Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/5018
AuthorsCaratori Tontini, F.* 
Cocchi, L.* 
Carmisciano, C.* 
TitleRapid 3-D forward model of potential fields with application to the Palinuro Seamount magnetic anomaly (southern Tyrrhenian Sea, Italy)
Issue Date13-Feb-2009
Series/Report no./ 114 (2009)
DOI10.1029/2008JB005907
URIhttp://hdl.handle.net/2122/5018
Keywordspotential field modeling
Fourier transform
Palinuro Seamount
Subject Classification04. Solid Earth::04.02. Exploration geophysics::04.02.02. Gravity methods 
04. Solid Earth::04.02. Exploration geophysics::04.02.04. Magnetic and electrical methods 
04. Solid Earth::04.03. Geodesy::04.03.04. Gravity anomalies 
04. Solid Earth::04.05. Geomagnetism::04.05.04. Magnetic anomalies 
05. General::05.01. Computational geophysics::05.01.05. Algorithms and implementation 
AbstractWe show a set of forward model equations in the Fourier domain for calculating the 3-D gravity and magnetic anomalies of a given 3-D distribution of density or magnetization. One property of the potential field equations is that they are given by convolution products, providing a very simple analytic expression in the Fourier domain. Under this assumption, the domain of the density or magnetization parameters is connected by a biunivoc relationship with the data space, and potential field anomalies can be seen as filtered versions of the corresponding density or magnetization distributions. A very fine spatial discretization can be obtained by using a large number of points within a unique 3-D grid, where both the source distributions and field data are defined. The main advantage of this formulation is that it dramatically reduces execution times, providing a very fast forward model tool useful for modeling anomalies at different altitudes. We use this method to evaluate an average magnetization of 8 A/m for the Palinuro Seamount in the Tyrrhenian Sea (southern Italy), thus performing a joint interpretation of morphological and newly acquired magnetic data.
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