Improved 3-D Joint Inversion of Gravity and Magnetic Data Based on Deep Learning With a Multitask Learning Strategy

The structural constraint is widely used to establish joint inversion of multiple geophysical data. To implement the above method, weighting parameters for different items (such as data misfit term, regularization term, and structural coupling term) should be pre-set, and global structural consistency assumption should also be made. However, these prerequisites are not easy to set very reasonable. To address the above issues, this article presents a novel deep learning (DL) network method based on a multitask learning strategy to realize joint inversion of gravity and magnetic data. Due to the automatic processing of the DL technique, this method does not consider the weighting parameters. The issue of global structural consistency assumption is addressed using a multitask learning strategy. The proposed network with a multitask learning strategy consists of five tasks. In the overall network, two tasks are used for the independent inversion, one task constructed by a gated network extracts the structural similarity information from the shared information of the independent inversion networks and generates the structural similarity model, and two tasks use the structural similarity information to constrain the independent inversion to achieve the accurate joint inversion. A synthetic example and an application of the real data from the Galinge iron-ore deposit in Qinghai Province demonstrate the effectiveness of the proposed method.