Processing gravity gradient data

Abstract

As the demand for high-resolution gravity gradient data increases and surveys are undertaken over larger areas, new challenges for data processing have emerged. In the case of full-tensor gradiometry, the processor is faced with multiple derivative measurements of the gravity field with useful signal content down to a few hundred meters' wavelength. Ideally, all measurement data should be processed together in a joint scheme to exploit the fact that all components derive from a common source. We have investigated two methods used in commercial practice to process airborne full-tensor gravity gradient data; the methods result in enhanced, noise-reduced estimates of the tensor. The first is based around Fourier operators that perform integration and differentiation in the spatial frequency domain. By transforming the tensor measurements to a common component, the data can be combined in a way that reduces noise. The second method is based on the equivalentsource technique, where all measurements are inverted into a single density distribution. This technique incorporates a model that accommodates low-order drift in the measurements, thereby making the inversion less susceptible to correlated time-domain noise. A leveling stage is therefore not required in processing. In our work, using data generated from a geologic model along with noise and survey patterns taken from a real survey, we have analyzed the difference between the processed data and the known signal to show that, when considering the G zz component, the modified equivalent-source processing method can reduce the noise level by a factor of 2.4. The technique has proven useful for processing data from airborne gradiometer surveys over mountainous terrain where the flight lines tend to be flown at vastly differing heights.

Full Tensor Gradient of Simulated Gravity Data for Prospect Scale Delineation

“…vertical component (g z ) only. In addition, it has interpolative power, which can be exploited to adapt the survey design as the exploration program progresses and also to increase the effective resolution of the data [15,16]. Furthermore, the modeling of gravity data involving their gradients for quantitative interpretation will result in more sharp lateral boundaries.…”

On the approximation of the potential fields when using right rectangular prisms

“…We conclude that, at least for gravity and FTG modelling, we cannot achieve a good approximation of the subsurface by simply downsampling an existing and more precise model. The reason is that the result of the downsampling is not an equivalent source of the original model, and this fact, itself, suggests the solution to the issue, i.e., apply the equivalent source method (Dampney ; Barnes and Lumley ). From here on, we would like to clarify that we are using the equivalent source technique in a broader sense with respect to what was proposed in Dampney () or Barnes and Lumley ().…”

Evaluating the utility of gravity gradient tensor components

“…Of these two sources of variable noise levels, the latter is more important because most pre-processing algorithms combine the original component measurements into a single quantity from which the final (processed) component values are then computed. Since the tensor components provide independent but related measurements of the gravity field, it is appropriate that they are combined by calculating the underlying field (or potential) or equivalent density distribution that models the field (Li, 2001;Barnes and Lumley, 2011). As a result, the two main pre-processing methods currently in use are the Fourier transform approach (to compute the field or potential) and the equivalent source method (to compute a density distribution).…”