Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network

Abstract

A convolutional neural network (CNN) was used to construct a semantic segmentation model to examine the abundance of Phycosiphon incertum by identifying the trace fossil regions in core section images. The abundance of trace fossils provides information about the past activities of benthic animals affected by paleoenvironmental conditions. To quantify the intensity of bioturbation, it is necessary to extract regions of trace fossils and measure the proportion of bioturbated and the observed area of the outcrop section. In this study, a U-Net-type CNN model was used with residual connections and attention mechanisms to identify the trace fossil Phycosiphon. The model was trained to recognize the relationships between core section images from the International Ocean Discovery Program Expedition 362 Site U1480 and manually annotated trace fossil images. After training, the model successfully classified the pixels of the background, outcrop, and Phycosiphon for core section images other than the training data set. The bioturbation intensity estimated from the image predicted by the model was nearly equal to that from the ground truth image. A long-term (approximately past 10 Myr) variation in Phycosiphon abundance was estimated by applying the model to the core section images at Site U1480. Phycosiphon abundance negatively correlated with the number of sandstone layer intercalations, but it was not affected by the sediment accumulation rates. These findings may reflect resistance of Phycosiphon producers to environmental stress. The model developed in this study can be used for other ichnotaxa to reveal the general tendency of variation in bioturbation intensity and ichnodiversity.