Efficient Reduction of Powerline Signals in Magnetic Data Acquired From a Moving Platform

High-bandwidth magnetic data are normally distorted by ubiquitous 50- or 60-Hz noise from powerlines and similar sources. The powerline noise can be orders of magnitude larger than the magnetic signal from targets and must be culled from data sets prior to interpretation. Suppression of the powerline noise by simple filtering can result in artifacts and an unacceptable reduction in resolution of ground-based and unmanned aerial vehicle magnetic surveys. Removal approaches such as those based on Biot–Savart modeling are sensitive to the estimated position of the powerline systems, in addition to their limited applicability due to the requirement of a DC source. Moreover, the powerline noise in data acquired from a moving platform is inherently nonstationary and removal techniques must be specifically developed with this in mind. We propose a model-based method that does not rely on a priori knowledge of the powerline system by fitting and subtracting a set of sinusoids to the data. These sinusoids are computed on small windows of data, tied together with regularization terms within the fitting process to reduce discontinuities between segments. We further incorporate powerline frequency as a nonlinear parameter, allowing for fluctuations in the fundamental frequency as loads on the power grid change. Through synthetic and field examples, we show that periodic noise can be reliably removed automatically without the need for filtering or significant alterations of the frequency content. Powerline noise is reduced by over 98% in the field example.