Rock mass structural surface trace extraction based on transfer learning
Abstract: To solve engineering geological problems, including water conservancy, transportation, and mining, it is necessary to obtain information on rock mass structures, such as slopes, foundation pits, and tunnels, in time. The traditional method for obtaining structural information requires manual measurement, which is time consuming and labor intensive. Because geological information is complicated and diverse, it is not practical for general deep learning methods to obtain full-scale structural surface trace images to prepare training samples. Transfer learning can abstract high-level features from low-level features with a small number of training samples, which can automatically express the inherent characteristics of objects. This article proposed a rock mass structural surface trace extraction method based on the transfer learning technique that considers the attention mechanism and shape constraints. For the general test set, the accuracy of rock mass structural surface trace recognition with the proposed method can reach 87.2%. Experimental results showed that the proposed method has advantages in extracting complicated geological structure information and is valuable for providing technical support for the extraction of geological information in the construction of water conservancy, transportation, mining, and related projects.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Rock mass structural surface trace extraction based on transfer learning ; volume:14 ; number:1 ; year:2022 ; pages:98-110 ; extent:13
Open Geosciences ; 14, Heft 1 (2022), 98-110 (gesamt 13)
- Creator
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Yi, Xuefeng
Li, Hao
Zhang, Rongchun
Gongqiu, Zhuoma
He, Xiufeng
Liu, Lanfa
Sun, Yuxuan
- DOI
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10.1515/geo-2022-0337
- URN
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urn:nbn:de:101:1-2022071914150794795902
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:27 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Yi, Xuefeng
- Li, Hao
- Zhang, Rongchun
- Gongqiu, Zhuoma
- He, Xiufeng
- Liu, Lanfa
- Sun, Yuxuan
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