Magnetotelluric inversion based on an improved residual network

Traditional inversion techniques rely on initial models and exhibit prolonged inversion times. This study proposed a magnetotelluric inversion method based on an improved residual network. Specifically, geoelectric models of varying shapes and resistivity values were established, and apparent resistivity data were obtained using the TM mode, forming a dataset. Then, a novel inversion network-iResNet (an improved residual network)-was established by improving classic residual network ResNet, and the new network was trained using the afore-mentioned data set. Finally, the apparent resistivity data were input to trained network, directly producing inversion results. The experimental results demonstrate that the method proposed in this study can accurately determine the positions, shapes, and resistivity values of the geoelectric models through swift inversion, suggesting high generalization and anti-noise capabilities. Therefore, this method can effectively deermine measured magnetotelluric data.