This dissertation presents a study on velocity modeling using ocean-bottom seismometer data (OBS) collected in 2004 and 2006 on the western Scotian slope. Gas hydrate and free gas concentrations and their distribution along the Scotian margin were derived based on the velocity results modeled with two different OBS data sets. A strong velocity increase (140-300 m/s) associated with gas hydrate was modeled for a depth of 220 m below seafloor (bsf). At the base of that high velocity zone (330 mbsf) the velocity decreases with 50-130 m/s. This depth is associated with the depth of the bottom-simulating reflector (BSR) observed in previous 2-D seismic reflection data. The gas hydrate concentrations (2-18 %) based on these velocities were calculated with an effective medium model. The velocity modeling shows that a sparser OBS spacing (~ 1 km) reveals more velocity uncertainties and smaller velocity contrasts than a denser (100 m) spaced OBS array. The results of the travel-time inverse modeling are applied in a waveform inverse modeling with OBS data in the second part of the thesis. The modeling tests were performed to obtain information on OBS instrument spacings necessary to detect low-concentration gas hydrate occurrences. The model runs show that an increase in instrument spacing leads to an increasing loss of model smoothness. However, large instrument spacings (>500 m) are beneficial for covering a wide target region with only using a few instruments, but decreasing the lateral resolution limits of the subsurface targets. In general half of the instrument spacing defines the lower boundary for the lateral width of the target structure. Waveform modeling with the 2006 OBS data has shown that low frequencies (<8 Hz) in the source spectrum are necessary to recover the background velocity of the model. The starting model derived from travel-time inversion of the 2006 data is not close enough to the true model. Thus the first-arrival waveforms do not match within half a cycle. Modeling with a starting frequency of 8 Hz and and applying data with a low signal-to-noise ratio (1.25) introduces artifacts into the final model result without updating the velocity. / Graduate