Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region |
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| Authors: | Jamileh Vasheghani Farahani Mehdi Zare Caro Lucas |
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| Institution: | (1) International Institute of Earthquake Engineering and Seismology, Tehran, Iran;(2) Faculty of Electrical and Computer Engineering, Control and Intelligent Processing Center of Excellence, University of Tehran, Tehran, Iran |
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| Abstract: | Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported
in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect
seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features
as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289
signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are
almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of
Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event,
distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg
wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes
and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results
confirmed that the proposed ANFIS model has good potential for determining seismic events. |
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