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 (in print/issue) - 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
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Development and Application of Collective Anomaly Detection methods to Electromagnetic Satellite DataAuthor: Christodoulou, V., Sept 2020
Supervisor: Wilkie, G. (Supervisor) & Bi, Y. (Supervisor)
Student thesis: Doctoral ThesisFile