Thermal Anomaly Detection prior to earthquakes with training artificial neural networks with ant colony optimization

Document Type : Applied Article


1 Graduate Student at Remote Sensing Department, Surveying and Geo-spatial Information Faculty, College of Engineering, University of Tehran, Tehran, Iran

2 Assistant Professor at Remote Sensing Department, Surveying and Geo-spatial Information Faculty, College of Engineering, University of Tehran, Tehran, Iran

3 Associate Professor at Remote Sensing Department, Surveying and Geo-spatial Information Faculty, College of Engineering, University of Tehran, Tehran, Iran


Remote sensing techniques made it possible to study thermal anomalies prior to major earthquakes regardless of complications in comprehending earthquake mechanisms. Thermal pre-cursors are one the main resources for earthquake prediction. In this article, land surface temperature, atmospheric temperature, surface latent heat flux and outgoing long-wave radiation have been studied to detect anomalies prior to Varzaghan (August 11, 2012), Boushehr (April 9, 2013) and Saravan (April 16, 2013) earthquakes.
To detect earthquake related anomalies, time series of each pre-cursor has been produced within the period of earthquake, land surface temperature and atmospheric temperature were acquired from MODIS products, surface latent heat flux from GLDAS library and outgoing long-wave radiation from AIRS products. These time series were predicted by an artificial neural network with ant colony optimization training method. The results of this study were compared with artificial neural network with Levenberg-Marquardt training algorithm. It has been shown that 10 to 13 days before Varzaghan earthquake, anomalies has appeared in all of the mentioned precursors, in case of Boushehr earthquake 6 to 9 days before the event, anomalies appeared in atmospheric temperature and outgoing long-wave radiation and also a strong anomaly appeared in surface latent heat flux 2 days prior to earthquake and in Saravan earthquakes anomalies have been detected 5 to 8 days before the earthquake in all of the studied thermal pre-cursors.


[1].  Asteriadis, G. and E. Livieratos (1988). Pre-seismic responses of underground water level and temperature concerning a 4.8 magnitude earthquake in Greece on October 20, 1988. Tectonophysics, 170(1), p. 165-169.
[2].  Zu-ji, Q., X. Xiu-Deng, and D. Chang-Gong (1991). Thermal infrared anomaly precursor of impending earthquakes. Chinese Science Bulletin, 36(4), p. 319-323.
[3].  Freund, F. (2009). Stress-activated positive hole charge carriers in rocks and the generation of pre-earthquake signals. Electromagnetic Phenomena Associated with Earthquakes, Transworld Research Network, Trivandrum, p. 41-96.
[4].  Ouzounov, D., et al. (2006). Satellite thermal IR phenomena associated with some of the major earthquakes in 1999–2003. Physics and Chemistry of the Earth, Parts A/B/C,31(4) , p. 154-163.
[5].  Ouzounov, D. and F. Freund (2004). Mid-infrared emission prior to strong earthquakes analyzed by remote sensing data. Advances in Space Research, 33(3), p. 268-273.
[6].  Tronin, A., et al. (2004). Temperature variations related to earthquakes from simultaneous observation at the ground stations and by satellites in Kamchatka area. Physics and Chemistry of the Earth, Parts A/B/C, 29(4), p. 501-506.
[7].  Tronin, A.A., M. Hayakawa, and O.A. Molchanov (2002). Thermal IR satellite data application for earthquake research in Japan and China. Journal of Geodynamics, 33(4), p. 519-534.
[8].  Saradjian, M. and M. Akhoondzadeh (2011). Thermal anomalies detection before strong earthquakes (M> 6.0) using interquartile, wavelet and Kalman filter methods. Natural Hazards and Earth System Science, 11(4), p. 1099-1108.
[9].  Akhoondzadeh, M. (2013). A comparison of classical and intelligent methods to detect potential thermal anomalies before the 11 August 2012 Varzeghan, Iran, earthquake (M w= 6.4). Natural Hazards and Earth System Science, 13(4), p. 1077-1083.
[10].  Akhoondzadeh, M. (2013). Thermal and TEC anomalies detection using an intelligent hybrid system around the time of the Saravan, Iran,(Mw= 7.7) earthquake of 16 April 2013. Advances in Space Research, 2014. 53(4), p. 647-655.
[11].  Blum, C. and K. Socha (2005). Training feed-forward neural networks with ant colony optimization: An application to pattern classification. in Hybrid Intelligent Systems, 2005. HIS'05. Fifth International Conference on, IEEE.
[12].  Wan, Z. and Z.-L. Li (1997). A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. Geoscience and Remote Sensing, IEEE Transactions on, 35(4), p. 980-996.
[13].  Panda, S., et al. (2007). MODIS land surface temperature data detects thermal anomaly preceding 8 October 2005 Kashmir earthquake. International Journal of Remote Sensing, 28(20), p. 4587-4596.
[14].  Ouzounov, D., et al. (2011). Atmospheric signals associated with major earthquakes. A multi-sensor approach.
[15].  Dey, S. and R. Singh (1999). Surface latent heat flux as an earthquake precursor. Natural Hazards and Earth System Science, 3(6), p. 749-755.
[16].  Chen, M., et al. (2006). Surface latent heat flux anomalies prior to the Indonesia Mw9. 0 earthquake of 2004. Chinese Science Bulletin, 51(8), p. 1010-1013.
[17].  Ouzounov, D., et al. (2007). Outgoing long wave radiation variability from IR satellite data prior to major earthquakes. Tectonophysics, 431(1), p. 211-220.
[18].  Pulinets, S., et al. (2006). Thermal, atmospheric and ionospheric anomalies around the time of the Colima M7. 8 earthquake of 21 January 2003. in Annales Geophysicae.
[19].  Xiong, P., Y. Bi, and X. Shen (2009). A Wavelet-Based Method for Detecting Seismic Anomalies in Remote Sensing Satellite Data, in Machine Learning and Data Mining in Pattern Recognition, Springer, p. 569-581.
[20].  Akhoondzadeh, M. (2013).  A MLP neural network as an investigator of TEC time series to detect seismo-ionospheric anomalies, Advances in Space Research, 51(11), p. 2048-2057.
[21]. Akhoondzadeh, M. and M. Saradjian (2011). TEC variations analysis concerning Haiti (January 12, 2010) and Samoa (September 29, 2009) earthquakes, Advances in Space Research, 47(1), p. 94-104.
[22].  Zhang, G.P. (2001). An investigation of neural networks for linear time-series forecasting. Computers & Operations Research, 28(12), p. 1183-1202.
[23].  Dorigo, M. and L.M. Gambardella (1997). Ant colony system: a cooperative learning approach to the traveling salesman problem, Evolutionary Computation, IEEE Transactions on, 1(1), p. 53-66.
[24].  Pao, H.-T. (2007). Forecasting electricity market pricing using artificial neural networks. Energy Conversion and Management, 48(3), p. 907-912.