EarthquakePrediction Using Bi-nomial Logistic Regression
In a variant，the method comprises using empirical data，such as found on the USGS database，or collecting new samples by satellite，to determine a base model for why and when earthquakes occur. The method seeks to statistically evaluate all possible variables that lead to the sequence of events，which precede the actual slippage and reformation of the earth's crust. The author seeks to integrate electromagnetic，solar or other signals seeking to combine all possible variables that can be accurately measured and quantified. The author assumes that there may be multiple causes and variables，which when combined result in a seismic event. The author also assumes that inference can be accurately applied，by way of studying micro-quakes and using that resulting data to generate statistical models geared towards predicting future seismic activity on a larger scale.Current technology allows us to predict quakes within 10 seconds，the author teaches that either earthquakes lay outside other natural phenomenon or the data is incomplete or being misinterpreted. This model will run binomial regression analysis using Bayesian analysis to take new or unknown variables into account and input them into the existing model. We will seek to combine as many variables as possible so that a user can ultimately mitigate damage，in terms of the cost of human lives，that are now are judged highly at risk.If the current monitoring methods are deemed to be insufficient we address these in the scope of our work.