A method is described for filtering magnetotelluric (MT) data in the wavelet domain that requires a minimum of human intervention and leaves good data sections unchanged. Good data sections are preserved because data in the wavelet domain is analyzed through hierarchies, or scale levels, allowing separation of noise from signals. This is done without any assumption on the data distribution on the MT transfer function. Noisy portions of the data are discarded through thresholding wavelet coefficients. The procedure can recognize and filter out point defects that appear as a fraction of unusual observations of impulsive nature either in time domain or frequency domain. Two examples of real MT data are presented, with noise caused by both meteorological activity and power-line contribution. In the examples given in this paper, noise is better seen in time and frequency domains, respectively. Point defects are filtered out to eliminate their deleterious influence on the MT transfer function estimates. After the filtering stage, data is processed in the frequency domain, using a robust algorithm to yield two sets of reliable MT transfer functions.

The MT interpretation procedure begins with a set of sounding data in the frequency domain. The overall quality of these data can be variable both as a function of frequency and location. Many simple interpretation procedures, such as the assessment of static distortion, act directly on the sounding data. A number of response characteristics, such as the location (in frequency) and number of turning points, are important to the interpretation. Localised scatter (noise) in the response estimates can produce false gradients which degrade the quality of the inferences made from the data.This study considers how the D+ solution can be used to process the raw sounding data to provide a number of interpretational advantages. Although the D+ solution has strict formal roots in I D inverse theory, it is used here simply to enhance those data attributes, particularly that of physical validity, which lead to a more meaningful assessment of data characteristics. The data considered are 84 broadband array soundings from the Parana basin, Brazil. The advantages provided by the D+ processed data set are demonstrated by using the raw and processed data in two main interpretational procedures. The first procedure concerns the ability of the data to provide quantitative assessments of the influence of static distortion. The second procedure concerns the application of transform methods which attempt to recover a resistivity I depth or reflectivity profile directly from the sounding data.

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