Real-valued negative selection algorithm for abnormal earthquake detection

Abstract

Earthquake prediction has been a research topic for many years. Many attempts have been made to predict the behavior of earthquake. However, there is yet another field of interest that is seldom explored by the researchers, which is detecting the abnormal behavior of the earthquake. The earthquake magnitude detection studies based on the analysis of historical earthquake data assumes a temporal model. Such models describe the frequencies of occurrence of seismic events as functions of their magnitudes. The most widely used magnitude-frequency model for hazard estimation is that based on the Gutenberg-Richter inverse power law. Artificial Immune System (AIS) has been a common approach in pattern recognition, optimization and many others. However, the application of AIS in the detection of abnormal earthquake behavior is still a new and challenging experience. In this study, Real-Valued Negative Selection Algorithm (RNSA) in AIS is used to establish a model of normal behavior from the large amount of earthquake data and to detect if elements of the data set have changed from an established norm. To show the applicability of the RNSA in abnormal earthquake detection, the earthquake data are divided into several segments and tested according to the assumed normal distribution. Simulation results have revealed that the RNSA improves the performance in terms of detection rate was 87% and 57% for false alarm rate with 8 features.