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<p>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.</p>

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Deep Learning in Gravity Research: A Review

  • 期刊论文

  • 发表日期：

    2025年8月13日

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  • Qingkui Meng,
  • Lianghui Guo,
  • Shuai Zhang,
  • Hanyu Lou,
  • Rui Li

摘要

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.