The Fourier transform of controlled-source time-domain electromagnetic data by smooth spectrum inversion

In controlled-source electromagnetic measurements in the near zone or at lo w frequencies, the real tin-phase) frequency-domain component is dominated by the primary field. However, it is the imaginary (quadrature) component t hat contains the signal related to a target deeper than the source-receiver separation. In practice, it is difficult to measure the imaginary componen t because of the dominance of the primary field. In contrast. data acquired in the time domain are more sensitive to the deeper target owing to the ab sence of the primary field. To estimate the frequency-domain responses reli ably from the time-domain data, we have developed a Fourier transform algor ithm using a least-squares inversion with a smoothness constraint (smooth s pectrum inversion). In implementing the smoothness constraint as a priori i nformation, we estimate the frequency response by maximizing the a posterio ri distribution based on Bayes' rule. The adjustment of the weighting betwe en the data misfit and the smoothness constraint is accomplished by minimiz ing Akaike's Bayesian Information Criterion (ABIC). Tests of the algorithm on synthetic and field data for the long-offset transient electromagnetic m ethod provide reasonable results. The algorithm can handle time-domain data with a wide range of delay times, and is effective for analysing noisy dat a.