# Automatic Identification of Auroral Substorms Based on Ultraviolet Spectrographic Imager Aboard Defense Meteorological Satellite Program (DMSP) Satellite

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## Abstract

**:**

## 1. Introduction

## 2. Data and Approaches

#### 2.1. Brief Description of a Substorm

#### 2.2. Datasets

#### 2.3. Automatic Identification of an Auroral Substorm

#### 2.3.1. Locating the Candidate Region of a WTS

- (1)
- Determine the polar boundary (PB) of the auroral oval fragment (yellow area to the right of Figure 1);
- (2)
- Locate the position of the PB with the largest value difference between MLT and MLAT, which is named as the extreme value point in this paper. Its vicinity is the candidate region for the WTS. The location of the extreme value point is shown in Equation (1).

_{P}represents the MLT of the polar boundary points and MLAT

_{P}represents the MLAT of the polar boundary points.

#### 2.3.2. Extracting the Features of a WTS

- (1)
- The MLT mean difference (diff_MLT_md) and MLAT mean difference (diff_MLAT_md) between the midnight side and the duskside boundaries are calculated.

- (2)
- The first derivative mean of MLAT to MLT at the midnight side boundary (mean_d1_d), the first derivative mean of MLAT to MLT at the duskside boundary (mean_d1_m), and the difference between them (diff_d1_md) are calculated.

- (3)
- The mean of the absolute value of the first derivative of MLAT to MLT for the midnight boundary (mean_absd1_d), the mean of the absolute value of the first derivative of MLAT to MLT for the duskside boundary (mean_absd1_m), and the difference between them (diff_absd1_md) are calculated.

- (4)
- The second derivative mean of MLAT to MLT for the midnight boundary (mean_d2_d), the second derivative mean of MLAT to MLT for the duskside boundary (mean_d2_m), and the difference between them (diff_d2_md) are calculated.

- (5)
- The mean of the absolute value of the second derivative of MLAT to MLT for the midnight boundary (mean_absd2_d), the mean of the absolute value of the second derivative of MLAT to MLT for the duskside boundary (mean_absd2_m), and the difference between them (diff_absd2_md) are calculated.

- (6)
- The histogram distributions of the duskside boundary with ten equal parts are calculated, along with the MLT distribution features (counts_MLT_d), the MLAT distribution features (counts_MLAT_d), the MLAT to MLT first derivative distribution features (counts_d1_d), and the MLAT to MLT second derivative distribution features (counts_d2_d).
- (7)
- The histogram distributions of midnight side boundary with ten equal parts are calculated, along with the MLT distribution features (counts_MLT_m), the MLAT distribution features (counts_MLAT_m), the MLAT to MLT first derivative distribution features (counts_d1_m), and the MLAT to MLT second derivative distribution features (counts_d2_m).

_{n,l}is the auroral intensity in the lth small sector of the nth sector.

#### 2.3.3. Determining the Structures of a WTS

## 3. Experimental Results and Analysis

#### 3.1. Objective Evaluation

- a.
- The actual positive samples were predicted to be positive samples by the classifier; i.e., the aurora image actually contained a WTS and the SVM classifier recognized it as containing a WTS.
- b.
- The actual positive samples were predicted to be negative samples by the classifier; i.e., the aurora image actually contained a WTS and the SVM classifier recognized that it did not contain a WTS.
- c.
- The actual negative samples were predicted to be positive samples by the classifier; i.e., the aurora image did not contain a WTS and the SVM classifier recognized it as containing a WTS.
- d.
- The actual negative samples were predicted to be negative samples by the classifier; i.e., the aurora image did not contain a WTS and the SVM classifier recognized that it did not contain a WTS.

_{accuracy}) (the ability of the classifier to identify the correctness of the overall sample), recall rate (R

_{recall}) (the ability of the classifier to correctly predict the full degree of positive samples), miss rate (R

_{miss}) (the ability of the classifier to correctly predict the purity of negative samples), false-alarm rate (R

_{false-alarm}) (the ability of the classifier to correctly predict the purity of positive samples), and precision rate (R

_{precision}) (the ability of the classifier to correctly predict the accuracy of positive samples) of the classification results. These five evaluation indicators identified the effectiveness of the SVM classifier accordingly. Accuracy is a measure of the overall performance of a classifier, while precision is a measure of the accuracy of a classifier when predicting a positive class. For the purpose of this paper, the precision represents the accuracy of detecting images containing a WTS. The formulas for calculating these five indicators were as follows:

#### 3.2. Analysis of Results

#### 3.2.1. Analysis of Missed Events

#### 3.2.2. Analysis of False-Alarm Events

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**(

**Left**) ultraviolet (UV) auroral image from SSUSI of DMSP F16 with the altitude-adjusted corrected geomagnetic (AACGM) coordinates; (

**right**) auroral oval fragment from 18–24 MLT sectors from the left UV image.

Train–Test Ratio | True Categories | SVM Classifier Determination Categories | Total | |
---|---|---|---|---|

WTS | No WTS | |||

1:9 | WTS | a = 162 | b = 165 | 327 |

No WTS | c = 131 | d = 341 | 472 | |

Total | 293 | 506 | 799 |

Train–Test Ratio | R_{accuracy} | R_{recall} | R_{miss} | R_{false-alarm} | R_{precision} |
---|---|---|---|---|---|

1:9 | 63.00% | 49.66% | 50.34% | 44.48% | 55.52% |

2:8 | 63.61% | 41.24% | 58.77% | 42.08% | 57.92% |

3:7 | 62.73% | 40.94% | 59.06% | 43.40% | 56.60% |

4:6 | 61.39% | 23.95% | 76.05% | 42.97% | 57.03% |

5:5 | 62.22% | 31.46% | 68.55% | 42.40% | 57.60% |

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**MDPI and ACS Style**

Hu, Z.-J.; Lian, H.-F.; Zhao, B.-R.; Han, B.; Zhang, Y.-S.
Automatic Identification of Auroral Substorms Based on Ultraviolet Spectrographic Imager Aboard Defense Meteorological Satellite Program (DMSP) Satellite. *Universe* **2023**, *9*, 412.
https://doi.org/10.3390/universe9090412

**AMA Style**

Hu Z-J, Lian H-F, Zhao B-R, Han B, Zhang Y-S.
Automatic Identification of Auroral Substorms Based on Ultraviolet Spectrographic Imager Aboard Defense Meteorological Satellite Program (DMSP) Satellite. *Universe*. 2023; 9(9):412.
https://doi.org/10.3390/universe9090412

**Chicago/Turabian Style**

Hu, Ze-Jun, Hui-Fang Lian, Bai-Ru Zhao, Bing Han, and Yi-Sheng Zhang.
2023. "Automatic Identification of Auroral Substorms Based on Ultraviolet Spectrographic Imager Aboard Defense Meteorological Satellite Program (DMSP) Satellite" *Universe* 9, no. 9: 412.
https://doi.org/10.3390/universe9090412