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Both large and small earthquakes rupture in complex ways. However, microearthquakes are often simplified as point sources and their rupture properties are challenging to resolve. We leverage seismic wavefields recorded by a dense array in Oklahoma to image microearthquake rupture processes. We construct machine-learning enabled catalogs and identify four spatially disconnected seismic clusters. These clusters likely delineate near-vertical strike-slip faults. We develop a new approach to use the maximum absolute SH-wave amplitude distributions (S-wave wavefields) to compare microearthquake rupture processes. We focus on one cluster with earthquakes located beneath the dense array and have a local magnitude range of -1.3 to 2.3. The S-wave wavefields of single earthquakes are generally coherent but differ slightly between the low-frequency (<12 Hz) and high-frequency (>12 Hz) bands. The S-wave wavefields are coherent between different earthquakes at low frequencies with average correlation coefficients greater than 0.95. However, the wavefield coherence decreases with increasing frequency for different earthquakes. This reduced coherence is likely due to the rupture differences among individual earthquakes. Our results suggest that earthquake slip of the microearthquakes dominates the radiated S-wave wavefields at higher frequencies. Our method suggests a new direction in resolving small earthquake source attributes using dense seismic arrays without assuming a rupture model.