Seismic Anomaly Detection in Time Series Electromagnetic Data by the SWARM Satellites

It has been hypothesized that electromagnetic (EM) anomalies act as precursors to seismic ac- tivities. More recently, there have been a lot of studies regarding seismic events and their possi- ble link with EM sequential anomalies from dif- ferent sources. A lot of work has been done such as in [1], where statistical methods have been used to prove this connection. Machine learning (ML) methods were used in [2] . Here, to ana- lyze the data we use simple and computationally e cient methods. The two proposed methods, a novel variant of Cumulative Sum (CUSUM) with Exponentially Weighted Moving Average (EWMA) and a Fuzzy Inspired Approach are evaluated under new EM observations by the SWARM satellites. Speci cally we are investi- gating two seismic events occurred on the 6th of December at 02:43 and 18:20 respectively and their possible causal links with EM anomalies.