LAUSR

The predominant signals of audio-frequency magnetotellurics (AMT) are called sferics, and they are generated by global lightning activity. When sferic signals are small or infrequent, measurement noise in electric and magnetic fields causes errors in estimated apparent resistivity and phase curves, leading to great model uncertainty. To reduce bias in apparent resistivity and phase, we use a global propagation model to link sferic signals in time series AMT data with commercially available lightning source information including strike time, location, and peak current. We then investigate relationships between lightning strike location, peak current, and the quality of the estimated apparent resistivity and phase curves using the bounded influence remote reference processing code. We use two empirical approaches to preprocessing time-series AMT data before estimation of apparent resistivity and phase: stitching and stacking (averaging). We find that for single-site AMT data, bias can be reduced by processing sferics from the closest and most powerful lightning strikes and omitting the lower amplitude signal-deficient segments in between. We hypothesized that bias can be further reduced by stacking sferics on the assumptions that lightning dipole moments are log-normally distributed whereas the superposed noise is normally distributed. Due to interference between dissimilar sferic waveforms, we tested a hybrid stitching-stacking approached based on clustering sferics using a wavelet-based waveform similarity algorithm. Our results indicate that the best approach to reduce bias was to stitch the closest and highest amplitude data.