physics-driven approach

We propose a coupled physics-driven and data-driven algorithm to improve standard deep learning workflow. In order to evaluate the proposed method, a 2.5D geological model including dip, fault and anisotropic formation is considered.  Comparing the inversion imaging performance of the proposed physics-driven method with the traditional classical residual network (Resnet), it shows a significant improvement in resistivity accuracy.