Full waveform inversion (FWI) suffers from the cycle-skipping problem when the available frequency-band of data is not low enough. We apply an exponential damping to the data to generate artificial low frequencies, which helps FWI avoid cycle skipping. In this case, the least-square misfit function does not properly deal with the exponentially damped wavefield in FWI, because the amplitude of traces decays almost exponentially with increasing offset in a damped wavefield. Thus, we use a deconvolution-based objective function for FWI of the exponentially damped wavefield. The deconvolution filter includes inherently a normalization between the modeled and observed data, thus it can address the unbalanced amplitude of a damped wavefield. We, specifically, normalize the modeled data with the observed data in the frequency-domain to estimate the deconvolution filter and selectively choose a frequency-band for normalization that mainly includes the artificial low frequencies. We calculate the gradient of the objective function using the adjoint-state method. The synthetic and benchmark data examples show that our FWI algorithm generates a convergent long wavelength structure without low frequency information in the recorded data.