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Earthquake prediction remains a challenging scientific endeavor due to its complex nature and potentially devastating consequences. This study explores the possibility of using fluctuations in Total Electron Content (TEC) within the ionosphere as potential indicators of impending seismic activity. By analyzing data gathered from the BAKO station, an Autoregressive Moving Average (ARMA) model was utilized to anticipate TEC values preceding two significant earthquakes, one in Papua, Indonesia, occurring on February 5, 2004, and another in Western New Guinea on April 6, 2013. Evaluation of the predictive accuracy involves metrics such as Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Normalized RMSE (NRMSE). The findings suggest promise in the ARMA model’s ability to capture TEC variations associated with seismic events, discrepancies between predicted and actual TEC values are evident. This study offers valuable insights into the potential utility of TEC anomalies as precursors to earthquakes, underscoring the necessity for further research to refine prediction capabilities in this domain.

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Prediction of TEC During Indonesia Earthquakes Using ARMA Based on GPS Data

  • 会议论文

  • 发表日期：

    2025年

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  • S. Kiruthiga,
  • S. Mythili,
  • A. Krishnakumar,
  • R. Mukesh,
  • Sarat C. Dass,
  • M. Vijay,
  • M. Praveen Kumar,
  • M. Prashanth

摘要

Earthquake prediction remains a challenging scientific endeavor due to its complex nature and potentially devastating consequences. This study explores the possibility of using fluctuations in Total Electron Content (TEC) within the ionosphere as potential indicators of impending seismic activity. By analyzing data gathered from the BAKO station, an Autoregressive Moving Average (ARMA) model was utilized to anticipate TEC values preceding two significant earthquakes, one in Papua, Indonesia, occurring on February 5, 2004, and another in Western New Guinea on April 6, 2013. Evaluation of the predictive accuracy involves metrics such as Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Normalized RMSE (NRMSE). The findings suggest promise in the ARMA model’s ability to capture TEC variations associated with seismic events, discrepancies between predicted and actual TEC values are evident. This study offers valuable insights into the potential utility of TEC anomalies as precursors to earthquakes, underscoring the necessity for further research to refine prediction capabilities in this domain.