A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection - Junpei Li; Hong Chen; Jingjing Wang; Yanhong Chen; Bingxian Luo; Hao Deng (The Astrophysical Journal Supplement Series)
Current models for detecting interplanetary coronal mass ejections (ICMEs) using spacecraft in situ measurements rely heavily on manual processes, resulting in time-consuming procedures, inconsistencies in identification criteria, and gaps in existing catalogs. Although several machine learning–based detection models have been proposed, there is still a need for improved accuracy. This study presents a convolutional neural network–long short-term memory (CNN-LSTM) model with dynamic loss function, designed to efficiently process complex multidimensional spatiotemporal data. The model leverages LSTM to capture evolving patterns in time series data, while employing a CNN to extract spatial features, making the model highly adaptable. Using in situ data collected by the Wind spacecraft from 1997 October 1 to 2016 January 1, the proposed model demonstrates strong competitiveness. Trained on data from 1998 January 1 to 2009 December 31, 189 out of 230 ICMEs are detected in the test set, achieving an F1 score of 81.29%.