Seismic events such as earthquakes are one of the most important issues in the field of geology. Meanwhile, less attention has been paid to micro-seismic events, despite the high number of earthquakes. Earthquakes, regardless of their size, affect human life; therefore, their detection and management is considered an important issue. For this purpose, experts developed seismic arrays as a system of linked seismometers. These systems equipped with sensors and seismographs are able to receive a range of waves from the earth, which are then sent to the central seismic station for analysis. So far, many tools and methods have been devised to analyze seismic data. However, the dominant method in most seismic mechanisms is trigger function, based on STA/LTA (short-time-average through long-time-average trigger). These mechanisms have considerable threshold in terms of earthquake range, so many micro-events are ignored as noise. Generally, in this field of geology, computer science techniques have been used to detect and classify these events. Statistical methods such as kurtosis, variance, and skewness can be applied to understand the changes in the signal curves of geophones in a seismic event, thereby helping in the initial detection of fuzzy features. According to the last 3 years' reports of global data mining agencies such as Rexer, KDnugget, and Gartner, Rapid Miner is one of the most popular tools for data mining in recent years. Furthermore, these institutions considered artificial neural networks, especially multilayer perceptron (MLP) and base radial function (RBF), to be among the most successful algorithms for detection and classification of stream data. In this research, the recorded data from several seismic experiments has been classified by a hybrid model. Hence, the present study was aimed to enhance the authenticity of data based on the application of effective variables. This was undertaken through use of a fuzzy method and an integrated neural network algorithm, involving MLP perceptron and radial network of RBF in the form of a collective learning system, in order to identify seismic events on a small scale. Based on the results, in comparison to basic methods, the proposed method significantly improved using the actual error and root-mean-square error (RMSE) criteria.
Keywords: Fuzzy logic; MLP; Micro-seismic events; Neural network; RBF; Seismic event detection.