Imaging of Earth’s interior has led to a large number of successful discoveries of plausible structures and associated geophysical processes. However, due to the limitations of geophysical data, Earth imaging has many trade‐offs between the underlying features, and most approaches apply smoothing to reduce the effect of such trade‐offs. Unfortunately, this smoothing often results in blurry images that are not clear enough either to infer the geologic processes of interest or to make quantitative inferences about the various geologic properties. Here, we first summarize some of the basic issues that make Earth imaging so difficult and explain how Earth imagers must choose between more open‐ended discovery‐oriented goals and more specific, scientific‐inference‐oriented goals. We discuss how the choice of the optimal imaging framework depends crucially on the desired goal, and particularly on whether plausible discovery or inference is the desired outcome. We argue that as Earth imaging has become more mature, sufficiently many plausible structures have been imaged that it is becoming more crucial for Earth imaging to serve the inference goal and would benefit from an inference‐oriented imaging framework, despite the additional challenges in posing imaging problems in this manner. Examples of inference‐oriented imaging frameworks are provided and contrasted with discovery‐oriented frameworks. We discuss how the success of the various frameworks depends critically on the data quality and suggest that a careful balance must be struck between the ambition of the imager and the reality of the data. If Earth imaging is to move beyond presenting qualitatively plausible structures, it should move toward making quantitative estimates of the underlying geologic processes inferred through a self‐consistent framework.

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