An Intelligent MT Data Inversion Method With Seismic Attribute Enhancement

Abstract

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