A study on clustering for anomalous signal detections from electromagnetic wave data

Detection of an anomalous signal radiated from the earth´s crust is useful for predicting the precursor of the earthquakes. Using the Extremely Low Frequency (ELF) band, we have observed the electromagnetic (EM) wave. Various methods for detection of an anomalous signal have been proposed. The known problems for those techniques are related to a false detection due to the limitation of training data for great earthquake. We proposed the HMM based anomalous signal detection whose training data are the amplitude density distribution extracted from normal EM wave signals. However, acceptance probability in this technique is not corresponded appropriately to a change of anomalous signal. In this paper, anomalous signal detection using amplitude density distribution calculated by short term signals to track a temporal change is proposed.