Magnetic anomaly maps reflect the spatial distribution of magnetic sources, which may be located at different depths and have significantly different physical and geometrical properties, complicating the identification of the corresponding geologic structures. Filtering techniques are frequently used to balance anomalies from shallow and deep sources, and to enhance certain features of interest, such as the edges of the causative bodies. Most methods used for enhancing magnetic data are based on vertical or horizontal derivatives of the magnetic anomalies or combinations of them, and the edges or centers of the sources are identified by maxima, minima, or null values in the transformed data. Normalized derivatives methods are used to equalize signals from sources buried at different depths. We present an edge detector method for the enhancement of magnetic anomalies, which is based on the tilt angle of the total horizontal gradient. The notable features of this method are that it produces amplitude maxima over the source edges and that it equalizes signals from shallow and deep sources. The method is applied to synthetic and real data. The effectiveness of the method is evaluated by comparing it with other edge detection methods that have been previously reported in the literature and that make use of derivatives. The results show that our method is less sensitive to variations in the depth of the sources and that it indicates the position of the edges of causative bodies in a more accurate fashion, when compared with previous methods, even for anomalies due to multiple interfering sources. These results demonstrate that the proposed method is a useful tool for the qualitative interpretation of magnetic data.

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We have constructed a Heisenberg-type algebra generated by the Hamiltonian, the step operators and an auxiliar operator. This algebra describes quantum systems having eigenvalues of the Hamiltonian depending on the eigenvalues of the two previous levels. This happens, for example, for systems having the energy spectrum given by Fibonacci sequence. Moreover, the algebraic structure depends on two functions f (x) and g(x). When these two functions are linear we classify, analysing the stability of the fixed points of the functions, the possible representations for this algebra.

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In this article we study the dependence degree of the traded volume of the Dow Jones 30 constituent equities by using a nonextensive generalised form of the Kullback-Leibler information measure. Our results show a slow decay of the dependence degree as a function of the lag. This feature is compatible with the existence of non-linearities in this type time series. In addition, we introduce a dynamical mechanism whose associated stationary probability density function (PDF) presents a good agreement with the empirical results.