715013233

American Journal of Earth Sciences 2014; 1(1): 25-32

Published online April 20, 2014(http://www.openscienceonline.com/journal/ajes)

An integrated analysis of Landsat OLI image and satellite gravity data for geological mapping in North Kordofan State, Sudan

Khalid A. Elsayed Zeinelabdein1,*

, Mohammed S. Elemam1, Hamdi A. Ali

2, Osman M. Alhassan

2

1Department of Geology, Faculty of Petroleum and Minerals,Al Neelain University, Khartoum, Sudan 2Department of Geology, Faculty of Science and Technology,Omdurman Islamic University, Omdurman, Sudan

Email address

[email protected](K. A. Elsayed Zeinelabdein)

To cite this article Khalid A. Elsayed Zeinelabdein, Mohammed S. Elemam, Hamdi A. Ali, Osman M. Alhassan. An Integrated Analysis of Landsat OLI

Image and Satellite Gravity Data for Geological Mapping in North Kordofan State, Sudan, American Journal of Earth Sciences.

Vol. 1, No. 1, 2014, pp. 25-32

Abstract

North Kordofan Region is characterized by poor rock exposure, which makes the traditional field mapping always a

problematic issue. The objective of the present study to test the viability of integrating Landsat 8 OLI image and satellite

gravity data with limited field work for regional geological mapping in poorly exposed areas. Remote sensing has

proven a valuable aid in geological mapping and exploring for mineral deposits. However, this technique has limitations,

especially in vegetated areas or regions characterized by poor rock exposure. The processing of Landsat 8 OLI image

utilizing various remote sensing techniques such as colour composite, PCA, band ratoing and PC spectral sharpening

improved the visual interpretation of the image set. The enhanced image provided persuasive spectral information

helpful for discriminating the various rock units. Bouguer anomaly map produced from the processed satellite gravity

data provided complementary information that assisted in the delineation of the boundary of different rock domains in

addition to the enhancement of the linear features which in most cases represent structural elements such as faults and

shear zones. The integration of the different datasets including the enhanced satellite images and gravity data with the

petrographic investigation of some selected rock samples in the GIS environment facilitated the production of the final

geological map of the study area, which is of accepted credibility and relatively took shorter time frame. Therefore, this

integrated approach should be adopted in mapping similar regions of the same characteristics.

Keywords

Landsat 8 OLI Images, Satellite Gravity, Geological Mapping, North Kordofan, Sudan

1. Introduction

Mineral exploration activities were dramatically

increased in the recent years in Sudan. This entails the

proper geological mapping and delineation of major

structures that control the presence of mineralization. North

Kordofan Region is characterized by poor rock exposure,

which makes the traditional field mapping always a

problematic issue. The tough environments coupled with

the poor exposure of rocks make the conventional

geological field survey very expensive, time consuming

and difficult to conduct. Additionally, the obtained results

to a great extent rely on interpolations. In this case, the

integration of remote sensing and satellite gravity data may

be qualified as an alternative tool for conducting regional

geological mapping with reasonable accuracy and relatively

short time frame. Therefore, the objective of the present

study to test the viability of integrating Landsat 8 OLI

image and satellite gravity data with limited field work for

regional geological mapping in a poorly exposed area.

Remote sensing techniques when used together with

field mapping and professional ground truthing, it makes

the geological mapping effective and efficient in all aspects

26 Khalid A. Elsayed Zeinelabdein et al.: An Integrated Analysis of Landsat OLI Image and Satellite Gravity

Data for Geological Mapping in North Kordofan State, Sudan

of cost [1]. Modern remote sensing systems record image

data in a digital raster format that is suitable for computer

processing using readily available software and personal

computers [2]. Remote sensing advances in recent years

have helped earth science researchers to identify and map

the distribution of target minerals on the Earth’s surface [3].

This study was carried out in North Kordofan State

within the area bounded by latitudes 14°31'30"N -

14°59'26"N and longitudes 29°09'00"E - 29°36'28"E(Fig.1).

This region is characterized by arid to semi-arid desert

climate and low laying gently undulating surface with few

scattered moderately elevated hills. The drainage system of

the area is irregular, having a dendritic drainage pattern

which seems to be structurally controlled by shear zone

andits related master and conjugate faults system.

Fig. 1. A map showing the location of the study area.

2. Geologic Setting

The late Proterozoic crust in the Arabian – Nubian Shield

was built by the growth and coalescence of several intra-

oceanic island arcs and Andean-type magmatic arcs to form

large continental masses [4]. The Precambrian basement of

Sudan can be divided into two major geodynamic systems,

namely gneisses with interfolded supra-crustal

metasediments and a dominantly low-grade juvenile

ophiolitic island-arc assemblage[5].

Continuation of Late Proterozoic structures with juvenile

basement from the Arabian-Nubian Shield into the foreland

region west of the Nile is well-documented [6].Two distinct

high-grade and low-grade basement sequences are exposed

in the foreland region of the Arabian Nubian Shield west of

the Nile. The high-grade sequence, which is probably

middle to lower Proterozoic in age, is overlain by low-

grade late Proterozoic Pan African sequences of

metasediments, metavolcanics and volcanoclastic rocks and

associated calc-alkaline intrusive rocks [7]. Relatively

intact ophiolite complexes have so far been reported from

the Jabel Rahib area [8]and from the Nuba Mountains[9].

Mesozoic(Carboniferous-Triassic) alkaline ring

complexes affected both of the high-grade and low-grade

sequences in the region [10]. The general structural trend of

the low-grade Pan-African sequences is NE-SW parallel to

other Pan-African structures in the main shield east of the

Nile,e.g. [11].

Umm Badir Shear zone (UBSZ) is a late Precambrian

Pan African brittle shear zone which affects all previous

formations. Kinematic indicators suggest dextral strike-slip

movement shearing structure related to the late

Precambrian. Sodari Shear Zone (SSZ) represents

reactivation of latter pan African orogeny, which is sinistral

brittle strike-slip structure trending 33°NE and extend for

about 200 km (144-240 Ma), it affects the very low-grade

units of Umm Badir belt [10].

3. Methodology

In order to fulfill the objective of the present study,

different materials and methods have been utilized:

3.1. Data Types

The following materials were made available for the

present study:

• Landsat 8 OLI image path 175 and row 50 acquired

on 24-05-2013 obtained from USGS.

• Satellite gravity grid data obtained from the

Satellite Geodesy at the Scripps Institution of

Oceanography, University of California San Diego.

• Rock samples for petrographic investigations.

3.2. Methods

The adopted methods comprised the digital enhancement

of satellite image utilizing different processing techniques

such as colour compositing, band ratioing, image sharpening

and principal component analysis (PCA). The gravity data

was originally provided as Free Air Anomaly (FAA)

delivered together with the elevation data for each point. This

combination of data was used to compute Bouguer Anomaly

(BA) for the study area. Furthermore, different derivatives

were computed from the Bouguer anomaly values. The

Bouguer anomaly and the derived values were interpreted in

terms of variations in lithological units, which were

constrained by the surface exposures where available.

Limited field work was carried out in order to collect

rock samples for petrographic investigations, take structural

measurements and establish the field relationships.

Polarized light microscopic investigations were conducted

for selected rock samples to identify the rock types and

give accurate names for the different lithologies.

The enhanced satellite images, the BA data and its

derivatives together with the results of the petrographic

investigations were all imported into the GIS environment,

wheredata integration and analysis was carried out to

facilitate the production of the final geological map of the

study area.

American Journal of Earth Sciences 2014; 1(1): 25-32 27

4. Results and Discussion

4.1. Digital Image Processing

In order to ease the subsequent digital images

processing,the Landsat 8 OLI bands were stacked and

resized to the following coordinate: long: 29°10ʹ00ʹʹ –

29°38ʹ30ʹʹ E and lat: 14°30ʹ00ʹʹ - 15°00ʹ00ʹʹ N.

Image fusion was then executed to sharpen the low spatial

resolution multispectral bands using the high spatial

resolution panchromatic band 8.

4.1.1. Colour Composite

For the purpose of image display only three bands or

band combinations, each directed to one of the primary

colour - guns (red, green, and blue) are required [12]. The

rule of colour composites is to set the most informative

band for a particular purpose in the red, the next to the

green and the least informative to the blue [13]. The

different RGB combinations helped in discriminating the

rock types, which is useful in the geological application.

Because Landsat 8 OLI images are delivered with

additional bands, the combinations used to create RGB

composites will be different than those of Landsat 7 ETM+

images(Fig. 2).

Fig. 2.Portion of the electromagnetic spectrum shows Landsat 7 ETM+ and Landsat 8 OLI bands locations.

Different colour composites were created during this

study. For instance, Figure (3) shows a false colour

composite of bands 7,5,2 in RGB, respectively. This colour

composite shows that the acid meta-volcanic rocks appear

in dark olive green colour with generally trend in NE- SW

direction. The weathering results of these rocks in the

composition of kaoline are clearly seen in white colour

restricted to the southwestern part of the image area.

Moreover, linear features such as structural lineaments,

faults and fold axes are not unclear on this image.

Fig3. OLI colour composite obtained using bands 7,5,2in RGB,

respectively.

Another colour composite image was prepared by using

the infrared bands of the image set, i.e. utilizing bands 7, 6,

and 5 in RGB respectively (Fig. 4). This image shows that

the acid meta-volcanic rocks appear in dark green colour,

while the weathering results of these rocks are clearly seen

in bluish white. As in the previous image, the linear

features are also clear on this image.

Fig4. OLI colour composite obtained using bands 7,6,5 in RGB,

respectively.

4.1.2. Principal Component Analysis (PCA)

Principal components analysis can be used either as an

enhancement technique to improve the visual interpretation

or as a tool for merging different data sets.ThePCs are

scene independent and totally uncorrelated [14]. Also,

relationships between different groups of pixels



representing different land cover types may become clearer

if they are viewed in the principal axis reference system

rather than in terms of the original spectral bands.

Generally, the PCA image enabled the differentiation

between major lithological units which have large spatial

extent.

Using the six reflected multispectral OLI bands of image,

the PCA was performed over the image of the study area.

Tables (1) show the covariance and eigen matrices used in

the transformation, respectively. It is clear from the Table

(1) that, the PC1 with 86.81% variance and positive

loadings from all OLI bands. It is well known that the PC1

contains significant topographic information that often

accounts for the high direct correlation between the input

bands.

Table 1.Statistical summary of Eigen vectors, eigen values and variance percentage of the OLI principal components.

Eigen vector Eigen value Var. % Acc. Sum

Band 2 Band 3 Band 4 Band 5 band 6 Band 7

PC 1 0.12 0.21 0.36 0.48 0.54 0.54 17408313.04 86.81 86.81

PC 2 0.55 0.52 0.27 0.25 -0.25 -0.46 1805816.588 9.01 95.82

PC 3 0.49 0.32 -0.34 -0.48 -0.11 0.54 569302.68 2.84 98.66

PC 4 0.23 0.02 -0.51 -0.01 0.72 -0.41 169926.43 0.85 99.51

PC 5 -0.01 -0.02 -0.63 0.68 -0.34 0.16 80548.47 0.40 99.91

PC 6 0.62 -0.76 0.14 0.08 -0.05 0.04 16808.81 0.08 99.99

The PC 1 and PC2 display more lithological contrast,

and the topographic expression is better and it is known

that they discriminate well between the VNIR and SWIR

bands. Both PC3 and PC4 although they have low variance

value but still display fair lithological contrast, whereas the

rest PC5 and PC6 with very low variance, are less

informative and show only noise. Colour composite image

of PC1, PC2 and PC3 in RGB (Fig. 5)has provided much

lithological information and discrimination between units.

Fig 5. The PCA colour composite image obtained using PC1, PC2 and

PC3 in RGB, respectively.

The differentiation between the acid meta-volcanics and

sheared granite rocks can be easily observed on this image,

since the former are displayed in pale green colour while

the latter are displayed in shining green, the post-orogenic

granite is displayed in beryl green colour and the kaolinite

is displayed in white colour.

4.1.3. Decorrelation Stretching

During this study the decorrelation stretching technique

was applied to the PC sharpened image to expand the

variability of the selected bands 7, 5, 3 in RGB,

respectively (Fig. 6). The resulted image improved the

range of intensities and saturations of colours. Moreover, it

well discriminates the different lithological units and

enhanced the appearance of the structural elements within

the investigated area.

Fig 6.Colour composite obtained through the decorrelation stretching of

the PC spectral sharpened bands 7, 5, 3 in RGB, respectively.

This image shows the acid meta-volcanic rocks in light

green colour, the sheared granite in cyan, the post-orogenic

granitein blue and the kaolinite is displayed in white

colour,the linear features are very clear.


4.1.4. Ratioing Technique

Spectral band radioing is a proven technique which

allows identification of geological materials based on the

reorganization of diagnostic absorption bands. It minimizes

the effect of topographic slope, aspect, and Albedo

differences between rocks, and enhances the subtle

differences in reflectivity between bands which are

diagnostic of various surface materials [15]. Ratio images

are prepared by dividing the DN value in one spectral band

by the corresponding DN value in another band for each

pixel [2].

Several ratio images were prepared during this study for

the purpose of geological mapping. OLI bands ratios: 6/7,

6/2 and (4/5*6/5) in R, G and B, respectively were

computed accoding to [16]. The resulted image (Fig.7)

shows that the acid meta-volcanic rocks appear in light

pink colour, the sheared granite in deep pink, while the

post-orogenic granite is displayed in deep violet and the

kaolinite appears with deep blue colour.

Fig7. Sultan's colour composite ratio image obtained using bands ratios

6/7,6/2 and (4/5*6/5) in RGB, respectively.

4.2. Gravity Investigations

4.2.1. Introduction

The present day is an attempt to integrate various data

sets (satellite, radar data, geologic and geomorphologic

field data) to obtain a general better understanding of the

tectonic setting [17]. The gravity method is a

nondestructive geophysical technique that measures

differences in the earth’s gravitational field at specific

locations. The success of the gravity method depends on

the different earth materials having different bulk densities

(Mass) that produce variations in the measured

gravitational field. The variations in the measured

gravitational field of the different earth materials, can be

interpreted by a variety of analytical and computer methods

to determine the depth, geometry and density that causes

the gravity field variations.

4.2.2. Satellite Gravity Data

The Radar altimeter measurements provided the

scientific community with valuable information about the

earth interior [18]. From this, the sea-surface topography

from radar altimeter data was used to calculate the vertical

component of the gravity field. This significantly helped in

improving the knowledge of the earth’s tectonic [19], [20].

Consequently, the gravity field measurements were moved

from only calculating the marine gravity field from radar

altimeter measurements to measuring the global gravity

field using new satellite missions. This step led to

improving the quality of the available data and also

improving our understanding of the overall earth tectonic

history,e.g. [21].

Satellite gravity is gravity field measurements that were

available recently in the last decade. Recent satellite

missions were launched like CHAMP in 2000, GRACE in

2002, and GOCE in 2009 to map the Earth’s gravity field

[21], [22]. The global coverage and the consistent data

quality are the most significant advantages of the satellite

gravity data [18].

4.2.3. Satellite Gravity Data Processing and

Interpretation

The study area is characterized by a very poor rock

exposure, where large parts are covered by superficial

deposits. These obstacles impair the interpretation of the

remotely-sensed data and make the delineation of contacts

between the various rock units more difficult. Therefore,

the variations in the Earth gravity field have been used to

draw the boundaries between the different rock units.

Bouguer Anomaly

Satellite gravity data in the form of Free Air Anomaly

(FAA) was processed usingthe elevation data to calculate

the Bouguer correction and hence the Bouguer Anomaly for

each point. The results of computations were imported into

the GIS environment for further manipulations which

finally resulted in the preparation of Bouguer anomaly map

of the study area (Fig. 8).

Fig 8. Bouguer gravity map of the study area, contour interval is 1mGal.



The obtained Bouguer anomaly map was visually

interpreted in term of boundaries between units having

different densities. The anomaly values were not directly

tied to certain lithologies, rather, the rock type information

was obtained from the field work coupled by petrographic

investigations, while the gravity data was used as

complementary information in delineating the boundary of

each rock type.

As it is clear from the map, many varieties of igneous

rocks both extrusive and intrusive show almost the same

gravity values due to similarities in mineral composition,

while some rocks show variation in the gravity values. Base

on the previous knowledge about the region, the high

gravity value rock were interpreted as basic meta-volcanics,

the medium as acid meta-volcanics and the low ones as

acidic intrusive rocks.

The only exception to this rule as can be noticed from

the low gravity value of the acid meta-volcanic rocks in the

center of the study area. This low value can be attributed to

the shearing and silicification of these rocks along linear

trends. Another advantage of the gravity map is that it

enabled the identification and delineation of linear features

which in most cases represent structural elements such as

shear zones that may control the mineralization and hence

improve the potentiality of the area.

Many faults were identified from the Bouguer map as

indicated by dense gradient belt of gravity anomaly. Some

of the identified faults are main faults but some of them are

just minor faults with limited effects. The main faults which

separate the gravity high area are trending N-S, while the

others are of N-E trend.

First and Second Vertical Derivatives

The Second Vertical Derivative technique was used as

two dimensional filters for interpretation of potential field

data [23]-[28].The second derivative accentuates shallow

anomalies and suppresses deep seated effects. Points of

inflections of the second derivatives, i.e. points where the

second derivative value changes its sign, are geologically

expressed as faults, since the gravity gradient undergoes its

most rapid changes from one level to another in the vicinity

of faulted areas [29].

If we use the symbol (g) to represent the gravity and (z)

is vertical downward axes, then the second derivative is the

quantity d2g/dz

2. The importance of the second derivative

for potential field interpretation arises from the fact that the

double differentiation with respect to depth tends to

emphasize the smaller, shallower geological anomalies at

the expense of larger, regional feature [24].

The intent of applying the derivative technique in this

study is to detect thesurface and near surface faults.As

shown in Figure (9), the derivative has a higher value at the

greatest curvature (crest or trough) and has a zero value

where there is no curvature i.e. at point of inflection; this

phenomenon could geologically represented by the

presence of a fault.

Fig9.The second vertical derivative gravity map of the study area, on

which the zero contour lines separate the positive from the negative

anomalies. The separated anomalies indicate different lithological units.

Fig10.The second vertical derivative gravity map of the study area. Note:

zero contours line separates the positive from the negative anomalies. The

separated anomalies indicate different lithological units.

4.3. GIS Data Analysis and Integration

By the accomplishment of the remote sensing OLI

imageanalysis;the different colour composite imageries, PC

spectrallysharpenedimage and sultan ratio image were

combined with the results of petrographic investigationsof

the rock samples in the GIS environment for further

analysis.The Bouguer anomaly map was, also, imported to

the GIS and utilized to delineate the boundary between the

different lithological units and to revealthe linear

structurespresent in the mapped area. The integration of all

these data sets facilitated the production of the final

geological map of the study area (Fig. 11).


Fig 11. Geological map of the study area obtained through the integrated analysis of Landsat OLI image and satellite gravity data.

5. Conclusions

Remote sensing has proven a valuable aid in geological

mapping and exploring for mineral deposits. However, this

technique has limitations, especially in vegetated areas or

regions characterized by poor rock exposure.

The processing of Landsat 8 OLI image utilizing various

remote sensing techniques such as colour composite,

principal component analysis transformation and PC

spectral sharpening techniques improved the visual

interpretation of the image set of the study area. The

enhanced Landsat 8 OLI Image provided persuasive

spectral information helpful for discriminating the various

rock units.

Bouguer anomaly map produced from the processed

satellite gravity data provided complementary information

that assisted in the delineation of the boundary between the

different rock domains in addition to the enhancement the

linear features which in most cases represent structural

elements such as faults and shear zones.

The integration of the different datasets including the

enhanced satellite images and gravity data with the

petrographic investigation of some selected rock samples in

the GIS environment facilitated the production of the final

geological map of the study area with reasonable accuracy

and relatively took short timeframe. Therefore, this

integrated approach should be adopted in mapping regions

characterized by poor rock exposure.

Acknowledgment

The authors wish to thank the United States Geological

Survey (USGS) for providing the Landsat 8 OLI

images.Thanks extended to the Satellite Geodesy atScripps

Institution of Oceanography, University of California San

Diego for availing the satellite gravity grid data.Thanks are,

also, due to the editor and anonymous reviewer for their

constructive comments.

References

[1] E. E. Mshiu. Landsat remote sensing data as an alternative approach for geological mapping in Tanzania: a case study in the Rungwe Volcanic Province, south-western Tanzania. Tanz. J. Sci. Vol. 37: p 26-36, 2011.

[2] F.F.Sabins.Remote Sensing, Principles and Interpretation, 3rd ed. Freeman & Co., New York, USA, 1997.

[3] B.Sadeghi, M.Khalajmasoumi, P.Afzal, P.Moarefvand, A.B.Yasrebi, A.Wetherelt, P. Foster, and A.Ziazarifi. Using ETM+ and ASTER sensors to identify iron occurrences in the Esfordi 1:100,000 mapping sheet of Central Iran. Journal of African Earth Sciences 85:103–114, 2013.



[4] W.R.Greenwood, D.G. Hadley, R.E. Anderson, R.J. Fleck and D. L. Schmidt. Late Proterozoic cratonization in southwestern Saudi Arabia. Phil. Trans. R. Soc. Lond. A, 280, 517-527, 1976.

[5] E.M. Abdelrahman.Geochemical and geotectonic controls of the metallugenetic evolution of selected ophiolite complexes from the Sudan. Berl. Geowiss. Abh. A 145, 175, 1993.

[6] H. Schandelmeier, A. Richter,U. Harms and E.M.Abdelrahman.Lithology and structure of the late Proterozoic Jebel Rahib Fold and Thrust belt (NW Sudan). Berliner Geowiss. Abh. A 122.1.pp. 15-30, 1990.

[7] Geological Research Authority of Sudan. Geological map of Sudan, scale 1: 2,000,000, 1982.

[8] E.M.Abdelrahman, U. Harms, H. Schandelmeier, G. Franz, D. P. F. Darbyshire, P. Horn and D. Müller-Sohnius. A new ophiolite occurrence in NW Sudan -constraints on Late Proterozoic tectonism. Terra Nova, 2,363-376, 1990.

[9] M. G. Abdelsalam and A. S. Dawoud. The Kabus ophiolitic mélange, Sudan, and its bearing on the western boundary of the Nubian Shield.Journal of the Geological Society, London, 148, 83-92, 1991.

[10] D. Müller-Sohniusand P.Horn. K-Ar dating of ring complexes and fault systems in Northern Kordofan, Sudan: evidence for independent magmatic and tectonic activity. Geol. Rundsch, 83: 604-613, 1994.

[11] D. Kuster, J.P. Liegeois, D. Matukovb, S. Sergeev, and F. Lucassenc. Zircon geochronology and Sr, Nd, Pb isotope geochemistry of granitoids from Bayuda Desert and Sabaloka (Sudan): Evidence for a Bayudian event (920–900 Ma) preceding the Pan-African orogenic cycle (860–590 Ma) at the eastern boundary of the Saharan Metacraton. Precambrian Research 164: 16–39, 2008.

[12] N. H. Kenea. Digital enhancement of Landsat data, spectral analysis and GIS data integration for geological studies of the Derudeb area, Southern Red Sea Hills, NE Sudan, Berl. Geowiss. Abh. D, Band14, 1997.

[13] S.A.Drury.Image interpretation in geology, 2nd ed. Chapman & Hall, London, UK, 1987.

[14] J.R.Jensen. Introductory digital image processing: a remote sensing perspective. 2nd ed. Prentice Hall Inc. New Jersey, USA, 1996.

[15] J.E.Estes. Manual of Remote Sensing, 2nd ed., volume II, Interpretation and Application.Pub. by American Society of Photogammetry, the Sheridan Press, USA, 1983.

[16] M. Sultan, R. E. Arvidson, R. C. Sturchio and E. A. Guiness.

Lithologic mapping in arid regions with Landsat Thematic Mapper data: Meeting dome, Egypt. Geol. Soc. America Bull., 99, 748-762, 1987.

[17] B.T. Willige. Seismic risk analysis in southwest-Germany based on satellite radar - data. International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7: 705-708, 1996.

[18] M.N. Nabigian, M.E. Andr, V.J.S. Grauch, R.O. Hansen, T.R. Lafehr, Y. Li, W.C. Oerson, J.W. Peirce, J.D. Phillips, and M.E. Runder. 75th Anniversary - Historical development of the gravity method in exploration. Geophysics, 70, 63ND-89ND, 2005.

[19] C. Hwang, E-C. Kao and B. Parsons. Global derivation of marine gravity anomalies from Seasat, Geosat, ERS-1 and TOPEX/POSEIDON altimeter data. Geophysical Journal international, 134: 449-459, 1998.

[20] D.T. Sandwell and W.H.F. Smith. Marine gravity anomaly from Geosat and ERS-1 satellite altimetry. Journal of geophysical research-all series, 102: 10-10, 1997.

[21] B.D. Tapley and M.C. Kim. Chapter 10 Applications to Geodesy. In: LEE-LUENG, F. and ANNY, C. (eds.) International Geophysics. Academic Press, 2001.

[22] T. Greicius. GRACE mission overview [online]. NASA. Available at: http://www.nasan.gov/mission_pages/Grace/overview/index/html [accessed 8April 2014].

[23] R.G. Henderson and I. Zietz. The computation of second vertical derivatives of geomagnetic field.Geophysics, Vol.14, pp. 508-516, 1949.

[24] T.A.Elkins. The second derivative method of gravity interpretation Geophysics, Vol. 16, pp.29-50, 1951.

[25] E.K.Darbyand E.B.Davies. Analysis and design of two-dimensional filters for two-dimensional data.Geophysical prospecting, Vol.15, pp.383-406, 1967.

[26] A. Mesko. Some notes concerning the frequency analysis for gravity interpretation. Geophysical prospecting, Vol. 31, pp.475-488, 1965.

[27] M. Dobrin. Introduction to geophysical prospecting. New York McGraw-Hill Inc., 1976.

[28] L.L.Nettleton. Gravity and magnetic in oil prospecting, New York, McGraw Hill Inc., 1976.

[29] A.E.Ibrahim.Interpretation of gravity and magnetic data from the Central African Rift System, Sudan. Ph.D. Thesis, University of Leeds, UK, 1993.

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