Geospatial Profiling for Threshold Mapping of Hydrothermal Alterations within Kushaka Schist Belt, North Central Nigeria: Implications for Mineral Exploration

Abstract

Determining thresholds are very useful and efficacious in hydrothermal alteration mapping because of their association with mineral deposits. Thus, quantifying the degree of association can serve as a reliable and definitive way of separating highly to less altered zones. This can be achieved through the application of the knowledge of the threshold values. The aim of the study is to propose a new and more effective method for defining thresholds from spatial profiles in thematic images. To achieve this, band ratio technique and threshold mapping of the study area were carried out. The band ratio method was used to delineate clay alteration by dividing band 5 on band 7. Ten (10) profiles each were generated within the study area and the maximum and minimum threshold values were determined. The results showed a close agreement and consistency between the Original Research Article Gajere et al.; AJOGER, 1(1): 1-11, 2018; Article no.AJOGER.41113 2 thresholds values derived from spatial profiling method with the values computed from other established method of threshold determination. It is also important to note that known gold mineralization points in the study area were observed to occur within the highly clay altered zones. Therefore, this study have shown that spatial profiling technique can be regarded as a valid and plausible method for determining threshold values in thematic images.

Full text

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*Corresponding author: E-mail: gajerejiriko@gmail.com;
Asian Journal of Geological Research
1(1): 1-11, 2018; Article no.AJOGER.41113
Geospatial Profiling for Threshold Mapping of
Hydrothermal Alterations within Kushaka Schist
Belt, North Central Nigeria: Implications for Mineral
Exploration
J. N. Gajere
1*
, E. A. Kudamnya
2
and W. T. Andongma
3
1
Department of Geology, Nasarawa State Polytechnic Lafia, Lafia, Nigeria.
2
Department of Geology, University of Calabar, Calabar, Nigeria.
3
Department of Geology, Kano University of Science and Technology, Wudil, Nigeria.
Authors’ contributions
This work was carried out in collaboration between all authors. Author JNG designed the study,
performed the statistical analysis, wrote the protocol and first draft of the manuscript. Author WTA
managed the analyses of the study. Author EAK managed the literature searches. All authors read
and approved the final manuscript.
Article Information
DOI: 10.9734/AJOGER/2018/41113
Editor(s):
(1)
Dr. Abdelaziz Mridekh, Professor, Department of Applied Geophysics, Ibn Tofail University, Morocco.
Reviewers:
(1)
Nguyen Ba Dai, Vietnam Academy of Sciences and Technology (VAST), Vietnam.
(2)
Tassongwa Bernard, University of Dschang, Cameroon.
(3)
Pavel Kepezhinskas, Norway.
Complete Peer review History:
http://www.sciencedomain.org/review-history/25232
Received 30
th
March 2018
Accepted 10
th
June 2018
Published 22
nd
June 2018
ABSTRACT
Determining thresholds are very useful and efficacious in hydrothermal alteration mapping because
of their association with mineral deposits. Thus, quantifying the degree of association can serve as a
reliable and definitive way of separating highly to less altered zones. This can be achieved through
the application of the knowledge of the threshold values. The aim of the study is to propose a new
and more effective method for defining thresholds from spatial profiles in thematic images. To
achieve this, band ratio technique and threshold mapping of the study area were carried out. The
band ratio method was used to delineate clay alteration by dividing band 5 on band 7. Ten (10)
profiles each were generated within the study area and the maximum and minimum threshold
values were determined. The results showed a close agreement and consistency between the
Original Research Article

Gajere et al.; AJOGER, 1(1): 1-11, 2018; Article no.AJOGER.41113
2
thresholds values derived from spatial profiling method with the values computed from other
established method of threshold determination. It is also important to note that known gold
mineralization points in the study area were observed to occur within the highly clay altered zones.
Therefore, this study have shown that spatial profiling technique can be regarded as a valid and
plausible method for determining threshold values in thematic images.
Keywords: Threshold; band ratio; spatial profiling; thematic images; hydrothermal alterations.
1. INTRODUCTION
The concept of remote sensing applied to
mineral exploration utilizes the identification of
alteration zones from well processed satellite
images. In simplifying this concept with respect
to mineral prospecting, it is believed that
hydrothermal alteration zones are generally
associated with mineral deposits [1].
Consequently, the higher the degree of
alterations, the greater the tendency of finding
mineral deposits. Therefore, quantifying the
degree of alteration is sure and reliable way of
separating highly altered areas from less altered
ones. Thus, defining a threshold values are
dependably useful in such endeavors. In mineral
exploration, threshold is a term use to signify a
specific value that effectively separate high and
low data value of fundamentally different
character that reflect different causes [2]. The
term is usually applied to a value that
distinguishes between the upper or anomalous
datasets from lower or background datasets [2].
Determination of threshold values have been
carried out for various types of geoscience
related datasets ranging from geochemical,
geophysical, remote sensing and environmental
studies using a heterogeneity of statistical
techniques [2,3,4]. This study is aimed at
inventing an innovatively new method for defining
thresholds from spatial profiles in thematic
images. The invention of this new technique
known as spatial profiling method for threshold
determination is targeted towards determining
maximum and minimum threshold values by dint
of constructing profiles across very high and very
low altered zones.
2. REGIONAL GEOLOGY OF THE STUDY
AREA
The study area is situated within latitudes 10
ᵒ
33'
32.7"N to 10
ᵒ
39' 50"N and longitudes 6
ᵒ
38' 38"E
to 6
ᵒ
43' 40"E (Fig. 1). It is located within the
Nigerian Basement Complex which forms part of
the Pan African mobile belts. It occupies the
reactivated region, a product of plate collision
between the passive continental margin of the
West African craton and the active Pharusian
continental margin affected by the Pan African
Orogeny [5,6,7]. Lithologically, the Nigerian
Basement Complex can be sub divided into
Migmatite Gneiss Complex (MGC), the Older
Metasediments, the Younger Metasediments
and the Older Granites. The Migmatite Gneiss
Complex (MGC), is the oldest rock units of the
Basement Complex, and dated as Birrimian in
age (about 2500Ma). It is believed to be of
sedimentary origin but was later profoundly
altered under metamorphic and granitic
conditions [8,9]. The Migmatite Gneiss Complex
(MGC) comprise of Archean polycyclic grey
gneiss of granodiorite to tonalitic composition
and is considered to be the basement Sensu
Stricto [10,7].
The Older meta-sediments (aged between 1100-
900Ma) are among the earliest rocks form on the
Nigerian Basement Complex, initially of
sedimentary origin with a more extensive
distribution. The Older meta-sediments have
undergone prolonged, repeated metamorphism
and now occurs as quartzites, mudrock and other
calcareous relics of highly altered clay sediments
and igneous rocks. Conversely, the Younger
Metasediments, aged between 850-700Ma, are
late pelites (represented by phyllites, muscovites
schists and biotite schists) with quartzites
forming the dominant ridge severally and
conspicuously in most parts of the belts. Some
belts of the Younger Metasediments contain
ferruginous and banded quartzites, spassetite-
bearing quartzites, conglomerates, horizon
marbles and calc-silicates.
The Older Granites of Nigeria dated 750-450Ma
[5] are widely spread throughout the basement
complex and occurs as large circular masses.
They consist of a wide spectrum of rocks which
vary in composition form tonalite through
granodiorite to granite, syenite and charnokitic
rocks [11]. The granitoids have been emplaced
into both the Migmatite-Gneiss Complex and the
schist belts. The north-south linear aggregation
of many large batholiths within the Basement
Complex suggests that they may

Gajere et al.; AJOGER, 1(1): 1-11, 2018; Article no.AJOGER.41113
3
Fig. 1. Regional Geology of the Study area (Modified after [12])
be related to deep-seated pre-existing plutonic
episodes controlled by deep mantle structures
[13]
3. METHODS
3.1 Band Ratio
Band ratio is an image processing method where
digital numbers (brightness values) of one band
is divided by that of another band. It corresponds
to the peak of high and low reflectance curves
[1]. Band ratio technique improves the contrast
and enhances compositional information while
suppressing less useful information like earth’s
surface and topographic shadow, thus,
highlighting some features that cannot be seen in
raw data [14,15,16]. Since gold mineralization
within the study area is associated with clay
alterations, a band ratio image 5/7 was
generated to show the intensity of the clay
alterations from dark colours to white. The dark
colours represents low clay alteration while the
white represents high clay alterations (Fig. 2A).
For a better display, ENVI 4.5 colour tool was
used to display these variations in clay alteration
intensity from blue which represents low to red
which represents high zones (Fig. 2B).
4. RESULTS
Gold mineralization within the study area is
known to be associated with clay alterations,
band ratio image 5/7 generated displayed the
intensity of the clay alterations from dark to white
(Fig. 2A). The low clay alteration zones are
displayed in dark colour while the white
represents high clay alterations (Fig. 2A). For
better display, ENVI 4.5 colour tool was used to
display these variations in clay alteration intensity
from blue which represents low to red which
represents high zones (Fig., 2B).
4.1 Threshold Mapping
Threshold mapping involves determination of
that value which separates regions of high and
low alteration from background. To proof the
efficacy of the spatial profiling method for
threshold determination, 10 profiles each were
constructed from anomalous high and low
altered zones.
4.1.1 Maximum threshold mapping
To determining the maximum threshold value
using spatial profiling method, 10 micro profiles
were constructed across highly altered zones
within the study area (Fig. 3). From these
profiles, the maximum reflectance value for all
the 10 profiles were extracted, summed and the
mean and standard deviation calculated (Table
1). The mean value was assumed to be the
maximum threshold value. The maximum
threshold value for each of the 10 profiles was
then used to segregate and delineate

Gajere et al.; AJOGER, 1(1): 1-11, 2018; Article no.AJOGER.41113
4
highly altered areas within the study location
(Fig. 4).
4.1.2 Minimum Threshold Mapping
This study constructed 10 profiles across least
altered zones (Fig. 5) and the extraction of their
minimum values (Table 1) ushered in the first
step towards calculating the minimum
threshold using spatial profiling method. The
minimum values, their summed, standard
deviation and the calculated threshold is
presented on Table 1. Using the minimum
value to segregate low altered zones, it was
observed that the least altered zones dominates
the eastern and western parts of the study area
(Fig. 6).
4.1.3 Validation of threshold method
The validation of spatial profiling method for
calculating threshold values for this geodata set
were carried out by comparing threshold values
obtained from spatial profiling method to
threshold values computed using other
established methods. To underscore the validity
of the spatial profiling method, the spatial
profiling threshold values were compared to a
known statistical method for calculating the
threshold as stated below.
Maximum Threshold = Mean + 2* Standard
deviation
Minimun Threshold = Mean – 2* Standard
deviation
Fig. 2. Band ratio image 5/7 displaying clay alterations within the study area
A = Black and white image, B = Colour image.
Table 1. Threshold statistics for altered imagery along profiles
P1
P 2
P 3
P4
P 5
P 6
P7
P8
P9
P10
STD
Total
Threshold
Min
1.55
1.5
1.48
1.63
1.36
1.47
1.59
1.55
1.55
1.59
4.14
15.27
1.527
Max
2.1
2.09
2.05
2.29
2.28
2.15
1.932
1.95
2
1.94
0.313
20.782
2.078
Table 2. Threshold validation for spatial profiling method
S/N
Threshold
Method
Result
1
Maximum Threshold Maximum Spatial Profiling 2.078
Mean + 2* Standard deviation 2.06
2
Minimum Threshold Minimum Spatial Profiling 1.527
Mean – 2* Standard Deviation 1.5

Gajere et al.; AJOGER, 1(1): 1-11, 2018; Article no.AJOGER.41113
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Fig. 3. Spectral Profiles across high anomalous zones within the study area (within Kushaka
Schist belt)
Fig. 4. Threshold image showing high threshold zones, the profile lines and their locations
within the study area
The comparison of threshold values from both
methods are presented in Table 2. The maximum
threshold value obtained from the application of
spatial profiling method is 2.078 and closely
agrees with 2.06 calculated using Mean + 2*
Standard Deviation, a statistical method of
calculating maximum threshold. Similarly, the
minimum threshold for the study area as
calculated using the statistical method of Mean –
2* Standard Deviation was 1.5 while 1.527
was obtained from the application of minimum
spatial profiling technique. This shows the
reliability of the application of spatial profiling
method.

Gajere et al.; AJOGER, 1(1): 1-11, 2018; Article no.AJOGER.41113
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Gajere et al.; AJOGER, 1(1): 1-11, 2018; Article no.AJOGER.41113
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Fig. 5. Spectral profile across low anomalous zones within the study area (within Kushaka
Schist belt)
Fig. 6. Threshold image showing low threshold zones, the profile lines and their locations
within the study area

Gajere et al.; AJOGER, 1(1): 1-11, 2018; Article no.AJOGER.41113
9
Fig. 7. Relationship between gold mineralization and alterations within the study area
4.2 Implications of Spatial Profiling
Technique for Mineral Exploration
Mineral exploration deals with sequential
processes of information gathering that help in
assessment of the mineral potential of an area of
interest. Mapping hydrothermal alterations is one
of the important process in gathering valuable
information on ore mineralization of any area of
interest. Thus, Remote sensing technique is
highly valuable in exploration programs due to its
ability to map not just alterations types
associated with mineralization but also the
degree of alteration. Therefore, the higher the
degree of alteration the greater the chances of
finding a mineral deposit [2]. Mapping the degree
of alteration is possible by defining threshold
values and using these values for segregating
and delineating areas of low, moderate and high
alterations (Fig. 7). Consequently, this
underscores the imperativeness of the
knowledge of alterations associated with
mineralization in the area of interest.
Furthermore, clay alteration from satellite
imagery was quantified using threshold values
derived from spatial profiling method in the study

Gajere et al.; AJOGER, 1(1): 1-11, 2018; Article no.AJOGER.41113
10
area. Known zones of gold mineralization were
plotted to establish relationship between clay
alteration and gold mineralization as determined
using the new method. This study, has therefore
proven that gold mineralization is associated with
clay alteration in the study area
5. DISCUSSION
There are several methods of threshold
determination. Iterative Mean ±2SD statistical
method (as applied in Galuszka, 2007; Hawkes
and Webb, 1962), the Box-plot method (as
applied in Turkey, 1997), the Fence method (as
applied in Schwertman and Silva, 2007;
Schwertman et al., 2004) and so many other
techniques including the probability graph,
univariate analysis, multivariate analysis of
Stanley and Sinclair, 1989, multifractal models
(as applied in Cheng and Aterberg, 1996,
Aterberg et al., 1996) have also been widely
used in threshold determination [17];[18]. Unlike
some of the methods mentioned above, this
method which is a new and innovative method of
threshold determination, does not require full
statistical details but detail knowledge of high
and low signal input zones. Spatial profiling
method also has addition advantage when used
in thematic images because it is a location-
specific data handling method, the high and low
alteration zones can easily be identify and
isolated for threshold determination in any area
of interest. The use of remote sensing technique
in mapping mineral deposits associated with
alteration has proven to be easier when
threshold values are defined and used to
segregate highly altered zones from low altered
zones. The rationale behind this method relies on
the fact that mineralization tend to increase with
the degree of alteration. From this study, it was
observed that the spatial profile method is a
reliable alternative method for defining and
quantifying geo-data set within any thematic
layer of interest. This is evident from the close
similarities of threshold values computed from
standard techniques to those obtained using
spatial profiling method. Applying the computed
values to a well processed band ratio imagery
was able to define regions of low and high
alteration within the study area. The resulting
imagery revealed highly altered zones as
dominating the central parts of the study area
along a N-S trend and is being flanked by a low
altered zone. Conversely, zones of intermediate
alterations are peppered throughout the study
area. Well known zones of gold mineralization
within the study area all plotted within the highly
altered zones confirming locations of gold
mineralization within the highly altered zones.
6. CONCLUSION
The spatial profile method for defining threshold
values in any geoscience dataset is valid and
effective especially for data displayed in thematic
layer format as shown from this study. The
application of this method for mapping
hydrothermal alterations have proven to be very
effective and can be relied upon in defining
zones of high alterations within the study area. A
close correlation has been seen to exist between
highly altered zones and known mineralization
points within the study area. Spatially, the highly
altered zones assumed a general N-S trend
within the central parts. Therefore, the higher and
the degree of alteration the greater the chances
of finding a mineral deposit. Mapping the degree
of alteration is possible by defining threshold
values though the application of spatial profiling
method and using these values to segregate and
delineate areas of low, moderate and high
alterations. The study established that known
gold mineralization points in the study area were
observed to occur within the highly altered clay
alteration zones.
COMPETING INTERESTS
Authors have declared that no competing
interests exist.
REFERENCES
1. Pour A, Hashim M. Hydrothermal alteration
mapping from Landsat-8 data, Sar
Chesmeh copper mining district, south-
eastern Islamic Republic of Iran. Journal of
Taibah University for Science. 2015;(9)
155-166.
2. Sinclair AJ. Selection of threshold values in
geochemical data using probability graphs.
J Geochem Explor.
1974;(3):129–49.
3. Aramesh AR, Afzal P, Adib A, Yasrebi BA.
Application of multifractal modelling for
identification of alteration zones and major
faults based on ETM+ multispectral data.
Arabian Journal of geosciences; 2014.
DOI: 10.1007/s12517-01401366-2
4. Chang Q. Multifractal modeling and spatial
analysis with GIS: Gold potential
estimation in the Mitchell-Sulphurets Area,
Northwestern British Columbia, Ph.D
Thesis, University of Ottawa, Ottawa.
1995;268.

Gajere et al.; AJOGER, 1(1): 1-11, 2018; Article no.AJOGER.41113
11
5. Burke KC, Dewey JF. Orogeny in Africa.
In: Dessauvagie TFJ, Whiteman AJ (ed.),
Africa geology. University of Ibadan Press,
Ibadan. 1972;583–608.
6. Black R. Precambrian of West Africa.
Episodes. 1980;4:3–8.
7. Dada SS. Proterozoic evolution of Nigeria.
In: Oshi O. (ed.) the basement complex of
Nigeria and its mineral resources (A
Tribute to Prof. M. A. O. Rahaman). Akin
Jinad and Company Ibadan. 2006;29–44.
8. McCurry P. The geology of degree sheet
21 (Zaria) (M.Sc thesis) Zaria, Nigeria,
Ahmadu Bello University. 1970;139.
9. McCurry P. The geology of the
precambrian to lower palaeozoic rocks of
Northern Nigeria– a Review. In: Kogbe
C.A. (ed) Geology of Nigeria. Elizabethan
Publishers, Lagos. 1976;15–39.
10. Rahaman MA. Recent advances in the
study of the basement complex of Nigeria.
In: Geological Survey of Nigeria (ed)
Precambrian Geology of Nigeria. 1988;
11–43.
11. Truswell JF, Cope RN. The geology of
parts of Niger and Zaria Provinces,
Northern Nigeria. Geol. Survey Nigeria.
1963;29:1–104.
12. Woakes M, Rahaman MA, Ajibade AC.
Some metallogenetic features of the
Nigerian basement. Journal of African
Earth Sciences. 1987;(6):655-664.
13. Ogezi AEO. Geochemistry and
geochronology of basement rocks from
Northwestern Nigeria. Unpublished Ph.D.
Thesis, University of Leeds; 1977.
14. Sabins FF. Remote sensing for mineral
exploration. Ore Geology Reviews.
1999;
(14):157–183.
15. Van der Meer DF. Analysis of spectral
absorption features in hyperspectral
imagery.
International Journal of Applied
Earth Observation and Geoinformation.
2004;5:55–68.
16. Ali A, Pour A. Lithological mapping and
hydrothermal alteration using Landsat 8
data: a case study in Ariab mining district,
red sea hills, Sudan. International Jour. of
Basic and Applied Sciences. 2014;3:199–
208.
17. Bolviken B, Stokke PR, Feder J, Jossay T.
The fractal nature of geochemical
landscapes. Jour. of Geochem. explor.
1992;43:91-109.
18. Cheng Q, Agterberg FP, Borham
Carter GF. A spatial analysis method
for geochemical anomaly separation.
Jour. of Geochem. Explor.
1996;56:183-
195.
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