VERY LONG PERIOD MAGNETOTELLURICS AT TUCSON-OBSERVATORY - ESTIMATION OF IMPEDANCES

Eleven years (1932-1942) of electric potential and magnetic measurements at the Tucson observatory represent a unique very long period magnetotelluric (MT) data set. We report here on a careful reanalysis of this data using modem processing techniques. We have developed and used novel methods for separating out the quasi-periodic daily variation fields and for cleaning up outliers and filling in missing data in the time domain. MT impedance tensors, estimated using die cleaned and filled data and using robust frequency domain methods, are well determined and smoothly varying for periods between 4 hours and 10 days. At longer periods the electric field data are swamped by large-amplitude incoherent noise, particularly after the third year of the experiment. Although we find no evidence for contamination of any field components by oceanic motional induction at tidal periods, the MT impedance estimates do show evidence of small systematic biases due to finite spatial scale geomagnetic sources at harmonics of the daily variation period. These periods are thus removed from the time series and not used in further analysis. We show that the resulting impedance tensor is well modeled by a real, frequency-independent distortion of a scalar impedance, which is consistent with non-inductive distortion of the electric fields by local surface geology. To estimate the undetermined static shift of the MT impedance, we compare the long-period MT results to equivalent MT impedances determined from 46 years of geomagnetic data. Combining the geomagnetic and undistorted MT impedances results in scalar impedance estimates for periods 0.17 < T < 91 days of unprecedented precision. However, for periods less than one day, the phase and amplitude of this impedance, while individually consistent, are not mutually consistent with any one-dimensional conductivity distribution. The inconsistency probably results from a combination of subtle multidimensional effects and systematic biases.