![]() We also output our network segmentation of pixel-wise prediction for ERT-INET and structured prediction segmentation. We developed a novel convolutional neural network (CNN) technique that comprised of a fully connected network (ERT-INET) and a fully convolutional network (ERT-UNET) both train ambiguity information of the inverted resistivity based on processing ERT datasets. The ERT-NET architecture was developed in this study to learn the parameter regression relationship between geophysical ERT datasets and subsurface models. Solving distribution resistivity inversion can be computationally challenging for two reasons: one is the significant cost of software and the other is the issue of local minima. ![]() In most cases, traditional ERT inversion problems are posed as nonlinear optimization problems. Electrical resistivity tomography (ERT) inversion has emerged as an effective method for predicting resistivity in complex geological structures. ![]()
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