The Cenomanian of the Arabian Peninsula comprises a carbonate platform setting with rudists, characterized by gradual lateral facies changes including the interfingering of carbonate reservoirs (Natih and Mishrif formations) and source rocks. In order to be more predictive with regard to the distribution and the geometrical aspects of the reservoirs and source rocks, a high resolution sequence stratigraphic study has been carried out in the Adam Foothills of Northern Oman. Based on detailed field sections a correlation scheme covering a transect of 100 kilometers (km) has been established. Three orders of stacked depositional sequences have been found based on the reoccurrence of facies. During long-term increase of accommodation the depositional environment was separated in basinal and platform facies. In contrast, during longer term sea level fall, i.e. long-term decrease of accommodation space, prograding shelfal units extended platform facies over a large part of the basin. The most heterogeneous facies associations are found in times of minimal accommodation space, when incisions and subaerial exposure produce lateral variable strata (e.g. top Natih E). The organic matter is found at the base of two of the three longer term (3rd order) depositional sequences. The organic carbon is contained in marl-limestone couplets (small-scale cyclicity) with a high abundance of oysters and monospecific brachiopod faunas (coquinas). Rudists are found in the progradational part of these sequences, and occur mostly as reworked rudstone layers in meter to decimeter scale, high frequency cycles. The detailed regional correlation depends on the identification of medium- to small-scale (4th to 5th order) depositional sequences which are bounded by regional shifts of the facies belts. The distinct hierarchical organization of the depositional sequences in the Cenomanian, and the relative stability at that time of the Arabian Peninsula, implies a strong correlation potential and thus a broad regional similarity of the architecture of the petroleum systems at that time.

This paper presents a novel motion parameter estimation (ME) algorithm based on the spatio-temporal continuous wavelet transform (CWT). The multidimensional nature of the CWT allows for the definition of a multitude of energy densities by integrating over a subset of the CWT parameter space. Three energy densities are used to estimate motion parameters by sequentially optimizing a state vector composed of velocity, position, and size parameters. This optimization is performed on a frame-by-frame basis allowing the algorithm to track moving objects. The ME algorithm is designed to address real world challenges encountered in the defense industry and traffic monitoring scenarios, such as attaining robust performance in noise and handling obscuration and crossing object trajectories.

This paper addresses the problem of detecting and tracking moving objects in digital image sequences. The main goal is to detect and select mobile objects in a scene, construct the trajectories, and eventually reconstruct the target objects or their signatures. It is assumed that the image sequences are acquired from imaging sensors. The method is based on spatio-temporal continuous wavelet transforms, discretized for digital signal analysis. It turns out that the wavelet transform can be used eciently in a Kalman ltering framework to perform detection and tracking. Several families of wavelets are considered for motion analysis according to the specic spatio-temporal transformation. Their construction is based on mechanical parameters describing uniform motion, translation, rotation, acceleration, and deformation. The main idea is that each kind of motion generates a specic signal transformation, which is analyzed by a suitable family of continuous wavelets. The analysis is therefore associated with a set of operators that describe the signal transformations at hand. These operators are then associated with a set of selectivity criteria. This leads to a set of lters that are tuned to the moving objects of interest.

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