The goal of the SWx TREC Deep Learning Laboratory (DLL) is to provide interdisciplinary resources to advance our knowledge of space weather using cutting edge software and hardware. If you are interested in working with the DLL on a project or want to know more about our resources, please contact Wendy Carande at wendy.carande@lasp.colorado.edu.

Projects

The DLL currently conducts research in solar flare prediction and GNSS scintillation prediction.

Hardware

The DLL has two Systems 76 workstations for machine learning research: one with an NVIDIA Titan RTX GPU card and 20 TB of local storage, and one with dual NVIDIA Titan V GPU cards and 85 TB of local HDD storage.

People

Leadership

Director Wendy Hawley Carande

Students

Allison Liu

Solar flare prediction

TREC solar flare prediction efforts employ novel topological data analysis (TDA), computational geometry (CG), feature engineering, long-short-term memory (LSTM), and convolutional neural network (CNN) architectures to predict solar magnetic eruptions. The systems are trained on the 20 PByte NASA Solar Dynamics Observatory (SDO) database using both magnetic field data from the Helioseismic and Magnetic Imager (HMI) instrument and chromospheric and coronal images from the Atmopheric Imaging Assembly (AIA) instrument.

Publications

  1. Deshmukh, V., T. E. Berger, E. Bradley, J. Meiss, “Leveraging the mathematics of shape for solar magnetic eruption prediction”, J. Space Weather & Space Clim., 10, 13, 2020.
  2. Newman, W. D., W. H. Carande, “Long-short-term memory model for solar flare predictions”, submitted to Frontiers in Astron. and Space Sci., 2020.

GNSS scintillation prediction

Global navigation satellite system (GNSS) atmospheric scintillation interference prediction and classification. This effort uses support vector machine (SVM), long short-term memory (LSTM), and spatio-temporal neural network architectures to predict the onset of ionospheric scintillation (bubbles) that can severely degrade GNSS signal reception in both equatorial and high latitude regions. Moreover, the algorithms developed must also be able to separate and identify ionospheric scintillation from other disturbance signatures observed on GNSS signals. These other sources maybe due to intentional or unintentional radio frequency interferences, multipath reflections, and satellite oscillator anomaly.

Publications

  1. Jiao, Y., J. Hall, Y. Morton, “Performance evaluation of an automatic GPS ionospheric phase scintillation detector using a machine-learning algorithm,” Navigation, J. Institute of Navigation, 64(3):391-402, DOI: 10.1002/navi.188, Summer 2017.
  2. Jiao, Y., J. Hall, Y. Morton, “Automatic equatorial GPS amplitude scintillation detection using a machine learning algorithm,” IEEE Trans. Aero. Elec. Sys., 53(1): 405-418, DOI:10.1109/TAES.2017.2650758, Online ISSN 1557-9603, 2017.