基于多模态特征融合的地质体识别方法

付偲; 李超岭; 张海燕; 刘畅; 李丰丹

doi:10.3799/dqkx.2021.176

Volume 48 Issue 10

Oct. 2023

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Article Navigation > Earth Science > 2023 > 48(10): 3743-3752

Fu Si, Li Chaoling, Zhang Haiyan, Liu Chang, Li Fengdan, 2023. Geological Body Recognition Based on Multi-Modal Feature Fusion. Earth Science, 48(10): 3743-3752. doi: 10.3799/dqkx.2021.176

Citation:

Fu Si, Li Chaoling, Zhang Haiyan, Liu Chang, Li Fengdan, 2023. Geological Body Recognition Based on Multi-Modal Feature Fusion. Earth Science, 48(10): 3743-3752. doi: 10.3799/dqkx.2021.176

Fu Si, Li Chaoling, Zhang Haiyan, Liu Chang, Li Fengdan, 2023. Geological Body Recognition Based on Multi-Modal Feature Fusion. Earth Science, 48(10): 3743-3752. doi: 10.3799/dqkx.2021.176

Citation:

Fu Si, Li Chaoling, Zhang Haiyan, Liu Chang, Li Fengdan, 2023. Geological Body Recognition Based on Multi-Modal Feature Fusion. Earth Science, 48(10): 3743-3752. doi: 10.3799/dqkx.2021.176

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Geological Body Recognition Based on Multi-Modal Feature Fusion

doi: 10.3799/dqkx.2021.176

Fu Si^{1, 3
,
,},
Li Chaoling²,
Zhang Haiyan^{1, 3
,
,
,},
Liu Chang²,
Li Fengdan²

1.
School of Information Science & Technology, Beijing Forestry University, Beijing 100083, China
2.
Development Research Center, China Geological Survey, Beijing 100037, China
3.
Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China

Received Date: 2021-07-02
Available Online: 2023-10-31
Publish Date: 2023-10-25

Abstract

Abstract

Applying deep learning technology to geological mapping to mine the deep-level in formation of different modal data, so as to achieve more accurate geological mapping. Considering the geophysical and geochemical data and remote sensing image data, in this paper it proposes a geological body recognition method based on multi-modal feature fusion. Firstly, Using deep neural network and convolution neural network to extract the features of the two different modal data and then performs feature splicing to obtain multi-modal features, finally, the fully connected neural network is used for feature fusion to complete the geological body classification. The cross-validation results show that the proposed multi-modal feature fusion method has obvious advantages compared with the deep learning methods using geophysical and geochemical data or remote sensing image data along, and the classification accuracy rate is increased by 14.08% and 2.79%. This result proves that this method can realize more accurate geological body identification, and then better assist geological mapping.
- geological body recognition,
- geological mapping,
- multi-modal feature fusion,
- deep learning,
- mineral exploration

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References(37)

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Geological Body Recognition Based on Multi-Modal Feature Fusion

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