Detection and characterization of geomagnetic anomaly waveforms

Abstract

Geomagnetic anomalies are abnormal changes in the geomagnetic field caused by changes in the stress of the underground rock. Research on geomagnetic anomaly signals can help explore earthquake prediction. This study proposes a window-weighted correlation degree (WWC) method to detect geomagnetic vertical component anomaly waveforms, addressing the limitations of traditional methods. The WWC method calculates the similarity between geomagnetic and reference data in daytime and nighttime windows, identifying abnormal signals based on change values. This method achieves a 0.976 accuracy in identifying geomagnetic anomaly waveforms, effectively detecting and recognizing complex waveform features in the geomagnetic field. Further, based on this method, a dataset of geomagnetic anomaly waveforms under various earthquake events was built, identifying seven typical anomalies. Five types of features were selected and constructed to measure the non-random deviation of diurnal geomagnetic variation from the baseline signal, improving the precision and recall of the random forest classification model. The machine learning method can classify and predict geomagnetic anomaly waveforms related to earthquake occurrence with superior precision and recall compared to traditional methods. This approach offers a productive means for identifying geomagnetic anomaly waveforms associated with earthquake occurrence and analyzing the potential correlations between anomalies and seismic events. It also provides a possibility for exploring the physical mechanism involved in geomagnetic anomalies' generation and evolution process. It provides effective data support for further analysis of seismic-magnetic relationships and helps to promote the deep integration of artificial intelligence and earthquake prediction research.