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

Vyron Christodoulou, Yaxin Bi, Gouge Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages1
Publication statusPublished - 22 Jun 2015
EventIn: Dragon 3 symposium. ESA Communication -
Duration: 22 Jun 2015 → …

Conference

ConferenceIn: Dragon 3 symposium. ESA Communication
Period22/06/15 → …

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Learning systems
Time series
Statistical methods
Satellites

Keywords

  • Seismic Anomaly Detection
  • Electromagnetic Data
  • SWARM Satellites

Cite this

Christodoulou, V., Bi, Y., & Zhao, G. (2015). Seismic Anomaly Detection in Time Series Electromagnetic Data by the SWARM Satellites. In Unknown Host Publication
Christodoulou, Vyron ; Bi, Yaxin ; Zhao, Gouge. / Seismic Anomaly Detection in Time Series Electromagnetic Data by the SWARM Satellites. Unknown Host Publication. 2015.
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title = "Seismic Anomaly Detection in Time Series Electromagnetic Data by the SWARM Satellites",
abstract = "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.",
keywords = "Seismic Anomaly Detection, Electromagnetic Data, SWARM Satellites",
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Christodoulou, V, Bi, Y & Zhao, G 2015, Seismic Anomaly Detection in Time Series Electromagnetic Data by the SWARM Satellites. in Unknown Host Publication. In: Dragon 3 symposium. ESA Communication, 22/06/15.

Seismic Anomaly Detection in Time Series Electromagnetic Data by the SWARM Satellites. / Christodoulou, Vyron; Bi, Yaxin; Zhao, Gouge.

Unknown Host Publication. 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Seismic Anomaly Detection in Time Series Electromagnetic Data by the SWARM Satellites

AU - Christodoulou, Vyron

AU - Bi, Yaxin

AU - Zhao, Gouge

PY - 2015/6/22

Y1 - 2015/6/22

N2 - 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.

AB - 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.

KW - Seismic Anomaly Detection

KW - Electromagnetic Data

KW - SWARM Satellites

M3 - Conference contribution

BT - Unknown Host Publication

ER -