ROBUST STATISTICAL-METHODS FOR REDUCING SFERICS NOISE CONTAMINATING TRANSIENT ELECTROMAGNETIC MEASUREMENTS

The transient electromagnetic (TEM) method is used extensively for min eral exploration and other applications such as geothermal soundings, oil exploration, groundwater pollution, soil salinity and geological m apping. Sferics pulses produced by lightning strokes propagating in th e ionosphere-earth waveguide cavity induce noise in a bandwidth of a f ew Hz to tens of kHz. The usual method of stacking and calculating the mean of a given stack cannot effectively reduce the spike-like noise induced by high-amplitude sferics pulses. To reduce this type of noise , a number of different ways of stacking data were investigated and co mpared. Noise data were stacked by using robust estimators such as the median, trimmed mean, and a range of M-estimators. Since storage of a ll the samples of a given stack can take up a prohibitively large amou nt of microprocessor memory, recursive algorithms for the M-estimators and their standard error were developed for the real-time reduction o f sferics pulses. The recursive algorithms have been demonstrated to w ork effectively on windowed data, and thus the memory normally require d to obtain the mean is sufficient for calculation of the M-estimate. In the recursive calculation of the robust estimate of the transient r esponse, the spread of the background noise distribution (known as the scale of the data) needs to be known or calculated. In the algorithm that has been developed, the scale of the data is derived from a noise run carried out before pulsing the transmitter loop with current. It has been assumed that the presence of a signal does not change the sca le of the data. This value of the scale of the data has been used to o btain a robust estimate of the transient response itself. To allow for possible changes in the background noise level during a given survey, the estimate of the scale of the data is updated throughout the surve y.Many tests of the performance of the recursive algorithms have been carried out with both simulated noise data and sferics data that have been recorded previously on magnetic tape. The results show that for s ferics activity as high as that observed in northern latitudes of Aust ralia in summer, a noise reduction by a factor of about 5 (when compar ed with simple stacking) should be obtained. In areas where sferics no ise predominates over geological background signal, such a reduction s hould lead to an increase in target detection depth by approximately 5 0%.