1993
DOI: 10.1190/1.1443374
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Advantages of using the vertical gradient of gravity for 3-D interpretation

Abstract: Gravity gradiometric data or gravity data transformed into vertical gradient can be efficiently processed in three dimensions for delineating density discontinuities. Model studies, performed with the combined use of maxima of analytic signal and of horizontal gradient and the Euler deconvolution techniques on the gravity field and its vertical gradient, demonstrate the superiority of the latter in locating density contrasts. Particularly in the case of interfering anomalies, where the use of gravity alone fai… Show more

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Cited by 141 publications
(72 citation statements)
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“…[18], [17] have shown that for the low index structural of from 0 to 1, there are the best estimates of depths and bundled solutions. Moreover, [23] noted that the appropriate choice of the window size depends on the wave length of the anomaly examined and the size of grid. Therefore, in this study the following parameters have permit reflect the morphological status of the study area:…”
Section: Depths From Euler Deconvolutionmentioning
confidence: 99%
“…[18], [17] have shown that for the low index structural of from 0 to 1, there are the best estimates of depths and bundled solutions. Moreover, [23] noted that the appropriate choice of the window size depends on the wave length of the anomaly examined and the size of grid. Therefore, in this study the following parameters have permit reflect the morphological status of the study area:…”
Section: Depths From Euler Deconvolutionmentioning
confidence: 99%
“…To reduce the effect of delocalization of the maxima, we made use of the vertical gradient maps which are quite able to minimize the effect of the interference of the gravity signatures of the sources [6,20,21].…”
Section: Methodsmentioning
confidence: 99%
“…The next stage was devoted to comparing an observed field to analytic models-first, by means of charts (e.g., Chastenet de Gery and Naudy, 1957), and later by a variety of different techniques that use synthetic model fitting, such as Werner deconvolution (e.g., Werner, 1953;Hartman et al, 1971), Euler deconvolution (e.g., Thompson, 1982;Reid et al, 1990), and analytic signal (e.g., Nabighian, 1972;Roest et al, 1992). Extensive applications and improvements of these methods have been published, such as the use of vertical gradients (e.g., Marson and Klingele, 1993;Debeglia and Corpel, 1997) and considering the effect of noise in data (Keating, 1998). These techniques have almost always been used assuming a chosen geometry of the source.…”
Section: Introductionmentioning
confidence: 99%