Application of curvatures to airborne gravity gradient data in oil exploration

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. G81-G88 ◽  
Author(s):  
Carlos Cevallos ◽  
Peter Kovac ◽  
Sharon J. Lowe
Keyword(s):  
Equipotential Surface ◽  
Shape Index ◽  
Airborne Gravity ◽  
Gravity Gradient ◽  
Gravity Gradiometer ◽  
Model Data ◽  
Canning Basin ◽  
Geologic Interpretation ◽  
The Mean ◽  
Differential Curvature

We apply equipotential surface curvatures to airborne gravity gradient data. The mean and differential curvature of the equipotential surface, the curvature of the gravity field line, the zero contour of the Gaussian curvature, and the shape index improve the understanding and geologic interpretation of gravity gradient data. Their use is illustrated in model data and applied to FALCON airborne gravity gradiometer data from the Canning Basin, Australia.

Interpreting the direction of the gravity gradient tensor eigenvectors: Their relation to curvature parameters of the gravity field

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. G49-G57 ◽  
Author(s):  
Carlos Cevallos
Keyword(s):  
Equipotential Surface ◽  
Vertical Axis ◽  
Tidal Force ◽  
Airborne Gravity ◽  
Gravity Gradient ◽  
Gravity Gradiometer ◽  
Model Data ◽  
Gradient Tensor ◽  
Gravity Gradient Tensor ◽  
Canning Basin

Rotating the gravity gradient tensor about a vertical axis by an appropriate angle allows one to express its components as functions of the curvatures of the equipotential surface. The description permits the identification of the gravity gradient tensor as the Newtonian tidal tensor and part of the tidal potential. The identification improves the understanding and interpretation of gravity gradient data. With the use of the plunge of the eigenvector associated with the largest eigenvalue or plunge of the main tidal force, it is possible to estimate the location and depth of buried gravity sources; this is developed in model data and applied to FALCON airborne gravity gradiometer data from the Canning Basin, Australia.

Mathematical properties and physical meaning of the gravity gradient tensor eigenvalues

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. G115-G124
Author(s):  
Carlos Cevallos
Keyword(s):  
Physical Meaning ◽  
Airborne Gravity ◽  
Gravity Gradient ◽  
Model Data ◽  
Gradient Tensor ◽  
Gravity Gradient Tensor ◽  
Mathematical Properties ◽  
Vertical Gravity Gradient ◽  
Vertical Gravity ◽  
Two Parameters

The eigenvalues of the gravity gradient tensor can be expressed as functions of two parameters: a magnitude and a phase. The decomposition gives physical meaning to the eigenvalues: The magnitude measures the amount of curvature, and the phase is related to the type of source. Their understanding allows one to propose new quantities to interpret. As an example, a modified phase eigenvalue offers the interpreter a subtle enhanced version of the vertical gravity gradient, which is evaluated with model data and applied to FALCON airborne gravity gradiometer data from the Perth Basin, Australia.

scholarly journals Interpreting 2D seismic with the assistance of FALCON® Airborne Gravity Gradiometer data in the Canning Basin

2015 ◽  
Vol 2015 (1) ◽  
pp. 1-4
Author(s):  
Jurriaan Feijth ◽  
Carlos Cevallos ◽  
Tony Rudge ◽  
Peter Edwards
Keyword(s):  
Airborne Gravity ◽  
Gravity Gradiometer ◽  
Canning Basin

scholarly journals Projected increases in surface melt and ice loss for the Northern and Southern Patagonian Icefields

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Claudio Bravo ◽  
Deniz Bozkurt ◽  
Andrew N. Ross ◽  
Duncan J. Quincey
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Historical Period ◽  
Emission Scenarios ◽  
Recent Past ◽  
Model Data ◽  
Surface Mass ◽  
The Mean ◽  
Surface Melt ◽  
Frontal Ablation

AbstractThe Northern Patagonian Icefield (NPI) and the Southern Patagonian Icefield (SPI) have increased their ice mass loss in recent decades. In view of the impacts of glacier shrinkage in Patagonia, an assessment of the potential future surface mass balance (SMB) of the icefields is critical. We seek to provide this assessment by modelling the SMB between 1976 and 2050 for both icefields, using regional climate model data (RegCM4.6) and a range of emission scenarios. For the NPI, reductions between 1.5 m w.e. (RCP2.6) and 1.9 m w.e. (RCP8.5) were estimated in the mean SMB during the period 2005–2050 compared to the historical period (1976–2005). For the SPI, the estimated reductions were between 1.1 m w.e. (RCP2.6) and 1.5 m w.e. (RCP8.5). Recently frontal ablation estimates suggest that mean SMB in the SPI is positively biased by 1.5 m w.e., probably due to accumulation overestimation. If it is assumed that frontal ablation rates of the recent past will continue, ice loss and sea-level rise contribution will increase. The trend towards lower SMB is mostly explained by an increase in surface melt. Positive ice loss feedbacks linked to increasing in meltwater availability are expected for calving glaciers.

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