8 research outputs found

    Non-stationarity and internal correlations of the occurrence process of mining-induced seismic events

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    A point process, e.g., the seismic process, is potentially predictable when it is non-stationary, internally correlated or both. In this paper, an analysis of the occurrence process of mining-induced seismic events from Rudna copper mine in Poland is presented. Stationarity and internal correlation are investigated in complete seismic time series and segmentally in subseries demonstrating relatively stable seismicity rates. It is shown that the complete seismic series are non-stationary; however, most of their shorter subseries become stationary. In the stationary subseries, the distribution of interevent time is closer to the exponential distribution, which is characteristic for the Poisson process. However, in most of these subseries, the differences between the interevent time and Poisson distributions are still significant, revealing correlations among seismic events

    Comparison of classical and Theil-Kendall methods in assessing the significance of linear trend of precipitation in south-eastern Poland

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    Two methods of linear trend estimation: the ordinary least squares (OLS, parametric) and Theil-Kendall (TK, nonparametric) are compared in the paper. The comparison was made using 65 time series of annual totals, Pa , and annual daily maximum, Pmax, of precipitation, 30-year long each, recorded in the south-eastern part of Poland (the Upper Vistula catchment). The OLS and TK slope coefficients of trends revealed high similarity for both Pa and Pmax series. The signs of slopes are the same for 64 sites for Pa and 63 sites for Pmax with positive signs prevailing: the numbers of decreasing trends for Pa OLS and TK slopes were 3 and 4, respectively, and, for Pmax, 13 for both OLS and TK slopes. In trend significance testing, both methods produced similar results for Pa time series: out of 16 significant trends, 13 were determined with both OLS and TK at the same sites. For Pmax series such agreement was found for 4 trends out of 10. Spatial distribution of significant trends showed a kind of clustering in certain parts of the investigated area
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