Super-Resolution of Seismic Velocity Model Guided by Seismic Data

Recently, a multitask learning framework named M: multitask, R: global residual skip connection structure, U: encoder–decoder structure of U-Net, D: dense skip connection structure, and SR: super-resolution (M-RUDSR) has successfully improved the accuracy of full-waveform inversion (FWI) results by enhancing the resolution of the seismic velocity model. However, M-RUDSR does not make full use of seismic data even though it contains high wavenumber information, which can help enhance the resolution of the velocity model. Moreover, the effects of employing seismic data realized by simply increasing the model’s input and output channels are limited since the seismic velocity model and seismic data are in different frequency bands. Therefore, we propose to consider super-resolution (SR) of seismic data and its edge images as supplementary auxiliary tasks of the seismic velocity model SR. Besides, the proposed method named M-RUDSRv2 improves the resolution of the seismic velocity model leveraging a three-step learning strategy. First, the model in M-RUDSRv2 is trained preliminarily on the specific data where the seismic velocity model and seismic data are in the same blurring levels. Then, the pretrained model is fine-tuned on the extensive data, where the seismic velocity model and seismic data are in various kinds of blurring levels, to achieve strong generalization ability. Finally, the fitted model focuses on improving the resolution of the seismic velocity model by adjusting the parameters in the loss function. Comparative experiments on synthetic and field data validate the superior performance of M-RUDSRv2 compared with M-RUDSR in SR of the seismic velocity model.