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 , where statistical methods have been used to prove this connection. Machine learning (ML) methods were used in  . 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.
|Title of host publication||Unknown Host Publication|
|Publisher||European Space Agency|
|Number of pages||1|
|Publication status||Published - 22 Jun 2015|
|Event||In: Dragon 3 symposium. ESA Communication - |
Duration: 22 Jun 2015 → …
|Conference||In: Dragon 3 symposium. ESA Communication|
|Period||22/06/15 → …|
- Seismic Anomaly Detection
- Electromagnetic Data
- SWARM Satellites
FingerprintDive into the research topics of 'Seismic Anomaly Detection in Time Series Electromagnetic Data by the SWARM Satellites'. Together they form a unique fingerprint.
Development and Application of Collective Anomaly Detection methods to Electromagnetic Satellite DataAuthor: Christodoulou, V., Sep 2020
Student thesis: Doctoral Thesis