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With the vector reflectivity-based acoustic wave equation, we can simultaneously obtain a high-resolution velocity model and the corresponding image using Full waveform inversion (FWI) imaging. However, the inversion process is quite non-linear and the field data is often blurred by noise, which makes the inversion process challenging. Thus, we propose an implicit joint imaging inversion (IJII) workflow, where the velocity and reflectivity models are implicitly represented by the weights of a neural network and can be resampled from the neural network at desired high resolution or even on irregular grids. The synthetic and field data (will be shown at the conference) examples show that the proposed method can recover high-resolution velocity models and migration images as an effective way to perform FWI imaging.