Learning velocity model for complex media with deep convolutional neural networks
This work addresses velocity modeling for complex media in geophysics, representing an incremental improvement over existing methods.
The paper tackles velocity model acquisition for complex media from boundary measurements by solving the inverse problem with deep convolutional neural networks, achieving statistically significant improvements in structural similarity index measure compared to previous works.
The paper considers the problem of velocity model acquisition for a complex media based on boundary measurements. The acoustic model is used to describe the media. We used an open-source dataset of velocity distributions to compare the presented results with the previous works directly. Forward modeling is performed using the grid-characteristic numerical method. The inverse problem is solved using deep convolutional neural networks. Modifications for a baseline UNet architecture are proposed to improve both structural similarity index measure quantitative correspondence of the velocity profiles with the ground truth. We evaluate our enhancements and demonstrate the statistical significance of the results.