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Volume 36 Issue 4
Aug 2025
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Qingkui Meng, Lianghui Guo, Shuai Zhang, Hanyu Lou, Rui Li. Deep Learning in Gravity Research: A Review. Journal of Earth Science, 2025, 36(4): 1808-1819. doi: 10.1007/s12583-023-1926-x
Citation: Qingkui Meng, Lianghui Guo, Shuai Zhang, Hanyu Lou, Rui Li. Deep Learning in Gravity Research: A Review. Journal of Earth Science, 2025, 36(4): 1808-1819. doi: 10.1007/s12583-023-1926-x

Deep Learning in Gravity Research: A Review

doi: 10.1007/s12583-023-1926-x
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  • Corresponding author: Lianghui Guo, guo_lianghui@163.com
  • Received Date: 27 Mar 2023
  • Accepted Date: 22 Aug 2023
  • Available Online: 05 Aug 2025
  • Issue Publish Date: 30 Aug 2025
  • This study explores the application of deep learning (DL) to gravity research, which is a promising intersection of earth science and information science. DL provides new methods and ideas for exploring and solving problems related to multiple solutions and uncertainty in the study of gravity. We focus on the application of convolutional neural networks, recurrent neural networks, and other DL technologies to gravity data denoising, interpolation, anomaly inversion, field modelling, and geological interpretation. However, importantly, the application of DL to the field of gravity research is still in its initial stage. There is significant potential for development and widespread application in overcoming limitations in sample size, network framework optimization, and generalization ability.

     

  • Conflict of Interest
    The authors declare that they have no conflict of interest.
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