Gravity gradiometry has been used as a geophysical tool to image salt structure in hydrocarbon exploration. The knowledge of the location, orientation, and spatial extent of salt bodies helps characterize possible petroleum prospects. Imaging around and underneath salt bodies can be challenging given the petrophysical properties and complicated geometry of salt. Methods for imaging beneath salt using seismic data exist but are often iterative and expensive, requiring a refinement of a velocity model at each iteration. Fortunately, the relatively strong density contrast between salt and background density structure pro- vides the opportunity for gravity gradiometry to be useful in exploration, especially when integrated with other geophysical data such as seismic. Quantitatively integrating multiple geophysical data is not trivial, but can improve the recovery of salt body geometry and petrophysical composition using inversion. This thesis provides two options for quantitatively integrating seismic, AGG, and petrophysical data that may aid the imaging of salt bodies. Both methods leverage and expand upon previously developed deterministic inversion methods. The inversion methods leverage seismically derived information, such as horizon slope and salt body interpretation, to constrain the inversion of airborne gravity gradiometry data (AGG) to arrive at a density contrast model. The first method involves constraining a top of salt inversion using slope in a seismic image. The second method expands fuzzy c-means (FCM) clustering inversion to include spatial control on clustering based on a seismically derived salt body interpretation. The effective- ness of the methods are illustrated on a 2D synthetic earth model derived from the SEAM Phase 1 salt model. Both methods show that constraining the inversion of AGG data using information derived from seismic images can improve the recovery of salt
