LAUSR

Magnetotelluric (MT) data inversion reconstructs an electrical resistivity structure most compatible with the observed MT data, and static correction can remove the undesired static shift effect in MT data. Conventional MT data static shift correction often faces the challenge of demanding requirements, such as large data amount, additional types of data, or a deep understanding of the research area. MT inversion constrained by seismic data often has better resolution and model consistency compared with independent MT inversion. However, valuable inversion knowledge contained in geophysicists’ expertise is not effectively incorporated. In this work, we present an intelligent MT data inversion method leveraging data- and physics-driven techniques based on deep learning. A novel MT data static shift correction method is introduced based on a neural network (NN). An MT data inversion method is formulated with the constraint of the extracted seismic reflection image based on two different NNs. Experiments on synthetic and field data verify the effectiveness of the proposed method.