Inference of permeability distribution in an enhanced geothermal reservoir is crucial for the sustainable management of geothermal energy. However, interpreting the uneven permeability distribution in deep geothermal reservoir remains a challenging task, because in practice there are often merely one to two wells available for hydro-geophysical tests. Considering that the induced seismicity data are widely captured during reservoir stimulation in enhanced geothermal systems, a framework of tracer test data inversion with the constraint of induced seismic data is proposed for permeability imaging. Hydraulic diffusivity, representing the prior estimation of permeability, is inferred from the occurrence time of seismic event. This is followed by the determination of petrophysical model, which relates hydraulic diffusivity to permeability, by tracer test data inversion based on the Monte Carlo Markov Chain algorithm. Implementation of seismicity-constraint tracer data inversion algorithm in the Habanero enhanced geothermal system, Australia, demonstrates that the proposed inversion model allows uneven permeability estimation at field scale in shorter burn-in period and lower uncertainty, than the traditional inversion model without seismicity constraint. Using the estimated permeability in the hydrothermal model enables accurate prediction of thermal performance in a 150-day trial-production test. Results indicates that the proposed algorithm can reliably characterize the spatial distribution of permeability in deep enhanced reservoirs, based on tracer test via doublet wells.