A suitable imaging condition is critical for the success of seismic imaging or source location. To understand what imaging condition to select for handling noisy data, the antinoise performance of the maximum amplitude imaging condition (MAIC), the autocorrelation imaging condition (ACIC), and the geometric mean imaging condition (GMIC) were comparatively studied. Synthetic microseismic data based on the Marmousi2 model, with different levels of synthetic Gaussian noise and field noise separately added, were used for tests. For Gaussian noise data, five signal-to-noise (S/N) ratio levels were considered, ranging from an absolutely clean level of S/N= to an extremely noisy level of S/N=0.032, in an increment of five times of the lower level of S/N. It was found that the antinoise ability of MAIC outperforms ACIC, and ACIC outperforms GMIC. This conclusion was confirmed to be valid for field noise in the further experiments performed, using 16 groups of industrial noise recordings from different areas. The statistical analysis shows these performance differences are statistically consistently significant. In terms of spatial resolution, it is the other way around; that is, GMIC outperforms ACIC, and ACIC outperforms MAIC. These suggest that in choosing a suitable imaging condition for time-reverse imaging location, one needs to consider the balance between the resolution demand and data quality requirement. If the data quality is very high, GMIC may be used to achieve a high-resolution location result. Conversely, if the data quality is poor, MAIC is a good choice for obtaining a robust location result. In between, ACIC or grouped GMIC is a proper approach to work out a balanced result for resolution demand and the noisy level provision.

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