Acoustic Gaussian beam migration is an attractive imaging method because it is flexible with input geometry, efficient, and accurate in imaging multipath arrivals. However, one of the hurdles that this method must overcome in production processing is its extension to use multimeasurement data, as recently allowed by novel acquisition technologies. This is inevitable when the compensation of the ghost effect is best corrected within a true-amplitude imaging process, a necessity for amplitude-variation-with-offset work. For this purpose, I introduced a novel formalism for vector-acoustic imaging, based on Green’s function theory, which can remove the ghost effect and produce amplitudes on reflectors that are proportional to the reflection coefficients. I established a theoretical framework with Gaussian beam representations of Green’s functions, including the weighted beam-stacking approach that reduced the cost of computation. I extended my formulas to use the steep-descent (i.e., stationary phase) approximation. Then, I explained the impact of this approximation on the illumination and the event continuity and sharpness. I also studied the special case of acoustic imaging corresponding to using single-measurement (i.e., pressure) data. I applied the derived formulations to realistic synthetic multisensor data (North Sea) using a research code of Gaussian beam migration. The numerical examples demonstrated that I can improve the illumination of the final images and obtain wide-bandwidth reflectivity maps.