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At some scales space is not at all flat: the power of Albert Einstein's theory of general relativity lies in its interpretation of gravity in terms of curved space.
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Linear anomalies are important in the interpretation of gravity and magnetic data because they indicate some important structural features.
Linear anomalies, as an indicator of the structural features of some geological bodies, are very important for the interpretation of gravity and magnetic data.
Geophysicists usually deal with lineaments as an indicator of a fault, boundary or any tectonic feature beneath the surface for the interpretation of gravity and magnetic anomaly maps.
Beiki and Pedersen (2011) developed a data window constrained two-dimensional (2D) inversion technique for the interpretation of gravity gradient tensor data using dike/sheet and contact models.
London observes that the principle of equivalence of inertial and gravitational mass, which prompted Einstein to provide a geometrical interpretation of gravity, was at least a physical fact underlying gravitational theory.
In terms of the estimation power and the computation time, the method can be considered as an effective and powerful tool in the interpretation of gravity and magnetic data.
Although quantitative modeling and inversion processes are quite recent and up-to-date techniques, qualitative geological interpretation of gravity and magnetic data are also carried out by visual inspection and image enhancement algorithms.
These examples demonstrate that the proposed method can be used in the visual interpretation of gravity-anomaly maps in order to detect geological features such as major and minor faults.
The experimental results demonstrate that this operator can be used in the visual interpretation of gravity-anomaly maps in order to detect geological features such as major and minor faults.
These findings confirm earlier interpretations of gravity in terms of basement lithology (Rotstein et al. [2006]).
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