This paper introduces a new technique in the automatic detection of storm sudden commencement (SC) using the discrete wavelet transform (DWT). A geomagnetic storm is a global simultaneous phenomenon affecting the whole Earth, which means that all ground magnetometers running online will record this event. An algorithm using different characteristic features of the SC is proposed. The selection of an optimal threshold for feature parameters is critical for the success of SC automatic detection. Therefore, this paper uses particle swarm optimization (PSO) to determine the optimal feature threshold values. The developed algorithm is based on data records from a network of ground magnetometers. This algorithm is implemented via multi-resolution analysis (MRA) of the DWT using the Haar wavelet filter. Four-year data sampled at one sample/s from six ground stations from low to high latitudes were analyzed to develop and test this technique. Data representing 450 days from five stations operating simultaneously are available. The confusion matrix of all possible outcomes shows that the accuracy of the proposed algorithm is 97.33%.

本文介绍了一种利用离散小波变换（DWT）自动检测风暴突然开始（SC）的新技术。地磁风暴是影响整个地球的全球同步现象，这意味着所有在线运行的地面磁力计都会记录此事件。提出了一种利用SC的不同特征的算法。为特征参数选择最佳阈值对于SC自动检测的成功至关重要。因此，本文使用粒子群算法（PSO）确定最佳特征阈值。所开发的算法基于来自地面磁力计网络的数据记录。该算法是使用Haar小波滤波器通过DWT的多分辨率分析（MRA）实现的。分析了从低纬度到高纬度的六个地面站以一个样本/秒采样的四年数据，以开发和测试该技术。有五个同时运行的站点代表450天的数据。所有可能结果的混淆矩阵表明，该算法的准确性为97.33％。