We provide analytical and computational characterizations on the general inverse data matching problems based on the quadratic Wasserstein distance under both deterministic and Bayesian approaches. We show that the quadratic Wasserstein metric has a "smoothing" effect on the inversion process, making it very robust against high-frequency noise in the data but leading to a reduced resolution for the reconstructed objects at a given noise level.