Training a Neural Network Using Geomagnetic Storm Data to Predict Ground Magnetic Field Fluctuations

Conference Paper


  • • The interaction between the solar wind and the Magnetosphere can produce Geomagnetically Induced Currents (GIC’s) on the ground, which are capable of causing power outages and damage to crucial infrastructure. • The ability to predict when and where these events may occur could allow us to avoid the worst of this damage. • The use of physics-based machine learning models can offer a computationally inexpensive method of predicting GIC events using dB/dt as a proxy, though most models thus far have fallen short of consistently accurate predictions. • Here we train a Long-Short Term Memory (LSTM) machine learning model exclusively on geomagnetic storm occurring between 1995-2010, and the results are compared to those from a model trained on all data from those years.
  • Authors

  • Johnson, Jeremiah
  • Coughlan, Michael
  • Keesee, Amy
  • Pinto, Victor
  • Connor, Hyunju
  • Presented At Event