figure 1 Bouguer Anomaly
Gravity observations at the earth’s surface
includes anomalies that result from the density of subsurface structures. A small body will cause an anomaly with a
short wavelength while a large body will cause an anomaly with a longer
wavelength. Similarly deeper structures
will generate longer wavelength anomalies than shallower structures. In order to isolate these changes, a
wavelength filter and spectral analysis were designed around the specified areas
of interest. These tools assisted in
highlighting the geological structures to be analyzed.
Filtering is a
way of separating signals of different wavelengths to isolate and hence enhance
anomalous features within a given bandwidth.
A good rule-of-thumbs is that the wavelength of an anomaly divided by
three or four is approximately equal to the depth at which body producing the
anomaly is located. Using this filtering technique can it is possible to
enhance anomalies produced by features in a given depth range. This filtering technique is sometimes
referred to as Regional-Residual
Separation.
In order to extract
meaningful information from the gravity data we applied an optimum Wiener
filter using Geosoft® program (Geosoft Inc., 1994). The purpose of this filter is to reduce the
effect of anomalous source up to a certain depth leaving a smoother anomaly
associated with deeper sources. The
results of the Wiener filter which rejects source up to 250, 500, 750 and 1000
meters depth are presented in Figure 16, 17, 18 and 19 respectively.
figure 2 Regional Anomaly1
figure 3 Residual Anomaly 1
The quantity to
be determined in gravity exploration is local lateral variation in
density. Generally density is not
measured in situ, although it can be
measured by borehole logging tools or estimated from seismic velocities. More often density measurements are made in
the laboratory using small outcrop or drill-hole samples. Laboratory results, however, rarely give the
true bulk density because the samples may be weathered, fragmented, dehydrated,
or altered in the process of being collected.
Consequently, for field specific situations, density measurements are
seldom carried out.
A reasonably
satisfactory method of estimating near-surface density uses a gravity profile
over topography that is not co relatable with density variation (Nettleton,
1976).
Density values
are applied to gravity measurements while reducing these measurements to create
Bouguer Anomaly profiles. In addition
the effects of the variation of the terrain around the measurement site have to
be corrected for. The effect of the
terrain correction on the gravity data is a function of the density.
A set of profiles of Bouguer Anomaly values
using different density values can be generated. Typically the profile that is least affected
by the terrain is the one with the best density match.
figure 4 Nettleton Method
A more
quantitative method than the graphic method used during the Nettleton density
derivation, described above, is the Parasnis method. The Parasnis method uses a mathematical
least-square algorithm to determine the density value that best fits the test
data set.
Linear regression (least squares) method
- Assumes no correlation between
topography and subsurface density (i.e., anomalies are randomly
distributed with respect to elevation)
- Therefore correlation between
topography and gravity (g) will
be due to Bouguer slab
- Plot Free Air Anomaly (Dg fa) against elevation (h)
- Fit line
through points
slope will approximate 2pGr; solve for r (Bouguer density)
figure 5 Regional Anomaly2
figure 6 Residual Anomaly 2
As an additional processing, we also
tried a preliminary inversion of the gravity data to obtain an insight into 3-D
density distribution of the area. The
inversion technique is a modified version of 3-D magnetic inversion presented
by Yudistira and Grandis (2001) which follows a technique proposed by Fedi and
Rapolla (1999). A model-smoothing factor
was used in the resolution of the matrix inversion by truncated singular value
decomposition (SVD) method (Press et al., 1987).
The band-pass filtered gravity data (with
wavelength of 2 – 10 km) were re-sampled into a 500 ´ 500 meters grid spacing. The
subsurface is represented by a grid of rectangular prism with 500 ´ 500 ´ 100 meter in dimension which covers
the whole survey area up to 3000 meters depth.
This under-sampling of the data and model was necessary to reduce the
computation time of the 3-D inversion.
Therefore, we prefer looking for rather large scale feature by inverting
the gravity data filtered for 2 – 10 km wavelength.
The inversion was performed without any
“a priori” constrain and the obtained model of density distribution (or
contrast density relative to 2.0 gram/cm3) at three depth slices
(500, 1000 and 2500 meters) is presented in Figure 23. These figures of the model are given without
scale or coordinate for simplicity, but they cover the whole survey area as
other figures or maps.
The models from 500,
1000 and 2500 meters depth slices represent superficial, intermediate and deep
density distribution respectively. We
can observe that the model is merely another type of representation of the
gravity data, i.e. 1000 meters depth slice is nearly identical to 1 – 5 km band-pass
filtered gravity data while 2500 meters depth slice reproduces 2 – 10 km
band-pass filtered gravity data.
Inversions of other filtered gravity data gave similar results, i.e. the
model tends to mimic the data such that they are related each other by a simple
transformation. This fact is mainly due
to the lack of constrain in the inversion procedure such that the inversion
only reproduces the data with no or little additional information.
figure 7 Modeling line seismic for gravity modeling support
figure 8 Initial model for 3D Model
figure 9 3D Inversion result, depth structure
figure 10 3D inversion result
figure 11 surface data with subsurface data(geology model)(1)
figure 12 surface data with subsurface data(geology model)(2)
