Identification of Cenozoic Ostracods in the Qaidam Basin Using Convolutional and Transformer-Based Neural Networks

Wenqiang Tang; Hanting Zhong; Zhisong Cao; Kunyu Wu; Dangpeng Xi; Xingxing Zhang; Ping Yang; Yuxuan Zhou; Chao Ma

doi:10.1007/s12583-025-0328-9

Volume 37 Issue 3

Jun 2026

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Journal of Earth Science > 2026 > 37(3): 968-984.

Wenqiang Tang, Hanting Zhong, Zhisong Cao, Kunyu Wu, Dangpeng Xi, Xingxing Zhang, Ping Yang, Yuxuan Zhou, Chao Ma. Identification of Cenozoic Ostracods in the Qaidam Basin Using Convolutional and Transformer-Based Neural Networks. Journal of Earth Science, 2026, 37(3): 968-984. doi: 10.1007/s12583-025-0328-9

Citation:

Wenqiang Tang, Hanting Zhong, Zhisong Cao, Kunyu Wu, Dangpeng Xi, Xingxing Zhang, Ping Yang, Yuxuan Zhou, Chao Ma. Identification of Cenozoic Ostracods in the Qaidam Basin Using Convolutional and Transformer-Based Neural Networks. Journal of Earth Science, 2026, 37(3): 968-984. doi: 10.1007/s12583-025-0328-9

Citation:

Wenqiang Tang, Hanting Zhong, Zhisong Cao, Kunyu Wu, Dangpeng Xi, Xingxing Zhang, Ping Yang, Yuxuan Zhou, Chao Ma. Identification of Cenozoic Ostracods in the Qaidam Basin Using Convolutional and Transformer-Based Neural Networks. Journal of Earth Science, 2026, 37(3): 968-984. doi: 10.1007/s12583-025-0328-9

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Identification of Cenozoic Ostracods in the Qaidam Basin Using Convolutional and Transformer-Based Neural Networks

doi: 10.1007/s12583-025-0328-9

Wenqiang Tang^{1, 2
,},
Hanting Zhong^{1, 2
,
,
,},
Zhisong Cao^{1, 2
,},
Kunyu Wu^{3, 4
,},
Dangpeng Xi^5
,,
Xingxing Zhang^{3, 4
,},
Ping Yang^{3, 4
,},
Yuxuan Zhou^{1, 2
,},
Chao Ma^{1, 2
,}

1.
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Institute of Sedimentary Geology, College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China
2.
Key Laboratory of Deep-Time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources, Chengdu University of Technology, Chengdu 610059, China
3.
Qinghai Oilfield Company, PetroChina, Dunhuang 736202, China
4.
Plateau Saline Lacustrine Basin Oil-Gas Geology Key Laboratory of Qinghai Province, Dunhuang 736202, China
5.
School of Earth Science and Resources, China University of Geosciences (Beijing), Beijing 100083, China

More Information

Corresponding author: Hanting Zhong, zhonghanting@cdut.edu.cn
Received Date: 11 Apr 2025
Accepted Date: 02 Jul 2025
Issue Publish Date: 30 Jun 2026

Abstract

Abstract

Microfossils play a crucial role in biostratigraphy and paleoenvironmental reconstructions, as the first appearance datum (FAD) and last appearance datum (LAD) of specific microfossils enable precise stratigraphic correlations and age determinations. However, traditional identification methods are often time-intensive and heavily dependent on expert knowledge. To overcome these limitations, we propose a dual-path deep learning model, MicroViT, which integrates convolutional neural networks (CNNs) and vision transformers (ViTs) to automate the identification of Cenozoic ostracods (Microlimnocythere, Cyprideis, Qaidamocythere, Hemicyprinotus, Qaibeigouia, Austrocypris, and Candoniella) from the Qaidam Basin. MicroViT achieves an accuracy of 95.34%, demonstrating superior performance across all classification metrics. Furthermore, we utilized Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize the decision-making process of the model, revealing that DL models focus on morphological features such as reticulation and honeycomb-like spots. We also investigated the potential for extending this approach to other microfossil groups, such as charophytes and sporopollen, as well as to diverse ostracod populations. These results highlight the significant potential of deep learning techniques for rapid and accurate microfossil classification, offering promising applications in micropaleontology and stratigraphic studies.
- Ostracods identification,
- deep learning,
- transformer-based neural networks,
- convolutional neural networks

Electronic Supplementary Materials: Supplementary materials (Text S1, Table S1, Figure S1) are available in the online version of this article at https://doi.org/10.1007/s12583-025-0328-9.
Conflict of Interest
The authors declare that they have no conflict of interest.

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References(87)

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Identification of Cenozoic Ostracods in the Qaidam Basin Using Convolutional and Transformer-Based Neural Networks

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Identification of Cenozoic Ostracods in the Qaidam Basin Using Convolutional and Transformer-Based Neural Networks

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