Rock block forms of the Szczeliniec Wielki (919 m a.s.l.) in the border area of the Stolowe Mountains massif originated due to various exogenous and endogenous pro cesses. The processes had started in the Upper Cretaceous, culminated in Late Tertiary, and continue till the present day with much lower intensity. Such processes were indica ted by historical earthquakes and different tectonic events in the Sudeten Mountains and adjacent areas. Results of geodetic geodynamic studies are presented. Several sectors of the Sudeten Mountains which include the Table Hills - Stolowe Mountains, show horizontal and vertical movements. Results of periodic precise levelling in three geodetic micro-networks established on the Szczeliniec Wielki: "Przy Schronisku", "Piekielko" and "Tarasy Poludniowe / Schody" are presented. Investigations have been augmented wit h TM-71 crack gauging in rock blocks. These studies started in 1972 increasing gradually effectiveness of monitoring. Levelling changes, as well as displaceme nts resulting from 3D monthly records of three TM-71 crack gauges have been confronted with recent investigations into tectonic micro-deformations along the Sudeten Fault in the Bohemian Massif. It is suggested that aseismic geotectonic processes participated in the deformations found in investigated networks., Stefan Cacoń, Blahoslav Košťák and Krzysztof Mąkolski., and Obsahuje bibliografické odkazy
The deformation measurements are performed for the purpose of obtaining information concerning ground movement and objects on the ground within given time intervals. For the purpose of improving conventional models of deformation analysis (CDA) it is desirable to use several different methods and also implement alternative proce dures as a further improvement, such as the concept of robust geodetic networks and strain analysis, aimed at obtaining objective information about the movements. In the present paper, in addition to the CDA methods, we also analyze the robust methods in deformation detecting and the method of the strain analysis based on elasticity theory as a supplement to the conventional geometric deformation methods (CDA). The mentioned methods are applied and analysed for the case of a test example of Fruška Gora in Serbia, for which there exist geological and geophysical studies of recent tectonic movements. The measuring results for two measuring epochs concern the GNSS vectors measured by applying the fast static method within closed polygons over a ten-year interval, where only the horizontal movement component is analysed. The efficiency of the applied CDA and robust methods is measured by applying a mean success rate (MSR) by applying Monte Carlo simulations in order to investigate the efficiency of a given methods for a given control network., Zoran Sušić, Mehmed Batilović, Toša Ninkov, Vladimir Bulatović, Ivan Aleksić and Gojko Nikolić., and Obsahuje bibliografické odkazy
Different approaches have been proposed to determine the possible outliers existing in a dataset. The most widely used consists in the application of the data snooping test over the least squares adjustment results. This strategy is very likely to succeed for the case of zero or one outliers but, contrary to what is often assumed, the same is not valid for the multiple outlier case, even in its iterative application scheme. Robust estimation, computed by iteratively reweighted least squares or a global optimization method, is other alternative approach which often produces good results in the presence of outliers, as is the case of exhaustive search methods that explore elimination of every possible set of observations. General statements, having universal validity, about the best way to compute a geodetic network with multiple outliers are impossible to be given due to the many different factors involved (type of network, number and size of possible errors, available computational force, etc.). However, we see in this paper that some conclusions can be drawn for the case of a leveling network, which has a certain geometrical simplicity compared with planimetric or three-dimensional networks though a usually high number of unknowns and relatively low redundancy. Among other results, we experience the occasional failure in the iterative application of the data snooping test, the relatively successful results obtained by both methods computing the robust estimator, which perform equivalently in this case, and the successful application of the exhaustive search method, for different cases that become increasingly intractable as the number of outliers approaches half the number of degrees of freedom of the network.