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| Seismic comprehensive forecast based on modified project pursuit regression |
| Anxu Wu1, Xiangdong Lin1, Changsheng Jiang2, Yongxian Zhang3, Xiaodong Zhang3, Mingxiao Li3, Ping’an Li1 |
1 Earthquake Administration of Beijing Municipality, Beijing 100080, China
2 Institute of Geophysics, China Earthquake Administration, Beijing 100081, China
3 China Earthquake Networks Center, Beijing 100045, China |
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Abstract In the research of projection pursuit for seismic comprehensive forecast, the algorithm of projection pursuit regression (PPR) is one of most applicable methods. But generally, the algorithm structure of the PPR is very complicated. By partial smooth regressions for many times, it has a large amount of calculation and complicated extrapolation, so it is easily trapped in partial solution. On the basis of the algorithm features of the PPR method, some solutions are given as below to aim at some shortcomings in the PPR calculation: to optimize project direction by using particle swarm optimization instead of Gauss-Newton algorithm, to simplify the optimal process with fitting ridge function by using Hermitian polynomial instead of piecewise linear regression. The overall optimal ridge function can be obtained without grouping the parameter optimization. The modeling capability and calculating accuracy of projection pursuit method are tested by means of numerical emulation technique on the basis of particle swarm optimization and Hermitian polynomial, and then applied to the seismic comprehensive forecasting models of poly-dimensional seismic time series and general disorder seismic samples. The calculation and analysis show that the projection pursuit model in this paper is characterized by simplicity, celerity and effectiveness. And this model is approved to have satisfactory effects in the real seismic comprehensive forecasting, which can be regarded as a comprehensive analysis method in seismic comprehensive forecast.
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Received: 27 April 2008
Published: 10 October 2009
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Corresponding Authors:
Anxu Wu
E-mail: wu-anxu@163.com.
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