Detection of hydrogeochemical seismic precursors by a statistical learning model
Abstract. The problem of detecting the occurrence of an earthquake precursor is faced in the general framework of the statistical learning theory. The aim of this work is both to build models able to detect seismic precursors from time series of different geochemical signals and to provide an estimate of number of false positives. The model we used is k-Nearest-Neighbor classifier for discriminating "no-disturbed signal", "seismic precursor" and "co-post seismic precursor" in time series relative to thirteen different hydrogeochemical parameters collected in water samples from a natural spring in Kamchachta (Russia) peninsula. The measurements collected are ion content (Na, Cl, Ca, HCO3, H3BO3), parameters (pH, Q, T) and gases (N2, CO2, CH4, O2, Ag). The classification error is measured by Leave-K-Out-Cross-Validation procedure. Our study shows that the most discriminative ions for detecting seismic precursors are Cl and Na having an error rates of 15%. Moreover, the most discriminative parameters and gases are Q and CH4 respectively, with error rate of 21%. The ions result the most informative hydrogeochemicals for detecting seismic precursors due to the peculiarities of the mechanisms involved in earthquake preparation. Finally we show that the information collected some month before the event under analysis are necessary to improve the classification accuracy.
