Gradient modeling with gravity and DEM
This study deals with the methods of forward gravity gradient modeling based on
gravity data and densely sampled digital elevation data and possibly other data, such as
crust density data. In this study, we develop an improved modeling of the gravity gradient
tensors and study the comprehensive process to determine gravity gradients and their
errors from real data and various models (Stokes’ integral, radial-basis spline and LSC).
Usually, the gravity gradients are modeled using digital elevation model data under
simple density assumptions. Finite element method, FFT and polyhedral methods are
analyzed in the determination of DEM-derived gravitational gradients. Here, we develop
a method to model gradients from a combination of gravity anomaly and DEM data.
Through a solution on the boundary value problem of the potential field, the gravity
anomaly data are combined consistently with the forward model of DEM to yield nine
components of the gravity gradient tensor. As a result, forward gravity gradients can be
synthesized using both geodetic and geophysical data. We use two different methods to
process gravity data. One is the regular griding method using kriging and least squares
collocation, and the other one is based on fitting splines or wavelet functions. For DEM
data, we use finite elements, polyhedra and wavelets or splines to compute the gradients.
The second Helmert condensation principle and the remove-restore technique are used to
connect DEM and gravity data in the determination of gravity gradients.
Modeling of the gradients thus, particularly at some altitude above ground, from
surface gravity anomalies is based on numerical implementations of solutions to
boundary-value problems in potential theory, such as Stokes’ integral, least-squares
collocation, and some Fourier transform methods, or even with radial-basis splines.
Modeling of this type would offer a complementary if not alternative type of support in
the validation of airborne gradiometry systems. We compare these various modeling
techniques using FTG (full tensor gradient) data by Bell Geospace and modeled gradients,
thus demonstrating techniques and principles, as well limitations and advantages in each.
The Stokes’ integral and the least-squares collocation methods are more accurate (about 3
E at altitude of 1200 m) than radial-basis splines in the determination of gravity gradient
using synthetic data. Furthermore, the comparison between the modeled data and real
data verifies that the high resolution (higher than 1 arcmin) gravity data is necessary to
validate the gradiometry survey data.
Ground and airborne gradiometer systems can be validated by analyzing the spectral
properties of modeled gradients. Also, such modeling allows the development of survey
parameters for such instrumentation and can lead to refined high frequency power
spectral density models in various applications by applying the appropriate filter.
School:The Ohio State University
School Location:USA - Ohio
Source Type:Master's Thesis
Keywords:remote sensing altitudes gravity
Date of Publication: