Geophysical inversion methods are used as part of an interpretation process that seeks to differentiate geologic units. To improve the reliability of geologic differentiation based on recovered images from geophysical inversions, we have developed a multidomain clustering inversion algorithm that can incorporate statistical petrophysical data into a deterministic geophysical inversion framework through the use of the fuzzy c-means clustering technique. Petrophysical data are treated in the parameter domain in the same way that geophysical data are treated in the spatial domain, and these two different types of data are simultaneously inverted in their respective domains through the minimization of a single common objective function. The resulting physical property model honors the geophysical and petrophysical data and therefore can represent the earth better than geophysical inversion models that solely honor the geophysical data. Geophysical inversion and geology differentiation are generally treated as two independent procedures and applied in a serial manner. In our inversion method, we integrate these two components into a unified scheme, within which they interact with each other and are mutually enhanced. We produce a geologic map showing the distribution of geologic units as a direct and integral part of the geophysical inversion. We tested the algorithm using two synthetic examples and a field data example.