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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.

Mn = log () + 2.50 log (d)-1.8 106 > d ≤ 600 where ν is the maximum amplitude in nm s 1 − , and d is the epicentral distance in km. There were 494 aftershocks in the zone with M ≥ 2.5 recorded by Broadband Iranian Network (BIN). In the present study, we analyze the 11 August 2012 earthquakes and review the seismicity in the last century within the limits of 36.5°and 39.5° N latitude; and 45.0°and 48.5°E longitude. The earthquakes' source parameters are obtained from spectral studies.