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Detection of ULF Geomagnetic Anomalies Prior to the Tohoku-Oki Earthquake by the Multireference Station Method

The ultralow-frequency (ULF) electromagnetic anomaly has been considered one of the earthquake precursory signals with the potential for short-term prediction. As such, effectively detecting ULF anomalies is important for mitigating… Click to show full abstract

The ultralow-frequency (ULF) electromagnetic anomaly has been considered one of the earthquake precursory signals with the potential for short-term prediction. As such, effectively detecting ULF anomalies is important for mitigating earthquake disasters. Given the comprehensive coverage of geomagnetic networks in Japan, the 2011 Tohoku-Oki earthquake (M9.0) provided a tremendous opportunity for investigating the characteristics and mechanisms of ULF anomalies. Previous studies reporting detections of ULF anomalies triggered by the Tohoku-Oki earthquake have been questioned on the grounds of insufficient reliability due to technological limitations. In this study, we employ a multireference station data-quality-weighted method to detect the ULF anomaly. Comparison with traditional single-reference station method demonstrates the robustness of our detection results. A statistical test also indicates that the ULF anomaly appearing approximately two months before the earthquake was driven by physical processes. Geomagnetic storm analysis further rules solar activity as the cause of the anomaly. The spatial distribution of the anomaly amplitude reveals a decrease in the anomaly energy with increasing epicentral distance, implying a strong association between the anomaly and the earthquake. Our study is helpful for understanding the possible connection between the ULF electromagnetic precursors and the seismogenic processes of strong earthquakes, which contributes to achieving the ultimate goal of short-term earthquake prediction.

Keywords: method; tohoku oki; multireference station; oki earthquake; earthquake

Journal Title: IEEE Transactions on Geoscience and Remote Sensing
Year Published: 2024

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