Application of statistical models to geoelectric variations with a long electrode span to detect anomalous changes

Abstract

We examined two techniques for eliminating components induced by variations of the geomagnetic field to accurately and quickly detect anomalous changes in the geoelectric field, which would enable us to identify crustal signals,. One method is based on a multiple regression model, where any geoelectric variation is presented as a linear combination of the past, present, and future values of geomagnetic variations. The induced geoelectric variation is estimated as a convolution of the geomagnetic variations and the parameters of the obtained regression model. The other method uses BAYTAP-G, by which the observed geoelectric data are separated into four components: 1) tidal, 2) electromagnetically induced, 3) trend, and 4) irregular components. Signals due to volcanic and/or seismic activity may be detected since the trend and the irregular component indicate variations of the self-potential of the crust and/or noise, provided that the amplitudes exceed that of the typical variation. The geomagnetic field at the Kakioka Magnetic Observatory was used as associated data for both methods. These methods were applied to geoelectric variations in the Numazu group, which are contaminated by artificial noise but show anomalous changes. The multiple regression method can apparently eliminate daily variations and telluric storms and can clarify anomalous changes that are not obvious in the original data. However, the gain and phase characteristics calculated from the estimated parameters of the model do not yield information on the underground resistivity structure since the estimated impulse responses do not correctly reflect relationships between the inducing geomagnetic field and the induced geoelectric field due to the presence of excessive artificial noise. BAYTAP-G separates daily variations due to artificial noise as the tidal component and the responses to geomagnetic variations. Anomalous changes are identified in the trend and the irregular component.We investigated the relationship of the tidal component of geoelectric variations estimated by BAYTAP-G with the tidal current and the tide. The correlation between the tidal component of geoelectric variations and tidal currents could not be clarified since simultaneous data of tidal currents with geoelectric variations were not available. Temporal variations of the amplitudes of geoelectric variations of KSM-MTO and tides at OAR for O1 and M2 constituents were not correlated despite their high coherencies.