Earthquake Prediction By Suganth Kannan President MathforUS LLC Florida USA Objective The objective of this research is to improve upon a previously developed Innovative Mathematical Model for Earthquake Prediction ID: 1039854
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1. Improving Innovative Mathematical Model for Earthquake PredictionBy: Suganth KannanPresident, MathforUS LLC Florida, USA
2. ObjectiveThe objective of this research is to improve upon a previously developed Innovative Mathematical Model for Earthquake Prediction.
3. Background Research M8-algorithmVapor theory and the earthquake cloud for predictionGeoelectrical signals near Wak-Air and Boe-Air Dipoles during Izu Island Earthquake cluster
4. Background Research Richter Scale - Logarithmic Epicenter – Point on surface above origin of earthquake Intensity - Depends on distance from epicenter and earthquake’s magnitude
5. Spanish Researcher Earthquake Correlation Theory and Magnet TheoryLow levels of Dilatancy before major earthquakes US, EU, Japan experiment on factors – Land deformations, Seismic wave velocities, Geomagnetic/electric phenomenonsBackground Research
6. MaterialsPersonal Workstation with 1 GB Graphic CardUSGS NEIC Data on past earthquakesGoogle Earth ProgramKML earthquake data filesMicrosoft ExcelPhotoshop Program
7. California, Central USA, Northeast USA, Hawaii, Turkey, and JapanSpatial Connection ModelPoisson Range Identifier (Pri) FunctionDistance Factor (Df) Past Research
8. California Zone Split into Two for ValidationIncorporation of Population Centers Current Research
9. Zonal Limits
10. Using NEIC database, earthquake data collected in KML filesAnalyzed using Spatial connection model – Based on logical assumption that all earthquakes in a fault zone are related to one another Procedure
11. Procedure Range Identifier Function f(ri) = {[x1 * time lag 2] / [ Cos(ϴ) * x2 * time lag 1]}Relationship exists based on Angle, Distance, and Time
12. ProcedurePoisson Distribution operation applied for all Pri valuesUtilizing Distance Factor, Pri value for set is determined
13. ProcedureExponential Distribution operation applied for all Range Identifier valuesUtilizing Distance Factor, Exponential value for fault zone is determined
14. Results
15. Discussion-Interpretation of ResultsFault zones have identifiable patterns in a predictable fashionFuture Predictions can be made using the improved model
16. Significance of Study Major Improvement Greater accuracy in combination FEMA can allocate necessary resources
17. Applications and Future ResearchSave millions of lives by creating an evacuation timeframeFewer Insurance claimsSave money for various industriesPotentially could save billions of $$$$ for economy.Team up with Leading Seismology Departments and utilize developed software
18. Thank You for listening to my Presentation. Are there any questions?Q & A?