Orlecka-Sikora, Beata (2006) Resampling Methods for Improving the Accuracy of Probabilistic Seismic Hazard Analysis. Publications of the Institute of Geophysics, M-39 (395). pp. 63-76.
Full text not available from this repository.Abstract
The cumulative distribution function (cdf) of the magnitude or energy of seismic events is one of the most important probabilistic characteristics in the Probabilistic Seismic Hazard Analysis. The probability distribution function (pdf) of the seismic events magnitude can be estimated through the parametric approach where the magnitude distribution model comes from the Gutenberg–Richter relation or the nonparametric approach using the kernel density estima-tors. In the nonparametric case, the pdf of the magnitude density estimator is un-known so the confidence intervals of the magnitude cdf are unable to be calcu-lated using the classical methods of mathematical statistics. The evaluation of the magnitude cdf amounts to the point estimate. The same concerns the seismic hazard parameters. To assess and reduce errors in the seismic events magnitude or energy estimation, and thereby in the seismic hazard parameters evaluation in nonparametric approach, we propose using resampling methods: the bootstrap and jackknife. These two resampling techniques applied to a one data set provide many replicas of this sample, which preserve its probabilistic properties. In this paper we present an example of the use of the developed nonparametric interval estimation algorithm based on the bias corrected and accelerated method (BCa method), with iterated and smooth bootstrap for characterizing the seismicity from an underground copper mine in the Legnica–Głogów Copper District in Poland.
| Item Type: | Article |
|---|---|
| Subjects: | Region > Poland > Legnica-Glogow Copper District Inducing technology > Underground mining Methodology > Method and procesing |
| Project: | IS-EPOS project > LGCD: Regional seismicity and ground motion associating underground hard rock mining |