Abstract:To improve the accuracy of the analysis of ionospheric disturbance data and eliminate anomalous data, this study proposes a detection method for seismic ionospheric disturbance anomaly data based on DBSCAN. According to the characteristics of wireless sensor network data, the Euclidean distance is set as the index, and different ionospheric disturbance data are compared and clustered. The similarities of the seismic ionospheric disturbance data are then calculated. The DBSCAN method is used to extract relevant environmental feature sets, through which the seismic ionospheric disturbance anomaly data can be detected. The run time, false positive rate, false negative rate, and detection rate of the proposed method are compared with those of a traditional method. Experimental results show that the proposed method requires minimal time, significantly reduces false positive and false negative rates, and improves detection rates.