Three-dimensional basin basement boundary inversion of gravity anomalies based on EfficientNetV2 network
Author:
Affiliation:
1. Chengdu University of Technology
2. Changchun University of Science and Technology
Publisher
Society of Exploration Geophysicists and Chinese Geophysical Society
Link
https://library.seg.org/doi/pdf/10.1190/GEM2024-077.1
Reference9 articles.
1. Alberto and M. J. G. J. International, 2002, Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem.
2. GAO, X.H. and D.N. J. C. J. o. G. HUANG, 2017, Research on 3D focusing inversion of gravity gradient tensor data based on a conjugate gradient algorithm. 60(4): 1571-1583.
3. He, S., H. Cai, S. Liu, J. Xie and X. J. J. o. g. r. S. e. J. Hu, 2021, “Recovering 3D Basement Relief Using Gravity Data Through Convolutional Neural Networks.” (10): 126.
4. Qin, Pengbo, Huang, Danian, Liu, Jie, Yuan, Geng and M. J. J. o. A. Geophysics, 2016, Integrated gravity and gravity gradient 3D inversion using the non-linear conjugate gradient.
5. Tan, M. and Q. V. Le, 2021, EfficientNetV2: Smaller Models and Faster Training.
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