RU-net: A Residual U-net for Automatic Interplanetary Coronal Mass Ejection Detection
Jun Chen, Hao Deng, Shuxin Li, Weifu Li, Hong Chen, Yanhong Chen, Bingxian Luo
Abstract
Abstract Detection methods for interplanetary coronal mass ejections (ICMEs) from in situ spacecraft measurements are mostly manual, which are labor-intensive and time-consuming, being prone to the inconsistencies of identification criteria and the incompleteness of the existing catalogs. Therefore, the automatic detection of ICMEs has aroused the interest of the astrophysical community. Of these automatic methods, the convolutional neural network–based methods show the advantages of fast speed and high precision. To further improve the computing feasibility and detection performance, this paper proposes a method called residual U-net (RU-net), from the perspective of time-series segmentation. With the help of U-net architecture, we design an encoder–decoder network with skip connection to capture multiscale information, where the end-to-end architecture with an embedded residual element is formulated to accelerate the algorithmic convergence. For the in situ data from 1997 October 1 to 2016 January 1 collected by the Wind spacecraft, the results of our experiments demonstrate the competitive performance of the proposed RU-net in terms of accuracy and efficiency (178 of 230 ICMEs are detected in the test set, and the F 1 score is 80.18%).