The Robust Collocation Methods of Integration Inversion of GPS and Earthquake Monitoring Data

Abstract eng:
This paper focuses on the mathematical model and computational methods for earthquake monitoring and forecasting by Robust Collocation Methods (RCM) of integration inversion utilizing GPS data, gravity data, geomagnetic data, and seismic data. The method of the paper is based on the various data combined collocation, according to the mathematical principles of signal processes, spectral analysis, and time series analysis. The mathematical modeling for earthquake monitoring and forecasting by combining GPS monitoring data and various common seismic monitoring data (e.g. gravity data, geomagnetic data, seismic data, etc.) is completely set up. The various seismic monitoring data mentioned above is transformed to the same basic framework and optimally combined in the framework. The mathematical modeling of robust covariance functions and robust collocation by the combined data mentioned above is independently proposed and are derived from the corresponding formula. Some important conclusions are attained form the application of GPS monitoring data and common seismic monitoring data is studied in the earthquake monitoring and forecasting. The robust covariance functions and Robust Collocation Method (RCM) by combined GPS monitoring data and common seismic monitoring data. Overcomes the short comings of conventional methods and the Least Squares Collocation (LSC) in the earthquake monitoring and sets up the foundation for increasing the accuracy of earthquake monitoring and forecasting by optimally combining GPS monitoring data and common seismic monitoring data. The new methods will be more and more useful thanks to the growing precision and the generalization of GPS monitoring.

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