  Seismic inverse modeling is a common method in reservoir prediction and it
plays a vital role in the exploration and development of oil and gas.
Conventional seismic inversion method is difficult to combine with complicated
and abstract knowledge on geological mode and its uncertainty is difficult to
be assessed. The paper proposes an inversion modeling method based on GAN
consistent with geology, well logs, seismic data. GAN is a the most promising
generation model algorithm that extracts spatial structure and abstract
features of training images. The trained GAN can reproduce the models with
specific mode. In our test, 1000 models were generated in 1 second. Based on
the trained GAN after assessment, the optimal result of models can be
calculated through Bayesian inversion frame. Results show that inversion models
conform to observation data and have a low uncertainty under the premise of
fast generation. This seismic inverse modeling method increases the efficiency
and quality of inversion iteration. It is worthy of studying and applying in
fusion of seismic data and geological knowledge.
