Geometric-mean Reverse-time migration (GmRTM), a powerful crosscorrelation-based imaging method, generates higherresolution source images and is more robust to noise compared with conventional time-reversal imaging. The price to pay is the requirements of more accurate velocity models and much higher computational costs. A hybrid strategy can retain the advantages of both, and is often more practical than GmRTM. Yet, designing appropriate grouping strategy is not trivial. Here, we use numerical examples to test the performances of two grouping strategies (adjacent and scattered) with various group numbers. In addition to the spatial resolution, robustness to random noise is another important assessment criterion, for which we consider two distribution patterns (concentrated and scattered) of traces contaminated with strong random noise. We also evaluated their effectiveness to visualize event(s) which might not be well recorded by all receivers. Our numerical examples illustrate the respective advantages of the two grouping strategies.