AN EFFECTIVE EVALUATION FUNCTION FOR ICA TO SEPARATE TRAIN NOISE FROM TELLURIC CURRENT DATA
Mika Koganeyama, Sayuri Sawa, Hayaru Shouno · 2003
Irregular changes of electric currents called Seismic Electric Signals (SESs) are often observed in Telluric Current Data (TCD). Recently, detection of SESs in TCD has attracted notice for shortterm earthquake prediction. Since most of the TCD collected in Japan is affected by train noise, detecting SESs in TCD itself is an extremely arduous job. The goal of our research is automatic separation of train noise and SESs, which are considered to be independent signals, using Independent Component Analysis (ICA). In this paper, we propose an effective ICA evaluation function for train noise considering statistic analysis. We apply the evaluation function to TCD and analyze the results. 1.