The dynamic behavior of solar prominences and filaments is a preursor to coronal mass ejections (CMEs), which can disrupt Earth's magnetosphere and affect satellite communications. Systematic ground-based solar observations, conducted with high temporal resolution, are instrumental in monitoring these structures. Analysis of the morphological changes and destabilization processes of filaments and prominences captured in datasets can help to identify early warning signs of potential eruptions. This capability is vital for developing reliable space weather forecasting systems, thereby mitigating the adverse effects of solar disturbances on Earth's technological infrastructure. Previously we introduced Wavetrack, a wavelet-based feature recognition software, which allowed, to a certain extent, to automate feature recognition for multiple events. We have since developed a convolutional neural network (CNN) model set which uses Wavetrack outputs as ground truth. Our initial model performance was shown on a set of SDO AIA instrument data performing segmentation of EUV and shock waves. In this work, we extend this hybrid approach for algorithmic and data-driven segmentation of on-disk solar features (prominences and filaments) using data from ground based-instruments, primarily focusing on Kanzelhöhe Observatory data. We discuss our approach to engineering training sets on real and synthetic data and the development of a CNN architecture generated within a general hyperparameter search routine. We showcase its performance on a set of filament/prominence events.
How to cite:
Stepanyuk, O., Pötzi, W., Kozarev, K., Dechev, M., and Miteva, R.: Hybrid AI Approaches for Solar Feature Recognition Using Ground-Based Instrument Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12654, https://doi.org/10.5194/egusphere-egu25-12654, 2025.
Share
Please decide on your access
Please use the buttons below to download the supplementary material or to visit the external website where the presentation is linked. Regarding the external link, please note that Copernicus Meetings cannot accept any liability for the content and the website you will visit.
You are going to open an external link to the presentation as indicated by the authors. Copernicus Meetings cannot accept any liability for the content and the website you will visit.
Comments on the supplementary material
AC: Author Comment | CC: Community Comment | Report abuse