<p>We propose the use of mutual information (MI) between airborne and corresponding surface gravity data as a comprehensive measure of the overall quality of airborne gravity measurements and their processing methods. MI is an information-theoretic measure that quantifies the similarity between datasets, capturing both linear and nonlinear dependencies. Unlike correlation coefficients, MI reflects the total shared information, making it particularly useful in classification and optimization tasks. In this study, we view airborne gravity data as an “airborne image” and its downward continued counterpart, potentially merged with surface data, as a “surface image.” Using both synthetic and real-world datasets, we demonstrate that high MI values of these two images point to similarity which corresponds to high-quality, consistent data and processing. Conversely, errors or incompatible processing methods degrade similarity, reducing MI. Our analysis of three adjacent airborne surveys reveals that MI mapping effectively distinguishes between varying data and processing quality, identifying the most consistent survey. MI thus offers a powerful quality control metric for airborne gravity applications.</p>