ing remote sensing satellite techniques applied to land and sea surface temperatures [7]. Thermal anomalies associated with strong earthquakes have been observed at various levels, from the ground surface up to the top of clouds. At present, the most promising is the Outgoing Longwave Radiation (OLR) anomaly measured at the top of clouds [10]. The advantage of this method is that it measures all of the infrared radiation emitted from the Earth’s surface and atmosphere within the transparency window of 8 - 12 microns. OLR is currently mapped by the AIRS (Atmospheric Infrared Sounder) instrument launched into orbit in 2002. AIRS is one of six instruments on board the Aqua satellite, part of NASA’s Earth Observing System.

6. Global Earthquake Prediction. Practical Cases

Let’s demonstrate a few cases of real stress gradually accumulating before major earthquakes (Figure 1 and Figure 2). At some point the stressed area becomes detectable for our prediction systems. In many cases, the epicenter of a forthcoming earthquake is located near the center of the stressed area. However, in some cases, the epicenter is closer to the boundaries of the stressed area. A possible explanation is that the rupture zone represents a better indicator for major earthquakes (rather than the epicenter). The rupture may reach a length of 1300 km for M9 events [23].

7. Results

A few recent successful prediction cases are shown in Figures 3-6.

8. Conclusions

Terra Seismic can predict most major earthquakes (M6.2 or greater) at least 2 - 5 months before they will strike. Global earthquake prediction is based on determinations of the stressed areas that will start to behave abnormally before major earthquakes. The size of the observed stressed areas roughly corresponds to estimates calculated from Dobrovolsky’s formula. To identify abnormalities and

(a) (b) (c) (d)

Figure 1. Example of prognostic signal analysis for M9.1 Tohoku earthquake 11 Mar 2011: (a) Mar 2010, (b) Aug 2010, (c) Mar 2011, (d) Apr 2011, after the shock. Note that the green stressed area grew larger in March 2011 due to an increased accumulation of stress. The red ellipse indicates the prognostic signal.

make predictions, Terra Seismic applies various methodologies, including satellite remote sensing methods and data from ground-based instruments. We currently

(a) (b) (c) (d)

Figure 2. An example of how multiple earthquakes developed simultaneously in Indonesia in 2018. Area A: preparation of M6.4 quake on 28.07.2018, M6.9 quake on 05.08.2018, M6.3 quake and M6.9 quake on 19.08.2019 in Lombok Region. Area B: preparation of M6.2 quake on 28.08.2018 in the East Timor region. (a) Dec 2017, (b) Jan 2018, (c) Jul 2018, (d) Sep 2018, after the shocks. Note that the cyan stressed areas grew larger due to an increased accumulation of stress. The red ellipse indicates the prognostic signal.

Figure 3. Example of prediction and real quake comparison for 22.10.2018 M6.8 earthquake in Vancouver Island, Canada region. Yellow circle indicates prognostic area and yellow dot shows the location of epicenter.

Figure 4. Example of predicted M7.3 earthquake in Banda Sea, Indonesia. Yellow circle indicates prognostic area and yellow dot shows the location of epicenter.

Figure 5. Example of predicted M6.9 earthquake in Southwest of Sumatra, Indonesia. Yellow circle indicates prognostic area and yellow dot shows the location of epicenter.

process terabytes of information daily, and use more than 80 different multiparameter prediction systems. Alerts are issued if the abnormalities are confirmed by at least five different systems. We observed that geophysical patterns of earthquake development and stress accumulation are generally the same for all key seismic regions. Thus, the same earthquake prediction methodologies and systems can be applied successfully worldwide.

Figure 6. Example of predicted M6.4 earthquake in Puerto Rico region. Yellow circle indicates prognostic area and yellow dot shows the location of epicenter.

Stress gradually accumulates before a major earthquake. To measure the different stages of stress accumulation, we have developed long-term (from 2 to 5 years), mid-term (from 2 months to 2 years), and short-term (from 10 to 60 days) global prediction systems. The most reliable are the mid-term systems that can predict most major earthquakes at least 2 - 5 months in advance. In some cases we can determine the final stage of stress build-up. We can also predict the epicenter of a forthcoming earthquake with a high degree of confidence to within a radius of 150 - 250 km. Terra Seismic currently provides earthquake predictions for 25 key earthquake-prone regions. Our technology has been used to retrospectively test data gathered since 1970 and it successfully detected about 90 percent of all significant quakes over the last 50 years. Throughout 2017-2020, Terra Seismic’s work was presented to more than 150 university professors from 63 countries. Our technology has been in practical use since 2013.

Our paramount priority is to help governments save human lives. Terra Seismic calls for collaboration with all governments and agencies responsible for dealing with natural disasters.

Acknowledgements

This project was not possible without the scientific data provided by different government agencies, international organizations, science institutions and academia. We would like to acknowledge their leading contribution to Earth and space data collection.

We wish thank to US Geological Survey (USGS), European-Mediterranean Seismological Centre (EMSC), Japanese Meteorological Agency (JMA), National Aeronautical and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), European Space Agency (ESA), International GNSS Service (IGS), Jet Propulsion Laboratory (JPL)/Caltech, Ionospheric Prediction Service (IPS), Weather Underground and World Data Center (WDC) in Kyoto, Japan.

Cite this paper
Elshin, O. and Tronin, A. (2020) Global Earthquake Prediction Systems. Open Journal of Earthquake Research, 9, 170-180. doi: 10.4236/ojer.2020.92010.
References

[1]   Keilis-Borok, V.I. and Kossobokov, V.G. (1990) Premonitory Activation of Earthquake Flow: Algorithm M8. Physics of the Earth and Planetary Interiors, 61, 73-83.
https://doi.org/10.1016/0031-9201(90)90096-G

[2]   Rathje, E.M. and Adams, B.J. (2008) The Role of Remote Sensing in Earthquake Science and Engineering: Opportunities and Challenges. Earthquake Spectra, 24, 471-492.
https://doi.org/10.1193/1.2923922

[3]   Liu, W.L. and Liu, Y.C. (2012) Applicability of Several Seismic Wave Parameters in Earthquake Prediction. International Research Journal of Geology and Mining, 2, 32-40.

[4]   Ogata, Y. (2013) A Prospect of Earthquake Prediction Research. Statistical Science, 28, 521-541. https://doi.org/10.1214/13-STS439

[5]   Bobrowsky, P. (2013) Encyclopedia of Natural Hazards. Springer, Dorchester, 1135 p. https://doi.org/10.1007/978-1-4020-4399-4

[6]   Ghaedi, K. and Ibrahim, Z. (2017) Earthquake Prediction. In: Zouaghi, T., Ed., Earthquakes—Tectonics, Hazard and Risk Mitigation, IntechOpen, London.
https://www.intechopen.com/books/earthquakes-tectonics-hazard-and-risk-mitigation/earthquake-prediction
https://doi.org/10.5772/65511


[7]   Tronin, A.A. (2010) Satellite Remote Sensing in Seismology. A Review. Remote Sensing, 2, 124-150. https://doi.org/10.3390/rs2010124

[8]   Alvan, H.V. and Azad, F.H. (2011) Satellite Remote Sensing in Earthquake Prediction. A Review. National Postgraduate Conference, Kuala Lumpur, 1-5.
https://doi.org/10.1109/NatPC.2011.6136371

[9]   Alvan, H.V. and Omar, H. (2011) Overview of Remote Sensing Techniques in Earthquake Prediction. Journal of Engineering, Design and Technology, 9, 164-177.
https://doi.org/10.1108/17260531111151050

[10]   Kong, X., Li, N., Lin, L., Xiong, P. and Qi, J. (2018) Relationship of Stress Changes and Anomalies in OLR Data of the Wenchuan and Lushan Earthquakes. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 2966-2976.
https://doi.org/10.1109/JSTARS.2018.2839089

[11]   Kalvik, J. (2019) Nobel Prize to Oleg Elshin and Terra Seismic Will Help Protect Humanity from Earthquakes and Tsunamis.
http://www.etterretningen.no/2019/08/16/nobel-prize-to-oleg-elshin-and-terra-seismic-will-help-protect-humanity-from-earthquakes-and-tsunamis

[12]   Carlucci, R. (2018) Terra Seismic con i Big Data satellitari candidata al Nobel per la Pace nella previsione dei terremoti.
https://rivistageomedia.it/2018050314651/Scienze-della-Terra/terra-seismic-con-i-big-data-satellitari-candidata-al-nobel-per-la-pace-nella-previsione-dei-terremoti-1

[13]   Marr, B. (2015) Big Data: Saving 13,000 Lives a Year by Predicting Earthquakes?
http://www.forbes.com/sites/bernardmarr/2015/04/21/big-data-saving-13000-lives-a-year-by-predicting-earthquakes

[14]   Norway News (2018) Terra Seismic Can Save Millions of Lives and Create a New, Safer Earth for Mankind.
http://www.norwaynews.com/terra-seismic-saving-millions-lives-a-year-by-predicting-earthquakes

[15]   Kassandra Petsas, Bodoposten (2018) Terra Seismic nominert til Nobels Fredspris: Eksklusivt intervju!
http://xn--bodposten-n8a.no/terra-seismic-nominert-til-nobels-fredspris-eksklusivt-intervju

[16]   Alice Scarsi, Express (2018) Earthquakes Can Be Predicted to “Protect Mankind”. Nobel Prize Nominee Claims.
http://www.express.co.uk/news/world/1049598/earthquakes-today-forecast-news-earthquakes-terra-seismic

[17]   Kassandra Petsas, Bodoposten (2018) Terra Seismic kan skape en ny, tryggere verden. http://xn--bodposten-n8a.no/terra-seismic-kan-skape-en-ny-tryggere-verden

[18]   CNN Philippines (2019) Live Interview with Oleg Elshin.
https://www.youtube.com/watch?v=x1erNJVtM4U&fbclid
=IwAR19kZFjwZgiA3B6bP_3hmF_uJ3m2_p3YC3uGJIfZ1Uyop7uybDYV__kU9E


[19]   BNAmericas (2015) Predicting Earthquakes “No Longer a Rough Science”.
http://www.bnamericas.com/en/news/insurance/predicting-earthquakes-no-longer-a-rough-science

[20]   Marr, B. How Terra Seismic Uses Big Data in Practice.
https://www.bernardmarr.com/default.asp?contentID=729

[21]   Marr, B. (2016) Big Data in Practice. How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. In: Terra Seismic: Using Big Data to Predict Earthquakes, Wiley, Hoboken, Chapter 39.
https://doi.org/10.1002/9781119278825

[22]   Dobrovolsky, I.P., Zubkov, S.I. and Miachkin, V.I. (1979) Estimation of the Size of Earthquake Preparation Zones. Springer, Berlin.
https://link.springer.com/article/10.1007/BF00876083
https://doi.org/10.1007/BF00876083

[23]   Lay, T., et al. (2005) The Great Sumatra-Andaman Earthquake of 26 December 2004. Science, 308, 1127-1133. https://doi.org/10.1126/science.1112250

 
 
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