EPISODES platform documents: No conditions. Results ordered -Date Deposited. 2023-12-06T21:51:48ZEPrintshttps://episodesplatform.eu/images/sitelogo.pnghttps://episodesplatform.eu/eprints/2015-02-17T11:41:22Z2018-03-28T09:53:03Zhttp://episodesplatform.eu/eprints/id/eprint/1539This item is in the repository with the URL: http://episodesplatform.eu/eprints/id/eprint/15392015-02-17T11:41:22ZEstimation of earthquake hazard parameters from incomplete data files. Part I. Utilization of extreme and complete catalogs with different threshold magnitudesThe maximum likelihood estimation of earthquake hazard parameters (maximum regional magnitude, mmax, earthquake activity rate λ, and b parameter in the Gutenberg-Richter equation) is extended to the case of mixed data containing large historical events and recent complete observations. The method accepts variable quality of complete data in different parts of a catalog with different threshold magnitude values. As an illustration, the procedure is applied for the estimation of seismicity parameters in the area of Calabria and eastern Sicily.Andrzej Kijkokijko@geoscience.orgMarkvard A. Sellevoll2015-02-17T11:40:24Z2018-03-28T09:52:45Zhttp://episodesplatform.eu/eprints/id/eprint/1492This item is in the repository with the URL: http://episodesplatform.eu/eprints/id/eprint/14922015-02-17T11:40:24ZNon-parametric Seismic Hazard in MinesSeismic hazard analysis methods in mines are reviewed for the purpose of selecting the best technique. To achieve this goal, the most often-used hazard analysis procedure, which is based on the classical frequency-magnitude Gutenberg-Richter relation, as well as alternative procedures are investigated.
Since the maximum regional seismic event magnitude m max is of paramount importance in seismic hazard analysis, this work provides a generic formula for the evaluation of this important parameter. The formula is capable of generating solutions in different forms, depending on the assumptions of the model of the magnitude distribution and/or the available information regarding past seismicity. It includes the cases (i) in which seismic event magnitudes are distributed according to the truncated frequency-magnitude Gutenberg-Richter relation, and (ii) in which no specific model of the magnitude distribution is assumed.
Both synthetic, Monte-Carlo simulated seismic event catalogues, and actual data from the copper mine in Poland and gold mine in South Africa, are used to demonstrate the discussed hazard analysis techniques.
Our studies show that the non-parametric technique, which is independent of the assumed model of the distribution of magnitude, provides an appropriate tool for seismic hazard assessment in mines where the magnitude distribution can be very complex.Andrzej Kijkokijko@geoscience.orgStanislaw Lasockilasocki@igf.edu.plGerhard Grahamgerhardg@geoscience.org2015-02-17T11:38:49Z2018-03-28T09:53:18Zhttp://episodesplatform.eu/eprints/id/eprint/1345This item is in the repository with the URL: http://episodesplatform.eu/eprints/id/eprint/13452015-02-17T11:38:49ZSeismic hazard assessment under complex source size distribution of mining-induced seismicityIt is well-documented that a variety of factors controlling the rockmass fracturing process in mines often results in a complexity of mining event size distribution. In such cases, the estimation of the probability functions of source size parameterizations, with the use of presently known distribution models, brings about an unacceptable and systematic over- or underestimation of the seismic hazard parameters. It is, therefore, recommended that the non-parametric, kernel estimators of the event size distribution functions, be applied to stationary hazard studies in mining seismicity.
These data-driven estimators, adapted to seismic source size characterization, accurately fit all kinds of data underlying distributions, regardless of their complexity. Recently, the non-parametric approach to size characterization was supported by a special method of uncertainty analysis based on resampling techniques. At present, it is a fully developed method, which provides point and interval estimates of size distribution functions and related hazard parameters. Two examples of its use in studying mining seismic data are presented and discussed in this paper. The analyzed data sets were recorded in two different copper mines in Poland. The smoothed bootstrap test for multimodality, which is a specialized tool for investigating the shapes of probability densities, provided highly significant proof that in both cases the probability densities of source size parameterization were complex thus implied the superiority of the non-parametric estimation to the classic, model-based approach in the studied cases. The data were then used to construct non-parametric, kernel estimates of the source size cumulative distribution function (CDF), the exceedance probability and the mean return period. Furthermore, confidence intervals for these quantities were also estimated. The intervals for CDF were narrow, showing that the procedures of non-parametric estimation and resampling based uncertainty analysis were precise. Due to the fact that the mean return period is very sensitive to values of the CDF, in particular for larger events sizes, the uncertainty of the return period estimates was not insignificant but remained manageable. The point and interval estimates of source size CDF and hazard parameters so obtained were compared with the respective point estimates achieved from the inappropriate in the case of complex magnitude distributions, model-based approach.Stanislaw Lasockilasocki@igf.edu.plBeata Orlecka-Sikora2015-02-17T11:36:37Z2018-03-28T09:53:36Zhttp://episodesplatform.eu/eprints/id/eprint/1282This item is in the repository with the URL: http://episodesplatform.eu/eprints/id/eprint/12822015-02-17T11:36:37ZBias, variance and computational properties of Kijko’s estimators of the upper limit of magnitude distribution, MmaxIt is often assumed in probabilistic seismic hazard analysis that the magnitude distribution has an upper limit M max, which indicates a limitation on event size in specific seismogeneic conditions. Accurate estimation of M max from an earthquake catalog is a matter of utmost importance. We compare bias, dispersion and computational properties of four popular M max estimators, introduced by Kijko and others (e.g., Kijko and Sellevoll 1989, Kijko and Graham 1998, Kijko 2004) and we recommend the ones which can be the most fruitful in practical applications. We provide nomograms for evaluation of bias and standard deviation of the recommended estimators for combinations of sample sizes and distribution parameters. We suggest to use the bias nomograms to correct the M max estimates. The nomograms of standard deviation can be used to determine minimum sample size for a required accuracy of M max.Stanislaw Lasockilasocki@igf.edu.plPawel Urbanurban@igf.edu.pl2015-02-09T13:06:59Z2019-11-07T06:02:12Zhttp://episodesplatform.eu/eprints/id/eprint/1552This item is in the repository with the URL: http://episodesplatform.eu/eprints/id/eprint/15522015-02-09T13:06:59ZMaximum likelihood estimate of b in the formula logN=a-bM and its confidence limits.Keiiti Aki