Application of Multitask Learning for 2-D Modeling of Magnetotelluric Surveys: TE Case

In this article, multitask learning is applied to forward modeling of 2-D magnetotellurics (MT) to predict the apparent resistivity and impedance phase of MT data. Multitask learning can learn multiple objectives simultaneously based on the shared representation, thereby improving efficiency and accuracy. The loss function is carefully designed by weighing multiple objective functions based on homoscedastic uncertainty, and the structural similarity regularization term is applied to ensure the texture of the obtained apparent resistivity and impedance phase. The proposed convolutional neural network can make accurate predictions with an average relative error of apparent resistivity and impedance phase less than 1.2% and 0.2%, respectively. The generalization ability of the proposed network is verified by applying it to cases with more complex resistivity distributions than training samples. This article shows the potential for fast and accurate computation of two highly correlated physical quantities in electromagnetic fields.