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3D gravity anomaly inversion based on LinkNet

  • Gravity Exploration Methods
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Abstract

Gravity anomaly inversion is a technique used to estimate underground density distribution using gravity data. This paper proposes a new three-dimensional (3D) gravity anomaly inversion method based on the LinkNet network. Compared with two-dimensional gravity anomaly inversion, 3D gravity anomaly inversion can determine the density distribution of the entire region below the observation surface. Additionally, compared with traditional methods, the neural network method does not require the selection of initial parameters, and several predictive models can be quickly sought during the prediction stage. The Tversky loss was used to improve the inversion accuracy of the boundary. By comparing the inversion of the fully convolutional network, UNet network, and LinkNet network proposed in this paper on simulated data, it was observed that the model reconstruction error obtained using the LinkNet network had the best fitting effect with gravity data, which were 0.3526 and 0.0521. The results reveal that this method can achieve accurate inversion. Using the San Nicolas deposit in Mexico as an example, the proposed method and the improved preconditioned conjugate gradient algorithm were compared to further illustrate the effectiveness of the algorithm. The results reveal that the position and shape trends of the geological body attained using the proposed approach are in good agreement with the drilling data.

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Acknowledgment

This work is supported by the National Science Foundation for Outstanding Young Scholars (Grant No. 42122025).

All authors are grateful to the reviewers and editors for offering valuable suggestions on this paper and also are thankful to the Geophysical Inversion Facility of the University of British Colombia (UBC-GIF) for providing the field data (https://zenodo.org/record/4089070). This work is supported by the National Science Foundation for Outstanding Young Scholars (Grant No. 42122025).

Author information

Authors and Affiliations

  1. College of Electrical Engineering, Naval University of Engineering, Wuhan, 430074, China

    Hou-Pu Li

  2. Department of Basic Courses, Naval University of Engineering, Wuhan, 430033, China

    Rui Qi

  3. United Imaging Surgical Technology Co., LTD, Shanghai, 200100, China

    Jia-Xin Hu

  4. School of Mathematics and Physics, China University of Geosciences, Wuhan, China

    Yu-Xin Sun

Authors
  1. Hou-Pu Li
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  2. Rui Qi
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  3. Jia-Xin Hu
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  4. Yu-Xin Sun
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Corresponding author

Correspondence to Rui Qi.

Additional information

Houpu Li received a Ph.D. degree from the Naval University of Engineering in 2010. He is a professor of Electrical Engineering at the Naval University of Engineering. His current research mainly includes marine geodesy and geophysics.

Rui Qi received a Ph.D. degree from the Institute of Geophysics and Geomatics of China University of Geosciences in 2018. He is an associate professor at the Department of Basic Courses of the Geophysics and Geomatics of China University of Geosciences. His research interests include blind source separation, gravity inversion, and compressed sensing.

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Li, HP., Qi, R., Hu, JX. et al. 3D gravity anomaly inversion based on LinkNet. Appl. Geophys. 20, 36–50 (2023). https://doi.org/10.1007/s11770-023-1020-4

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  • DOI: https://doi.org/10.1007/s11770-023-1020-4

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