We have developed two inversion workflows that sequentially invert conductivity and susceptibility models from a frequency-domain controlled-source electromagnetic data set. Both workflows start with conductivity inversion using electromagnetic (EM) kernel and out-of-phase component data, which is mainly sensitive to conductivity, and then we adopt the susceptibility inversion using in-phase component data. The difference between these two workflows is in the susceptibility inversion algorithm: One uses an EM kernel and a conductivity model as the input model; the other uses a magnetostatic kernel and a conductivity model to generate the appropriate input data. Because the appropriate input data for magnetostatic inversion should not contain the EM induction effect, the in-phase induction effect is simulated through the conductivity model obtained by inverting out-of-phase data and subtracting them from observed in-phase data to generate an “induction-subtracted” in-phase data set that becomes input data for magnetostatic inversion. For magnetostatic inversion, we used a linear magnetostatic kernel to enable rapid computation. Then, we applied the two inversion workflows to a field data set of a DIGHEM survey, and we successfully reconstructed the conductivity and susceptibility models from each workflow using two zones within the data sets, in which conductive and susceptible anomalies were present. One important finding is that the susceptibility inversion results obtained from two different workflows are very similar to each other. However, computational time can be significantly saved with linear magnetostatic inversion. We found out how the results of the conductivity and susceptibility models could be well-imaged using a sequential inversion workflow and also how magnetostatic inversion could be used efficiently for airborne EM data inversion.