that three times falling was performed from aforementioned heights in each of the three surfaces (without slope, positive slope and negative slope). Since three steps of experiment were carried out each of the surfaces and data were received from three sensors in each step, the total of obtained data equals to 3*3*3=27 which a sample of extracted signals has been shown in this section in diagrams. Figure (1) represents falling from a height of 0.5 m on a non-sloping surface.