Journal of Jilin University(Earth Science Edition) ›› 2020, Vol. 50 ›› Issue (6): 1929-1938.doi: 10.13278/j.cnki.jjuese.20190209

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Remote Sensing Geochemical Inversion Model by Using Extreme Learning Machine

Sun Liying1, Yang Chen1, Zhao Haishi2, Chang Zhiyong3,4   

  1. 1. College of Earth Sciences, Jilin University, Changchun 130061, China;
    2. College of Computer Science and Technology, Jinlin University, Changchun 130012, China;
    3. College of Biological and Agricultural Engineering, Jilin University/Key Laboratory of Bionic Engineering(Jilin University), Ministry of Education, Changchun 130022, China;
    4. National-Local Joint Engineering Laboratory of In-Situ Conversion, Drilling and Exploitation Technology for Oil Shale, Jilin University, Changchun 130021, China
  • Received:2019-08-02 Published:2020-12-11
  • Supported by:
    Supported by National Natural Science Foundation of China (61572228), Science-Technology Development Plan Project of Jilin Province of China (20190303006SF, 20190302107GX) and Industrial Innovation Special Funds Project of Jilin Province (2019C053-5, 2019C053-7)

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Abstract: Geochemical exploration research involves a large amount of sampling work, which is extremely difficult in inaccessible terrain with harsh working environments. The authors propose a geochemical inversion model with remote sensing images by using extreme learning machine (ELM) to alleviate the difficulty of ore prospecting in the areas with insufficient regional data. The partial least squares regression (PLSR) method is used to select the remote sensing image features which are highly correlated with geochemistry data. In this model, the nonlinear relationship between the geochemical data and the remote sensing images is established using ELM for getting unknown geochemical anomalies, after which the ore prospecting work can be further promoted. In the experiment, 1:200 000 soil geochemical data of Cu element and the Landsat 8 OLI remote sensing images were used for the inversion analysis. The experimental results showed that the anomalous distribution obtained by the ELM-based inversion model had a good correspondence with known ore spots, which verified the effectiveness of the proposed model.

Key words: extreme learning machine (ELM), partial least squares regression, remote sensing geochemistry, inversion

CLC Number: 

  • TP751.1
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