An Enhanced SAR-Based Tsunami Detection System

Authors: Jean-Pierre Dubois, Jihad S. Daba, H. Karam, J. Abdallah

Abstract:

Tsunami early detection and warning systems have proved to be of ultimate importance, especially after the destructive tsunami that hit Japan in March 2012. Such systems are crucial to inform the authorities of any risk of a tsunami and of the degree of its danger in order to make the right decision and notify the public of the actions they need to take to save their lives. The purpose of this research is to enhance existing tsunami detection and warning systems. We first propose an automated and miniaturized model of an early tsunami detection and warning system. The model for the operation of a tsunami warning system is simulated using the data acquisition toolbox of Matlab and measurements acquired from specified internet pages due to the lack of the required real-life sensors, both seismic and hydrologic, and building a graphical user interface for the system. In the second phase of this work, we implement various satellite image filtering schemes to enhance the acquired synthetic aperture radar images of the tsunami affected region that are masked by speckle noise. This enables us to conduct a post-tsunami damage extent study and calculate the percentage damage. We conclude by proposing improvements to the existing telecommunication infrastructure of existing warning tsunami systems using a migration to IP-based networks and fiber optics links.

Keywords: Detection, GIS, GSN, GTS, GPS, speckle noise, synthetic aperture radar, tsunami, wiener filter.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1094253

References:

[1] W. M. Adams, J. M. Jordaan, "Tsunamis and tsunami warning systems,” in J. M. Jordaan & A. Bell (Eds.), Hydraulic Structures, Equipment and Water Data Acquisition Systems, Vol. 1, pp. 238 – 261, 2011.
[2] Australian Government Bureau of Meteorology, Tsunami Facts and Information, Sept.18, 2011.
[3] G. Bugliarello, "Tsunami simulations and numerical models,” Systems Challenges on a Global Scale, Vol. 35, No.2, 2005.
[4] D. Chang-Seng, "Seychelles progress report towards a Tsunami warning system in the Indian ocean,” 2006.
[5] DART, "Deep-ocean assessment and reporting of tsunamis description,” National Data Buoy Center, May 6, 2011.
[6] Envirtech, "Technical specifications of underwater module,” Sept. 3, 2011.
[7] K. Horsburgh, L. Bradley, M. Angus, D. Smith, E. Wijeratne, and P. Woodworth, "High Frequency Sea Level Recording For Tsunami Warning and Enhanced Storm Surge Monitoring at UK Sites,” Natural Environmental Research Council Open Research Archive, 2010.
[8] W. Morissay, "Tsunamis: Monitoring, Detection, and Early Warning Systems,” The Library of Congress Congressional Research Service, Washington DC, 2005.
[9] R. Stosius, G. Beyerle, M. Semmling, A. Helm, A. Hoechner, J. Wickert, and J. Lauterjung, "Tsunami detection from space using GNSS reflections: Results and activities from GFZ,” Geoscience and Remote Sensing Symposium (IGARSS), pp. 3047 – 3050, 2010.
[10] Y. Teshirogi, J. Sawamoto, N. Segawa, and E. Sugino, "A proposal of tsunami warning system using area mail disaster information service on mobile phones,” Advanced Information Networking and Applications Workshops, IEEE International, pp. 890-895, 2009.
[11] Forecast Inundation Models, NOAA Center for Tsunami Research, May 6, 2011. (nctr.pmel.noaa.gov/forecast_inundation_models.html)
[12] J. Dubois and O. Abdul-Latif, "SVM-Based Detection of SAR Images in Partially Developed Speckle Noise,” Transactions on Engineering, Computing, and Technology, Vol. 12, pp. 139-143, 2005.
[13] J. S. Daba and M. R. Bell, "Synthetic-Aperture-Radar Surface Reflectivity Estimation Using a Marked Point-Process Speckle Model,” Optical Engineering, Vol. 42, No. 1, pp.211-227, January 2003.
[14] J. Dubois, "Segmentation of Speckled Ultrasound Images Based on a Statistical Model,” Proceedings of the 16th International EURASIP Conference BIOSIGNAL 2002, Vol. 16, Brno, Czech Republic, June 2002.
[15] J. Daba, "Improved Segmentation of Speckled Images Using an Arithmetic-to-Geometric Mean Ratio Kernel”, International Journal of Computer and Information Engineering, Vol. 1, No. 4, pp.218-221, 2007.
[16] J. S. Daba and M. R. Bell, "Estimation of the Surface Reflectivity of SAR Images Based on a Marked Poisson Point Process Model,” IEEE International Symposium on Signals, Systems, and Electronics, San Francisco, USA, October 25, 1995.
[17] J. S. Daba and M. R. Bell, "Segmentation of Speckled Images Using a Likelihood Random Field Model,” Optical Engineering, Vol. 47, No. 1, pp. 017005-1 to 017005-20, Jan. 2008.
[18] J. S. Daba and M. R. Bell, "Synthetic-Aperture-Radar Surface Reflectivity Estimation Using a Marked Point-Process Speckle Model,” Optical Engineering, Vol. 42, No. 1, pp.211-227, January 2003.