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Transient electromagnetics (TEM) is a well-established method for delineation of resistivity structures in the subsurface. New applications of TEM include time-lapse monitoring where measurements repeated daily or weekly can be used to track variations in groundwater levels, groundwater-saltwater interfaces, or contamination plumes. However, the small changes in resistivity caused by these dynamic processes are embedded in resistivity profiles from the geological background. Therefore, time-lapse TEM monitoring requires high signal-to-noise ratio data. Recent work has shown that the ubiquitous interference from very low frequency radios can act as a coherent noise source that is difficult to suppress by standard method but that it can be mitigated by modelling and subtraction. Here we investigate this concept in the context of time-lapse monitoring where data records typically consist of very short time segments at high repetition rate interleaved with gaps. This can pose challenges for modelling, but we demonstrate with synthetic data that algorithms are robust even in this case.