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Curve generation method of deep parallel spatiotemporal fusion network

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Abstract

To make full use of the effective information of spatiotemporal data for curve prediction, a parallel spatiotemporal fusion network is proposed to predict curves. A differential feature fusion module is designed for spatiotemporal feature fusion, which makes the fused features have richer feature information. The designed adaptive adjusted Huber loss can find adjustable parameters suitable for the curve according to the input data and reduce the influence of outliers on the model prediction. The experiment used the logging data of Huabei Oilfield as the data set. The adaptive adjusted Huber loss outperforms the MAE, MSE, and Huber losses by 9%, 4%, and 2% on the correlation coefficient metric. The parallel spatiotemporal fusion network has 94.11%, 96.81%, 99.90%, 97.93%, and 99.19% accuracy in AC, CAL, SP, GR, and R4. Compared with the LSTM network, the accuracies are improved by 2.23%, 1.18%, 0.01%, 1.42%, and 1.45%, respectively. The experimental results verify that the parallel spatiotemporal fusion network proposed in this paper has good robustness and can combine spatiotemporal data to achieve higher curve prediction accuracy.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (2020D01A131), the Fund of Hubei Ministry of Education (B2019039), the Graduate Teaching and Research Fund of Yangtze University (YJY201909), the Teaching and Research Fund of Yangtze University (JY2019011), the Undergraduate Training Programs for Innovation and Entrepreneurship of Yangtze University under Grant Yz2020057、Yz2020059、Yz2020156, and the National College Student Innovation and Entrepreneurship Training Program (202110489003).

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Author notes

  1. Sheng-Tao He, Chang Wen, Kai Xie, Zi-Han Chen and Bin-Yu Wang have contributed equally to this work.

Authors and Affiliations

  1. School of Electronic Information, Yangtze University, Jingzhou, 434023, China

    Sheng-Tao He, Kai Xie, Zi-Han Chen & Bin-Yu Wang

  2. National Demonstration Center for Experimental Electrical & Electronic Education, Yangtze University, Jingzhou, 434023, China

    Sheng-Tao He, Kai Xie, Zi-Han Chen & Bin-Yu Wang

  3. Western Institute of Yangtze University, Karamay, 834000, China

    Sheng-Tao He, Chang Wen, Kai Xie, Zi-Han Chen & Bin-Yu Wang

  4. School of Computer Science, Yangtze University, Jingzhou, 434023, China

    Chang Wen

  5. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Jian-Biao He

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  1. Sheng-Tao He
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  2. Chang Wen
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Correspondence to Chang Wen.

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He, ST., Wen, C., Xie, K. et al. Curve generation method of deep parallel spatiotemporal fusion network. SIViP 17, 1305–1313 (2023). https://doi.org/10.1007/s11760-022-02338-5

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  • DOI: https://doi.org/10.1007/s11760-022-02338-5

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