# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
## 3 Experiments  
### Dataset  
The performance of a deep learning algorithm is inseparable from large-scale data, so we collect partial-/full-halo CME events and corresponding satellite time series observation images from 1996 to 2018 to build a large-scale and differentiated data set.  
First, we select partial-/full-halo CME events from the SOHO LASCO CME Catalog [48], and each CME event includes onset time, the angular width and other parameters. Then the geoeffectiveness of each CME event is checked with other sources' data. The geoeffectiveness definition used in this research is one CME eventually arrives at Earth and causes geomagnetic disturbances during which the minimal Dst index is smaller than \(-30\) nT. We employ a similar approach to the one in CAT-PUMA [21] to construct the geoeffective CMEs data set, collect events from the Richardson and Cane List [49], the full halo CMEs provide by the University of Science and Technology of China [50], the George Mason University CME/ICME List [51] and the CME Scoreboard by NASA, respectively. We collect geoeffective CMEs and their corresponding onset/arrival time. Here the onset time is the time of a CME's first appearance in the Field-of-View (FOV) of SOHO LASCO C2, and the arrival time of CMEs is defined as the arrival time of interplanetary shocks driven by CMEs hereafter. After that, align the collected geoeffective CMEs with the selected partial-/full-halo CME events through onset time.  
For convenience, we collected images directly through CDAW Data Center ([[https://cdaw.gsfc.nasa.gov/images/soho/lasco/](https://cdaw.gsfc.nasa.gov/images/soho/lasco/)]([https://cdaw.gsfc.nasa.gov/images/soho/lasco/](https://cdaw.gsfc.nasa.gov/images/soho/lasco/)) (accessed on 18 March 2021), the Day Month Year can be obtained through the corresponding CME event). Observations from Extreme ultraviolet Imaging Telescope (EIT) on the SOHO spacecraft or Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO) can provide some information on the source region of CMEs, while the LASCO C2 coronagraph observations provide CMEs' evolution and propagation information. So for each CME event, the composite running-difference images of SOHO/LASCO C2 and SOHO/EIT or SDO/AIA from 10 min before up to 4 h after the onset times of the events are collected. We believe such combined observations are more discriminative and can provide a better forecast.  
After the CME events with zero images are eliminated, there are a total of 2400 partial-/full-halo CME events and corresponding 35,666 images are collected, including 246 geoeffective CME events and 3460 corresponding images. In our data set, since the SDO satellite was launched in 2010 and the CCD bakeout of EIT and AIA, there will be three different formats of observations images: only LASCO C2 format, LASCO C2 combine EIT format and LASCO C2 combine AIA format. Meanwhile, we count the distribution of images in the CME events, as indicated in Figure 5, the number of CME events with only LASCO C2 format is 76, the number of CME events with LASCO C2 combine EIT format is 1117 and the number of CME events with LASCO C2 combine AIA format is 1207. The CME events which only contain LASCO C2 observation are fewer and randomly distributed in geoeffective or non-geoeffective CMEs, so they will not bias the training and testing of our method. Due to the different operation status of different instruments, the time interval between adjacent observation images is not fixed, which will lead to large fluctuations in the number of images collected for each CME event (shown in Figure 6).