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<p>Heat flow is an essential indicator of Earth’s internal thermal evolution and is critical in tectonic activity analysis and geothermal resource evaluation. However, global heat flow measurements are sparse and unevenly distributed, posing significant challenges for accurate prediction. Existing methods aim to predict heat flow distribution by modeling the relationship between other geological features and heat flow. However, they fail to fully capture the spatial relationships among neighboring geographic features, thereby limiting the effective utilization of spatial information. To address these limitations, this study introduces an innovative approach that integrates computer vision techniques into heat flow prediction, proposing a feature mapping method and developing an end-to-end framework named DeepHFMap. Geological features and heat flow sequences are transformed into image-like structures based on latitude and longitude coordinates, with heat flow values from nearby target points incorporated as prompts to guide the model’s learning process, thereby enhancing the model’s understanding of spatial relationships. An encoder-decoder architecture integrated with a channel attention mechanism is used to optimize feature extraction and weighting, significantly improving prediction accuracy and robustness. Experimental results in the United States, Asia, and Europe demonstrate that DeepHFMap significantly outperforms existing methods, effectively capturing spatial correlations among neighboring features and offering a novel solution for heat flow prediction under sparse measurement conditions. Furthermore, in regions with high, medium, and low heat flow, DeepHFMap reduces the Mean Absolute Error (MAE) by 0.92, 0.83, and 1.45, respectively, compared to the second-best performing method. The source codes are available for downloading at the <a href="https://github.com/zhang152267/DeepHFMap">https://github.com/zhang152267/DeepHFMap</a>.</p>

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DeepHFMap: a deep neural network for mapping the heat flow distribution

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Heng Zhang,
  • Yiheng Cai,
  • Shinan Lang,
  • Xiangbin Cui,
  • Xiaoqing Zhang,
  • Yanliang He

摘要

Heat flow is an essential indicator of Earth’s internal thermal evolution and is critical in tectonic activity analysis and geothermal resource evaluation. However, global heat flow measurements are sparse and unevenly distributed, posing significant challenges for accurate prediction. Existing methods aim to predict heat flow distribution by modeling the relationship between other geological features and heat flow. However, they fail to fully capture the spatial relationships among neighboring geographic features, thereby limiting the effective utilization of spatial information. To address these limitations, this study introduces an innovative approach that integrates computer vision techniques into heat flow prediction, proposing a feature mapping method and developing an end-to-end framework named DeepHFMap. Geological features and heat flow sequences are transformed into image-like structures based on latitude and longitude coordinates, with heat flow values from nearby target points incorporated as prompts to guide the model’s learning process, thereby enhancing the model’s understanding of spatial relationships. An encoder-decoder architecture integrated with a channel attention mechanism is used to optimize feature extraction and weighting, significantly improving prediction accuracy and robustness. Experimental results in the United States, Asia, and Europe demonstrate that DeepHFMap significantly outperforms existing methods, effectively capturing spatial correlations among neighboring features and offering a novel solution for heat flow prediction under sparse measurement conditions. Furthermore, in regions with high, medium, and low heat flow, DeepHFMap reduces the Mean Absolute Error (MAE) by 0.92, 0.83, and 1.45, respectively, compared to the second-best performing method. The source codes are available for downloading at the https://github.com/zhang152267/DeepHFMap.