Resolution is a key feature of borehole image logs, but characterization of resolution is a nontrivial task. In practice, resolution is often determined from the apparent thickness of the thinnest features on an image log. However, in general, this approach is unreliable because the finest features do not necessarily correspond to the geology. They simply may be caused by tool artifacts such as electrode edge effects or electronic artifacts. In this paper, we characterize image resolution with an alternative time-series analysis approach.Time-series analysis has previously been applied to conventional log (e.g., gamma ray) data to characterize vertical resolution. We applied this approach, with certain modifications, to borehole image log data. The premise is that real geological features and tool artifacts can be differentiated with coherence analysis because they generally have different spatial coherence characteristics. This study expands the coherence analysis method to statistically quantify the image resolution. The method calculates the coherence between each and every pair of neighbouring image traces. By defining a 95% confidence level, a cutoff spatial frequency can be found. The vertical resolution of the image log is inversely proportional to the cut-off frequency. We illustrate this approach with data examples collected with the EARTH ImagerSM tool designed for oil-based muds.To supplement and confirm the coherence resolution analysis, we performed a 3D finite-difference modeling study of an oil-based mud imaging tool. We simulated the image responses to various beds of different thicknesses and resistivities. By considering the tool geometry in detail, we were able to quantify the true resolution of the tool. The modeling results confirmed the coherence analysis

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