Discrimination between Small Earthquakes and Local Quarry Blasts Using Committee Machine

A combination of multiple discrimination artificial neural networks using different seismic source parameters is suggested using a committee machine. In this work, a committee machine was used to combine supervised and unsupervised artificial neural networks to discriminate between earthquakes and quarry blasts using data from the Egyptian National Seismological Network (ENSN). The unsupervised network is used as a measure of accuracy for the results of the supervised neural network. The unsupervised Self-Organized Map (SOM) and the k-means clustering algorithms are used to estimate support and confidence measures for the results. Meanwhile, the supervised neural network is used to discriminate between earthquakes and explosions. The artificial neural networks are trained using different input parameters which are the P wave spectrum corner frequency (PcF), S wave corner frequency (ScF), and the ratio (Rcf) of PcF to Scf. The combined approach succeeds to discriminate between earthquakes and quarry blasts in Northern Egypt. The method provides the results with a measure of confidence which eliminates false discrimination. The current paper represents an idea to implement artificial intelligence to assist experts in decision-making situations. The committee machine could identify the nature of a particular event, using the aid of several discrimination methods. The proposed committee machine could combine the results of several algorithms and expert opinions to form one single output with a confidence measure.