A recently validated technique for buried target detection relies on applying an acoustic stimulus signal to a patch of earth and then measuring its seismic (vibrational) response using a laser Doppler vibrometer (LDV). Target detection in this modality often relies on estimating the acoustic-to-seismic coupling ratio (A/S ratio) of the ground, which is altered by the presence of a buried target. For this study, LDV measurements were collected over patches of earth under varying environmental conditions using a known stimulus. These observations are then used to estimate the performance of several methods to discriminate between target and non-target patches. The first part of the study compares the performance of human observers against a set of established seismo-acoustic features from the literature. The simple features are based on previous studies where statistics on the Fourier transform of the acoustic-to-seismic transfer function estimate are measured. The human observers generally offered much better detection performance than any established feature. One weakness of the Fourier features is their inability to utilize local spatiotemporal target cues. To address these weaknesses, a novel automatic detection algorithm is proposed which uses a multi-scale blob detector to identify suspicious regions in time and space. These suspicious spatiotemporal locations are then clustered and assigned a decision statistic based on the confidence and number of cluster members. This method is shown to improve performance over the established Fourier statistics, resulting in performance much closer to the human observers.