A Multi-Class Labeled Ionospheric Dataset for Machine Learning Anomaly Detection

The binary anomaly detection (classification) of ionospheric data related to Very Low Frequency (VLF) signal amplitude in prior research demonstrated the potential for development and further advancement. Further data quality improvement is integral for advancing the development of machine learning (ML)-based ionospheric data (VLF signal amplitude) anomaly detection. This paper presents the transition from binary to multi-class classification of ionospheric signal amplitude datasets. The dataset comprises 19 transmitter–receiver pairs and 383,041 manually labeled amplitude instances. The target variable was reclassified from a binary classification (normal and anomalous data points) to a six-class classification that distinguishes between daytime undisturbed signals, nighttime signals, solar flare effects, instrument errors, instrumental noise, and outlier data points. Furthermore, in addition to the dataset, we developed a freely accessible web-based tool designed to facilitate the conversion of MATLAB data files to TRAINSET-compatible formats, thereby establishing a completely free and open data pipeline from the WALDO world data repository to data labeling software. This novel dataset facilitates further research in ionospheric signal amplitude anomaly detection, concentrating on effective and efficient anomaly detection in ionospheric signal amplitude data. The potential outcomes of employing anomaly detection techniques on ionospheric signal amplitude data may be extended to other space weather parameters in the future, such as ELF/LF datasets and other relevant datasets.