Automatic identification of semi-tracks on apatite and mica using a deep learning method

• Published in: Computers & geosciences 2022-05, Vol.162, p.105081, Article 105081
• Main Authors: Li, Rongyan, Xu, Zeyu, Su, Cheng, Yang, Rong
• Format: Article
• Language: English
• Subjects: Convolutional neural network; Deep learning; Fission track identification; Semantic segmentation
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2022.105081

Fission track dating is a widely used thermochronological approach to constrain the thermal history of rocks. Conventionally this approach requires manual identification of fission tracks under the microscope which can be time-consuming and labor-intensive. In this study, we proceed with the newly developed approach to identify fission tracks on transmitted light images based on a deep learning method. In this new approach, we use a convolution neural network (CNN) to extract semi-tracks through image semantic segmentation. Considering the boundary ambiguity inherent in the CNN, we also extract the multi-scale boundary of the images in order to refine the semantic segmentation. We then calculate an area threshold of semi-tracks to determine whether semi-tracks are overlapping or not. These non-overlapping tracks are counted directly from the refined semantic segmentation images. For these overlapping tracks, we develop a boundary-superimposed method by using the refined semantic segmentation and the multi-scale boundary images with the help of the reflected-light images to split them before counting. We used 101 images of spontaneous fission tracks and 7 images of induced fission tracks for training with this new approach and tested the resulting convolutional neural networks on 114 spontaneous fission track images and 60 induced fission track images. Most of the test samples show high precision, recall, F1-score, and overall accuracy, highlighting the potential usage of this approach to identify fission tracks automatically. •A new deep learning method is developed to identify apatite and mica semi-tracks automatically.•The multi-scale boundary is implemented to solve the boundary ambiguity of CNN.•Identifying overlapping tracks is achieved by the boundary-superimposed method.•Most of the F1-score and overall accuracy are over 90% in the identification of semi-tracks.