Abstract
Gravity inversion is an important approach for obtaining the spatial structure and physical properties of underground geological bodies based on surface information. Owing to the recent advances in deep learning, neural network-based methods have been widely used for gravity inversion. However, convolutional neural networks (CNNs) require a large number of labeled samples, and the generation of such datasets for all considered geological bodies, requiring gravity forward modeling, is expensive in terms of time and storage space. To reduce the dependence on labeled samples, a three-dimensional gravity inversion method based on a cycle-consistent generative adversarial network (Cycle-GAN) is proposed herein. This network comprises two parts: generator subnetworks and discriminator subnetworks. The generator subnetworks generate gravity forward and inversion data, while the discriminator subnetworks mainly ensure the consistency of the distribution between the generated and real data. We compared the results obtained on synthetic and real data. The findings suggest that Cycle-GAN outperforms CNNs in the inversion of underground geological bodies when using a small number of labeled samples. Furthermore, the results obtained using the proposed method on real data from the San Nicolas deposit in central Mexico are consistent with previously reported results.
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This study was financially supported by the National Natural Science Foundation of China (Grant nos. 42122025, 42374174 and 42274152).
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Qiao Shu-Bo He received his doctoral degree in astrometry and celestial mechanics from the Shanghai Observatory of the Chinese Academy of Sciences in 2011. He is currently a professor at PLA Strategic Support Force Information Engineering University and a member of the Professional Committee of Geodesy and Navigation of the Chinese Society of Surveying and Mapping. His research interests include space and physical geodesy, space datum and coordinate systems, astrometry and geodynamics.
Li Hou-pu He received his Ph.D. degree at Naval University of Engineering in 2010. He is a professor of Electrical Engineering at Naval University of Engineering. His current research mainly includes marine geodesy and geophysics.
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Qiao, SB., Li, HP., Qi, R. et al. 3D gravity inversion using cycle-consistent generative adversarial network. Appl. Geophys. (2024). https://doi.org/10.1007/s11770-024-1096-5
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DOI: https://doi.org/10.1007/s11770-024-1096-5