The three-dimensional inversion of magnetic and gravity gradient tensor data

2003 ◽  
Vol 2003 (2) ◽  
pp. 1-4
Author(s):  
Philip J. Heath
Keyword(s):  
Three Dimensional ◽  
Gravity Gradient ◽  
Gradient Tensor ◽  
Gravity Gradient Tensor ◽  
Tensor Data

Noise filtering of full-gravity gradient tensor data

2013 ◽  
Vol 10 (3) ◽  
pp. 241-250 ◽  
Author(s):  
Yuan Yuan ◽  
Da-Nian Huang ◽  
Qing-Lu Yu ◽  
Mei-Xia Geng
Keyword(s):  
Gravity Gradient ◽  
Noise Filtering ◽  
Gradient Tensor ◽  
Gravity Gradient Tensor ◽  
Tensor Data

scholarly journals Window constrained inversion of gravity gradient tensor data using dike and contact models

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. I59-I72 ◽  
Author(s):  
Majid Beiki ◽  
Laust B. Pedersen
Keyword(s):  
Source Parameters ◽  
Gravity Gradient ◽  
Model Parameters ◽  
Crustal Material ◽  
Gradient Tensor ◽  
Data Set ◽  
Central Dome ◽  
Gravity Gradient Tensor ◽  
Contact Models ◽  
Tensor Data

We have developed a constrained inversion technique for interpretation of gravity gradient tensor data. For dike and contact models striking in the [Formula: see text]-direction, the measured [Formula: see text] and [Formula: see text] components can be jointly inverted for estimating the model parameters horizontal position, depth to the top, thickness, dip angle, and density contrast. For a given measurement point, the strike direction of the gravity gradient tensor caused by a quasi 2D structure can be estimated from the eigenvector corresponding to the smallest eigenvalue. Then, the measured components can be transformed into the strike coordinate system. It is assumed that the maximum of [Formula: see text] is approximately located above the causative body. In the case of gridded data, all measurement points enclosed by a square window centered at the maximum of [Formula: see text] are used to estimate the source parameters. The number of data points used for estimating source parameters is increased by increasing the size of the window. Solutions with the smallest data-fit error were selected as the most reliable solutions from any set of solutions. The gravity gradient tensor data are deconvolved using both dike and contact models within a set of square windows. Then, the model with the smallest data-fit error is chosen as the best model. We studied the effect of random noise and interfering sources using synthetic examples. The method is applied to a gravity gradient tensor data set from the Vredefort impact structure in South Africa. In this particular case, the dike model provides solutions with smaller data-fit errors than the contact model. This supports the idea that in the central dome area there is a predominance of vertical structures related to the formation of the transient crater and subsequent central uplift of the lower and middle crustal material.

scholarly journals Inversion of gravity gradient tensor data: does it provide better resolution?

2016 ◽  
Vol 205 (1) ◽  
pp. 192-202 ◽  
Author(s):  
V. Paoletti ◽  
M. Fedi ◽  
F. Italiano ◽  
G. Florio ◽  
S. Ialongo
Keyword(s):  
Gravity Gradient ◽  
Gradient Tensor ◽  
Gravity Gradient Tensor ◽  
Tensor Data

Analytic signals of gravity gradient tensor and their application to estimate source location

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. I59-I74 ◽  
Author(s):  
Majid Beiki
Keyword(s):  
Analytic Signal ◽  
Three Dimensions ◽  
Gravity Gradient ◽  
Automatic Identification ◽  
Gradient Tensor ◽  
Gravity Gradient Tensor ◽  
Signal Functions ◽  
First Vertical Derivative ◽  
Tensor Data ◽  
Analytic Signals

The analytic signal concept can be applied to gravity gradient tensor data in three dimensions. Within the gravity gradient tensor, the horizontal and vertical derivatives of gravity vector components are Hilbert transform pairs. Three analytic signal functions then are introduced along [Formula: see text]-, [Formula: see text]-, and [Formula: see text]-directions. The amplitude of the first vertical derivative of the analytic signals in [Formula: see text]- and [Formula: see text]-directions enhances the edges of causative bodies. The directional analytic signals are homogenous and satisfy Euler’s homogeneity equation. The application of directional analytic signals to Euler deconvolution on generic models demonstrates their ability to locate causative bodies. One of the advantages of this method is that it allows the automatic identification of the structural index from solving three Euler equations derived from the gravity gradient tensor for a collection of data points in a window. The other advantage is a reduction of interference effects from neighboring sources by differentiation of the directional analytic signals in [Formula: see text]-, [Formula: see text]-, and [Formula: see text]-directions. Application of the method is demonstrated on gravity gradient tensor data in the Vredefort impact structure, South Africa.

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