Self‐organizing diffusion‐reaction systems naturally form complex patterns under far‐from‐equilibrium conditions. A representative example is the rhythmic concentration pattern of Fe‐oxides in Zebra rocks; these patterns include reddish‐brown stripes, rounded rods, and elliptical spots. Similar patterns are observed in the banded iron formations, which are presumed to have formed in the early earth under global glaciation. We propose that such patterns can be used directly (e.g., by computer‐vision‐analysis) to infer basic quantities relevant to their formation giving information on generalized chemical gradients. Here we present a phase‐field model that quantitatively captures the distinct Zebra rock patterns based on the concept of phase separation that describes the process forming Liesegang stripes. We find that diffusion coefficients (the bulk self‐diffusivities of the native species and the mobility of the reaction product) play an essential role in controlling the appearance of regular stripe patterns as well as the transition from stripes to spots. The numerical results are carefully benchmarked with well‐established empirical spacing law, width law, timing law and the Matalon‐Packter law. Using this model, we invert for the important process parameters originating from the intrinsic material properties, the self‐diffusivity ratio and the mobility of Fe‐oxides with a series of Zebra rock samples. This study allows a quantitative prediction of the generalized chemical gradients in mineralized source rocks without intrusive measurements, providing a better intuition for the mineral exploration space. Patterns in nature are observed in disparate fields of science in biology, geology, mechanics, atmospheric physics, chemistry and others. A unifying principle to decipher those patterns is using a reaction‐diffusion approach, as employed here, when implemented in numerical simulations, can deliver a perfect match to patterns observed in nature. Here we go one step further and show that dynamic coefficients describing the concentrations and mobility of valuable species such as iron oxide can be derived from the images. The proposed algorithms are benchmarked on the Zebra banded rock in Western Australia. The broader impact of the presented work includes the development of a future exploration tool based on computer‐version, revealing high grades of iron in the prominent worldwide banded iron formations which bear similar characteristic stripes. Using a physics‐based model, our formulation also captures the interesting phenomenon of transition from bands to spots that has not been addressed before. Inversion of Cahn‐Hilliard dynamic coefficients from photographic images of Zebra rock reveals multiphysics coupled processesTransition from stripes to spots is triggered by low reaction product (here Fe‐Oxyhydroxite) mobilityApplication of computer‐vision algorithms in conjunction with inversion may allow novel geophysical exploration tools for ores Inversion of Cahn‐Hilliard dynamic coefficients from photographic images of Zebra rock reveals multiphysics coupled processes Transition from stripes to spots is triggered by low reaction product (here Fe‐Oxyhydroxite) mobility Application of computer‐vision algorithms in conjunction with inversion may allow novel geophysical exploration tools for ores