Deep Learning Inversion for Multivariate Magnetic Data

• Published in: IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-10
• Main Authors: Shi, Xiaoqing, Jia, Zhuo, Geng, Hua, Liu, Shuang, Li, Yinshuo
• Format: Article
• Language: English
• Subjects: 3-D convolution; 3-D magnetic inversion; Accuracy; Convolution; Data models; Deep learning; Feature extraction; Information processing; Inverse problems; Inversion; Machine learning; Magnetic anomalies; Magnetic data; Magnetic domains; Mathematical models; Multivariate analysis; multivariate data; Neural networks; Solid modeling; Spatial data; Tensors; Three dimensional models; Three-dimensional displays; Training
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3337413

Three-dimensional inversion of magnetic data can obtain the distribution of subsurface magnetic targets. Deep learning is an effective way to achieve 3-D inversion, which trains a neural network to learn the features of magnetic anomaly data and then generates a 3-D model based on these features. Large training samples are required to achieve persuasive results due to the limited observational data and the multisolution nature of the inverse problem. To reduce the nonuniqueness of the inversion, this article proposes a multivariate magnetic data-based deep learning 3-D inversion strategy. With the proposed strategy, more domain knowledge is incorporated into the training data of the neural network to improve the inversion accuracy. The input data of the neural network adopt multivariate observation data, including multiscale data and multitype data such as magnetic three-component data, magnetic gradient tensor data, and so on, and output a 3-D model to realize 3-D to 3-D mapping. Then, the neural network structure uses the 3-D convolution to extract 3-D spatial information. Both tests on simulation and measured data verify that the proposed strategy can effectively improve the accuracy of the 3-D magnetic inversion.