The common-reflection-surface stack provides a zerooffset simulation from seismic multicoverage reflection data. Whereas conventional reflection imaging methods (e.g. the NMO/dip moveout/stack or prestack migration) require a sufficiently accurate macrovelocity model to yield appropriate results, the common-reflectionsurface (CRS) stack does not depend on a macrovelocity model.We apply the CRS stack to a 2-D synthetic seismic multicoverage dataset. We show that it not only provides a high-quality simulated zero-offset section but also three important kinematic wavefield attribute sections, which can be used to derive the 2-D macrovelocity model. We compare the multicoverage-data-derived attributes with the model-derived attributes computed by forward modeling. We thus confirm the validity of the theory and of the data-derived attributes.For 2-D acquisition, the CRS stack leads to a stacking surface depending on three search parameters. The optimum stacking surface needs to be determined for each point of the simulated zero-offset section. For a given primary reflection, these are the emergence angle α of the zero-offset ray, as well as two radii of wavefront curvatures R N and R NIP . They all are associated with two hypothetical waves: the so-called normal wave and the normal-incidence-point wave. We also address the problem of determining an optimal parameter triplet (α, R NIP , R N ) in order to construct the sample value (i.e., the CRS stack value) for each point in the desired simulated zero-offset section. This optimal triplet is expected to determine for each point the best stacking surface that can be fitted to the multicoverage primary reflection events.To make the CRS stack attractive in terms of computational costs, a suitable strategy is described to determine the optimal parameter triplets for all points of the simulated zero-offset section. For the implementation of the CRS stack, we make use of the hyperbolic second-order Taylor expansion of the stacking surface. This representation is not only suitable to handle irregular multicoverage acquisition geometries but also enables us to introduce simple and efficient search strategies for the parameter triple. In specific subsets of the multicoverage data (e.g., in the common-midpoint gathers or the zero-offset section), the chosen representation only depends on one or two independent parameters, respectively.

The simulation of a zero-offset stack section from multicoverage seismic reflection data for 2-D media is a widely used seismic reflection imaging method that reduces the amount of data and enhances the signal-to-noise ratio. The aim of the common-reflection-surface stack is not only to provide a well-simulated zero-offset stack section but also to determine certain attributes of hypothetical wavefronts at the surface useful for a subsequent inversion. The main advantage of the common-reflection-surface stack is the use of analytical formulae that describe the kinematic reflection moveout response for inhomogeneous media with curved interfaces. These moveout formulae are valid for arbitrary shotreceiver pairs with respect to a common reference point and do not depend on the macro velocity model. An analytic reflection response that fits best to an actual reflection event in the multicoverage data set is determined by coherency analysis. We applied the common-reflection-surface stack to various synthetic and real data sets. For synthetic data sets, i. e. for a given model, data-derived as well as model-derived (forward calculated) wavefront attributes were computed. This enables us to verify the wavefront attributes determined by the commonreflection-surface stack exposing a wide agreement with the expected results. For real data sets we compare conventional stacking results and the common-reflection-surface stack.

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A global positioning system (GPS)-based online control and alarm system (GOCA) for monitoring of threedimensional movements has been developed at the Karlsruhe University of Technology. The GOCA hardware consists of an array of GPS sensors and communication units to be placed in the monitoring area. The hardwaredependent control software communicates with the GPS sensors and provides the GPS baseline data and covariance information to the GOCA deformation analysis software. The GOCA center, which comprises both the control software and the GOCA software, may be linked-for example, over a long distance-to another personal computer (PC) that serves as a remote control station. GOCA is able to provide the full capabilities of classical deformation analysis online (with stations grouped into stable points and moving object points). Both types of points may be occupied either continuously or over short periods at different times. The object points are determined with respect to the stable points. A network adjustment is performed for each interval of data collection, and the coordinate and covariance information may optionally be transformed into a specific reference system (e.g., the building system). Unstable reference points are to be detected by statistical tests. The estimated object point time series are filtered with respect to gross errors using robust estimation techniques. Online filters are used to smooth the time series data of critical displacements and to predict other deformation functions. The time series data, as well as prediction results, are displayed graphically for each object point. An example concerning the online