Work Description

Title: Data and Data products for machine learning applied to solar flares Open Access Deposited

Ingest Append Scripts

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Attribute Value
Methodology
  • The data deposited here are the both the input and resulting output of our machine learning efforts for predicting solar flare events. The input data are produced by our pre-processing pipeline, which is built to extract from two sources -- the Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) . The GOES files provide a list of flare events in excel csv format. The SHARP files provide time sequences of more than 20 physical parameters derived from SDO/HMI data, which are saved in hdf5 format. The remaining files (B_HARPs_CNNencoded_part_XXX.hdf5, and M_X_HARPs_CNNencoded_part_XXX.hdf5) are the output from the convolutional neural network (CNN), a deep learning algorithm used here to extract/select features from raw HMI data. The CNN is a deep learning model trained to capture both the spatial and temporal information from HMI magnetogram data for strong/weak flare classification and for predictions of flare intensities. The input data for the CNN are HMI Active Region Patch (HARP) 3-component vector magnetograms available online from the Stanford Joint Science Operations Center (JSOC) at  http://jsoc.stanford.edu/.
Description
  • GOES_flare_list: contains a list of more than 10,000 flare events. The list has 6 columns, flare classification, active region number, date, start time end time, emission peak time

  • GOES_B_flare_list: contains time series data of SDO/HMI SHARP parameters for B class solar flares

  • GOES_MX_flare_list: contains time series data of SDO/HMI SHARP parameters for M and X class solar flares

  • SHARP_B_flare_data_300.hdf5 and SHARP_MX_flare_data_300.hdf5 files contain time series more than 20 physical variables derived from the SDO/HMI SHARP data files. These data are saved at a 12 minute cadence and are used to train the LSTM model.

  • B_HARPs_CNNencoded_part_xxx.hdf5 and M_X HARPs_CNNencoded_part_xxx.hdf5 include neural network encoded features derived from vector magnetogram images derived from the Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI). These data files typically contains one or two sequences of magnetograms covering an active region for a period of 24h with a 1 hour cadence. We encode each magnetogram with frames of a fixed size of 8x16 with 512 channels.
Creator
Depositor
  • chipm@umich.edu
Contact information
Discipline
Funding agency
  • National Aeronautics and Space Administration (NASA)
  • National Science Foundation (NSF)
ORSP grant number
  • F051148 F033166
Keyword
Citations to related material
  • Chen, Y., Manchester, W., Hero, A., Toth, G., DuFumier, B. Zhou, T., Wang, X., Zhu, H., Sun, Zeyu, Gombosi, T., Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters, Space Weather, 17, 1404–1426. https://doi.org/10.1029/2019SW002214
  • Jiao, Z., Chen, Y., Manchester, W. (2020). Data for Solar Flare Intensity Prediction with Machine Learning Models [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/b07j-bj08
Resource type
Last modified
  • 05/20/2021
Published
  • 08/13/2019
Language
DOI
  • https://doi.org/10.7302/qnsq-cs38
License
To Cite this Work:
Chen, Y., Manchester, W. I. (2019). Data and Data products for machine learning applied to solar flares [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/qnsq-cs38

Relationships

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Files (Count: 408; Size: 5.77 GB)

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readme GOES data 2019-08-11 2019-08-13 586 Bytes Open Access
GOES_flare_list.csv 2019-08-11 2019-08-11 974 KB Open Access
GOES_B_flare_list.csv 2019-08-11 2019-08-11 105 KB Open Access
GOES_MX_flare_list.csv 2019-08-11 2019-08-11 66.9 KB Open Access
SHARP_B_flare_data_300.hdf5 2019-08-10 2020-10-06 236 MB Open Access
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M_X_HARPs_CNNencoded_part_1.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
M_X_HARPs_CNNencoded_part_2.hdf5 2019-08-12 2019-08-12 18.1 MB Open Access
M_X_HARPs_CNNencoded_part_3.hdf5 2019-08-12 2019-08-12 18.1 MB Open Access
M_X_HARPs_CNNencoded_part_4.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
M_X_HARPs_CNNencoded_part_5.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
M_X_HARPs_CNNencoded_part_6.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
M_X_HARPs_CNNencoded_part_7.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
M_X_HARPs_CNNencoded_part_8.hdf5 2019-08-12 2019-08-12 18.1 MB Open Access
M_X_HARPs_CNNencoded_part_9.hdf5 2019-08-12 2019-08-12 18.1 MB Open Access
M_X_HARPs_CNNencoded_part_10.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
M_X_HARPs_CNNencoded_part_11.hdf5 2019-08-12 2019-08-12 13.3 MB Open Access
M_X_HARPs_CNNencoded_part_12.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
M_X_HARPs_CNNencoded_part_13.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
M_X_HARPs_CNNencoded_part_14.hdf5 2019-08-12 2019-08-12 13.8 MB Open Access
M_X_HARPs_CNNencoded_part_15.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
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M_X_HARPs_CNNencoded_part_18.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
M_X_HARPs_CNNencoded_part_19.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
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M_X_HARPs_CNNencoded_part_21.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
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M_X_HARPs_CNNencoded_part_39.hdf5 2019-08-12 2019-08-12 9.56 MB Open Access
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M_X_HARPs_CNNencoded_part_69.hdf5 2019-08-12 2019-08-12 24.1 MB Open Access
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M_X_HARPs_CNNencoded_part_83.hdf5 2019-08-12 2019-08-12 18.1 MB Open Access
M_X_HARPs_CNNencoded_part_84.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
M_X_HARPs_CNNencoded_part_85.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
M_X_HARPs_CNNencoded_part_86.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
M_X_HARPs_CNNencoded_part_87.hdf5 2019-08-12 2019-08-12 11.6 MB Open Access
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M_X_HARPs_CNNencoded_part_89.hdf5 2019-08-12 2019-08-12 15.6 MB Open Access
M_X_HARPs_CNNencoded_part_90.hdf5 2019-08-12 2019-08-12 7.55 MB Open Access
M_X_HARPs_CNNencoded_part_91.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
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M_X_HARPs_CNNencoded_part_105.hdf5 2019-08-12 2019-08-12 4.78 MB Open Access
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M_X_HARPs_CNNencoded_part_111.hdf5 2019-08-12 2019-08-12 6.29 MB Open Access
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M_X_HARPs_CNNencoded_part_114.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
M_X_HARPs_CNNencoded_part_115.hdf5 2019-08-12 2019-08-12 6.29 MB Open Access
M_X_HARPs_CNNencoded_part_116.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
M_X_HARPs_CNNencoded_part_117.hdf5 2019-08-12 2019-08-12 6.79 MB Open Access
M_X_HARPs_CNNencoded_part_118.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
M_X_HARPs_CNNencoded_part_119.hdf5 2019-08-12 2019-08-12 10.1 MB Open Access
M_X_HARPs_CNNencoded_part_120.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
M_X_HARPs_CNNencoded_part_121.hdf5 2019-08-12 2019-08-12 13.1 MB Open Access
M_X_HARPs_CNNencoded_part_122.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
M_X_HARPs_CNNencoded_part_123.hdf5 2019-08-12 2019-08-12 18.1 MB Open Access
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M_X_HARPs_CNNencoded_part_126.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
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M_X_HARPs_CNNencoded_part_137.hdf5 2019-08-12 2019-08-12 8.55 MB Open Access
M_X_HARPs_CNNencoded_part_138.hdf5 2019-08-12 2019-08-12 4.28 MB Open Access
M_X_HARPs_CNNencoded_part_139.hdf5 2019-08-12 2019-08-12 6.79 MB Open Access
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M_X_HARPs_CNNencoded_part_144.hdf5 2019-08-12 2019-08-12 6.79 MB Open Access
M_X_HARPs_CNNencoded_part_145.hdf5 2019-08-12 2019-08-12 8.3 MB Open Access
M_X_HARPs_CNNencoded_part_146.hdf5 2019-08-12 2019-08-12 6.54 MB Open Access
M_X_HARPs_CNNencoded_part_147.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
M_X_HARPs_CNNencoded_part_148.hdf5 2019-08-12 2019-08-12 7.3 MB Open Access
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M_X_HARPs_CNNencoded_part_150.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
M_X_HARPs_CNNencoded_part_151.hdf5 2019-08-12 2019-08-12 11.3 MB Open Access
M_X_HARPs_CNNencoded_part_152.hdf5 2019-08-12 2019-08-12 8.55 MB Open Access
M_X_HARPs_CNNencoded_part_153.hdf5 2019-08-12 2019-08-12 7.8 MB Open Access
M_X_HARPs_CNNencoded_part_154.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
M_X_HARPs_CNNencoded_part_155.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
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M_X_HARPs_CNNencoded_part_157.hdf5 2019-08-12 2019-08-12 7.04 MB Open Access
M_X_HARPs_CNNencoded_part_158.hdf5 2019-08-12 2019-08-12 8.81 MB Open Access
M_X_HARPs_CNNencoded_part_159.hdf5 2019-08-12 2019-08-12 11.1 MB Open Access
M_X_HARPs_CNNencoded_part_160.hdf5 2019-08-12 2019-08-12 3.27 MB Open Access
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M_X_HARPs_CNNencoded_part_169.hdf5 2019-08-12 2019-08-12 17.9 MB Open Access
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M_X_HARPs_CNNencoded_part_189.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
M_X_HARPs_CNNencoded_part_190.hdf5 2019-08-12 2019-08-12 12.1 MB Open Access
M_X_HARPs_CNNencoded_part_191.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access
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M_X_HARPs_CNNencoded_part_194.hdf5 2019-08-12 2019-08-12 18.1 MB Open Access
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M_X_HARPs_CNNencoded_part_196.hdf5 2019-08-12 2019-08-12 18.1 MB Open Access
M_X_HARPs_CNNencoded_part_197.hdf5 2019-08-12 2019-08-12 18.1 MB Open Access
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M_X_HARPs_CNNencoded_part_199.hdf5 2019-08-12 2019-08-12 18.1 MB Open Access
M_X_HARPs_CNNencoded_part_200.hdf5 2019-08-12 2019-08-12 6.04 MB Open Access

GOES_flare_list.csv contains a list of more than 12,000 flare events. The list has 6 columns, flare classification, NOAA active region number, date, start time end time, emission peak time

GOES_B_flare_list.csv contains the list of B flare events with data including numofframes, flare end time, flare event class, event date, NOAA active region number, peak time, and start_time

GOES_MX_flare_list.csv contains the list of M and X class flare events with data including numofframes, flare end time, flare event class, event date, NOAA active region number, peak time, and start_time

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