Microscopic recognition of micro fossils in carbonate rocks based on convolutional neural network

YU Xiaolu; YE Kai; DU Chongjiao; GONG Hanning; MA Zhongliang

doi:10.11781/sysydz202105880

Volume 43 Issue 5

Sep. 2021

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YU Xiaolu, YE Kai, DU Chongjiao, GONG Hanning, MA Zhongliang. Microscopic recognition of micro fossils in carbonate rocks based on convolutional neural network[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2021, 43(5): 880-885. doi: 10.11781/sysydz202105880

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YU Xiaolu, YE Kai, DU Chongjiao, GONG Hanning, MA Zhongliang. Microscopic recognition of micro fossils in carbonate rocks based on convolutional neural network[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2021, 43(5): 880-885. doi: 10.11781/sysydz202105880

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Microscopic recognition of micro fossils in carbonate rocks based on convolutional neural network

doi: 10.11781/sysydz202105880

YU Xiaolu^{1, 2
,},
YE Kai^{2
,
,},
DU Chongjiao²,
GONG Hanning^{1, 2},
MA Zhongliang^{1, 2}

1.
SINOPEC Key Laboratory of Petroleum Accumulation Mechanisms, Wuxi, Jiangsu 214126, China
2.
Wuxi Research Institute of Petroleum Geology, SINOPEC, Wuxi, Jiangsu 214126, China

Received Date: 2021-06-06
Rev Recd Date: 2021-08-31
Publish Date: 2021-09-28

Abstract

Abstract

The identification of microfossils in carbonate rocks with thin-section observation is of great significance for the study of sedimentary environment, but the traditional method by manual identification is highly experience required and is greatly affected by subjective factors.In this paper, a method for microscopic recognition of carbonate rocks based on ResNet convolutional neural network was introduced. Through image preprocessing, model design, model training etc., the intelligent recognition of fossils of organisms within thin section images were realized, and the recognition accuracy showed to be 86%.Meanwhile, an advanced YOLO(You Look Only Once) object detection model was proposed, which could realize the detection and recognition of the area where the organism locates in thin section image, and the recognition accuracy appeared to be 85%.This method verified the feasibility of using digital image processing algorithm and deep learning method to intelligently identify biological microscopic images of carbonate rocks.It can be regarded as a useful supplement to traditional manual identification methods and has certain practical application value.
- Convolutional Neural Network,
- ResNet,
- YOLO,
- biological recognition,
- fossils,
- carbonate rock

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References

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