A new method for identifying key fossil species in the Miocene Calcareous Nannofossil Zone: insights from deep convolutional neural networks

Abstract

BackgroundCalcareous nannofossils are minute microfossils widely present in marine strata. Their identification holds significant value in studies related to stratigraphic dating, paleo-environmental evolution, and paleoclimate reconstruction. However, the process of identifying these fossils is time consuming, and the discrepancies between the results obtained from different manual identification methods are substantial, hindering quantification efforts. Therefore, it is necessary to explore automated assisted identification of fossil species. This study mainly focused on 18 key fossil species from the Miocene era. Five convolutional neural network (CNN) models and 10 data augmentation techniques were compared. These models and techniques were employed to analyze and collectively train two- and three-dimensional fossil morphologies and structures obtained from three different fossils observed under single-polarized light microscopy, orthogonal polarized light microscopy, and scanning electron microscopy. Finally, the model performance was evaluated based on the predictive outcomes on the test set, using metrics such as confusion matrix and top-k accuracy.ResultThe results indicate that, for the calcareous nannofossil images, the most effective data augmentation approach is a combination of four methods: random rotation, random mirroring, random brightness, and gamma correction. Among the CNN models, DenseNet121 exhibits the optimal performance, achieving an identification accuracy of 94.56%. Moreover, this model can distinguish other fossils beyond the 18 key fossil species and non-fossil debris. Based on the confusion matrix, the evaluation results reveal that the model has strong generalization capability and outputs highly credible identification results.ConclusionDrawing on the identification results from CNN, this study asserts a robust correlation among extinction photographs, planar images, and stereoscopic morphological images of fossil species. Collective training facilitates the joint extraction and analysis of fossil features under different imaging methods. CNN demonstrates many advantages in the identification of calcareous nannofossils, offering convenience to researchers in various fields, such as stratigraphy, paleo-ecology, paleoclimate, and paleo-environments of ancient oceans. It has great potential for advancing the development of marine surveys and stratigraphic recognition processes in the future.