1. Joint Gravity and Magnetic Inversion Using CNNs' Deep Learning.
- Author
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Bai, Zhijing, Wang, Yanfei, Wang, Chenzhang, Yu, Caixia, Lukyanenko, Dmitry, Stepanova, Inna, and Yagola, Anatoly G.
- Subjects
ARTIFICIAL neural networks ,CONVOLUTIONAL neural networks ,INVERSION (Geophysics) ,DEEP learning ,GRAVITY ,MACHINE learning ,MAGNETIC fields - Abstract
Enhancing the reliability of inversion results has always been a prominent issue in the field of geophysics. In recent years, data-driven inversion methods leveraging deep neural networks (DNNs) have gained prominence for their ability to address non-uniqueness issues and reduce computational costs compared to traditional physically model-driven methods. In this study, we propose a GMNet machine learning method, i.e., a CNN-based inversion method for gravity and magnetic field data. This method relies more on data-driven training, and in the prediction phase after the model is trained, it does not heavily depend on a priori assumptions, unlike traditional methods. By forward modeling gravity and magnetic fields, we obtain a substantial dataset to train the CNN model, enabling the direct mapping from field data to subsurface property distribution. Applying this method to synthetic data and one-field data yields promising inversion results. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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