Advances in Geological and Geotechnical Engineering Research | Vol.2, Iss.2 April 2020

Ph.D in Civil Engineering, Aristotle University of Thessaloniki, Greece. Pro-
fessor of Geotechnical Engineering and Architectural Preservation of historic buildings, Conservation Department, faculty of
archaeology, Cairo university., Egypt
Editor-in-Chief
Professor. Dr. Sayed Hemeda
Editorial Board Members
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Swostik Kumar Adhikari,Nepal

Professor. Dr. Sayed Hemeda
Journal of
Geological Research
Editor-in-Chief
Volume 2 Issue 2 · April 2020· ISSN 2630-4961 (Online)

Seismic Edge Detection by Application of Cepstral Decomposition to Data Driven Modeled
Geologic Channel Feature in Niger Delta
Orji, O.M. Ugwu, S.A. Ofuyah, W.N.
Analysis of Heavy Metals Contamination and Quality Parameters of Groundwater in Ihetu-
tu, Ishiagu
A. G. Benibo R. Sha’Ato R. A. Wuana A. U. Itodo
Mineral Chemistry and Nomenclature of Amphiboles of Garnet Bearing Amphibolites
From Thana Bhilwara, Rajasthan, India
H. Thomas Haritabh Rana
Volume 2 | Issue 2 | April 2020 | Page 1-40
Journal of Geological Research
Article
Contents
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11
34
Review of Groundwater Potentials and Groundwater Hydrochemistry of Semi-arid Hade-
jia-Yobe Basin, North-eastern Nigeria
Saadu Umar Wali Ibrahim Mustapha Dankani Sheikh Danjuma Abubakar Murtala
Abubakar Gada Abdulqadir Abubakar Usman Ibrahim Mohammad Shera Kabiru
Jega Umar
Review
20

1
Journal of Geological Research | Volume 02 | Issue 02 | April 2020
Distributed under creative commons license 4.0 DOI: https://doi.org/10.30564/jgr.v2i2.2046
https://ojs.bilpublishing.com/index.php/jgr-a
ARTICLE
Seismic Edge Detection by Application of Cepstral Decomposition to
Data Driven Modeled Geologic Channel Feature in Niger Delta
Orji, O.M.1*
Ugwu, S.A.2
Ofuyah, W.N.3
1. Department of Petroleum Engineering and Geoscience, Petroleum Training Institute, Effurun, Nigeria
2. Department of Geology, University of Port Harcourt, Nigeria
3. Department of Earth Sciences, Federal University of Petroleum Resources, Effurun, Nigeria
ARTICLE INFO ABSTRACT
Article history
Received: 23 June 2020
Accepted: 8 July 2020
Published Online: 30 July 2020
Seismic edge detection algorithm unmasks blurred discontinuity in an
image and its efficiency is dependent on the precession of the processing
scheme adopted. Data-driven modeling is a fast machine learning scheme
and a formal automatic version of the empirical approach in existence for
a long time and which can be used in many different contexts. Here, a de-
sired algorithm that can identify masked connection and correlation from
a set of observations is built and used. Geologic models of hydrocarbon
reservoirs facilitate enhanced visualization, volumetric calculation, well
planning and prediction of migration path for fluid. In order to obtain new
insights and test the mappability of a geologic feature, spectral decompo-
sition techniques i.e. Discrete Fourier Transform (DFT), etc and Cepstral
decomposition techniques, i.e Complex Cepstral Transform (CCT), etc can
be employed. Cepstral decomposition is a new approach that extends the
widely used process of spectral decomposition which is rigorous when an-
alyzing very subtle stratigraphic plays and fractured reservoirs. This paper
presents the results of the application of DFT and CCT to a two dimension-
al, 50Hz low impedance Channel sand model, representing typical geologic
environment around a prospective hydrocarbon zone largely trapped in
various types of channel structures. While the DFT represents the frequen-
cy and phase spectra of a signal, assumes stationarity and highlights the
average properties of its dominant portion, assuming analytical, the CCT
represents the quefrency and saphe cepstra of a signal in quefrency domain.
The transform filters the field data recorded in time domain, and recovers
lost sub-seismic geologic information in quefrency domain by separating
source and transmission path effects. Our algorithm is based on fast Fou-
rier transform (FFT) techniques and the programming code was written
within Matlab software. It was developed from first principles and outside
oil industry’s interpretational platform using standard processing routines.
The results of the algorithm, when implemented on both commercial and
general platforms, were comparable. The cepstral properties of the channel
model indicate that cepstral attributes can be utilized as powerful tool in
exploration problems to enhance visualization of small scale anomalies
and obtain reliable estimates of wavelet and stratigraphic parameters. The
practical relevance of this investigation is illustrated by means of sample
results of spectral and cepstral attribute plots and pseudo-sections of phase
and saphe constructed from the model data. The cepstral attributes reveal
more details in terms of quefrency required for clearer imaging and better
interpretation of subtle edges/discontinuities, sand-shale interbedding, dif-
ferences in lithology. These positively impact on production as they serve
as basis for the interpretation of similar geologic situations in field data.
Keywords:
Complex Cepstral Transform
Fourier transform
Gamnitude
Quefrency
Saphe
*Corresponding Author:
Orji, O.M.,
Department of Petroleum Engineering and Geoscience, Petroleum Training Institute, Effurun, Nigeria;
Email: orji_om@pti.edu.ng

2
Distributed under creative commons license 4.0
1. Introduction
S
eismic edge detection algorithm unambiguously
unmasks blurred discontinuity in an image and its
efficiency is dependent on the precession of the
processing scheme adopted. Data-driven modeling is a
fast developing machine learning scheme and a formal
usually automatic version of the empirical approach in
existence for long time and which can be used in many
different contexts, i.e. when manual processing and infor-
mal observations are used. Here, a desired algorithm that
can identify masked connection and correlation from a set
of observations or data is built and used.
Geologic models of hydrocarbon reservoirs facilitate
enhanced visualization, volumetric calculation, well plan-
ning and prediction of migration path for fluid. In order to
obtain new insights and test the mappability of a geologic
feature, spectral decomposition techniques i.e. Discrete
Fourier Transform (DFT), Short-Time Fourier Transform
(STFT), etc and Cepstral decomposition techniques,
i.e. Real Cepstral Transform (RCT), Complex Cepstral
Transform (CCT), etc. can be employed. Cepstral decom-
position is a new approach that extends the widely used
process of spectral decomposition which is rigorous when
analyzing very subtle stratigraphic plays and fractured
reservoirs.
This paper presents the results of the application of
DFT and CCT to a two dimensional, 50Hz low impedance
Channel sand model, representing typical geologic envi-
ronment around a prospective hydrocarbon zone. A large
number of oil and gas ﬁelds have been found to be trapped
in various types of channel structures. While the DFT
represents the frequency and phase spectra of a signal in
frequency domain, assumes stationarity and highlights
the average properties of its dominant portion, assuming
analytical, the CCT represents the quefrency and saphe
cepstra of a signal in quefrency domain. The transform
filters the field data recorded in time domain, and recovers
lost sub-seismic geologic information in quefrency do-
main by separating source and transmission path effects.
Our algorithm is based on fast Fourier transform (FFT)
techniques and the programming code was written within
Matlab software. It was developed from first principles
and outside oil industry’s interpretational platform using
standard processing routines. The results of the algorithm,
when implemented on both oil industry (e.g. Kingdom
Suite, Petrel) and general platforms, were comparable.
The cepstral properties of the channel model indicate
that cepstral attributes can be utilized as powerful tool in
exploration problems to enhance visualization of small
scale anomalies and obtain reliable estimates of wavelet
and stratigraphic parameters. The practical relevance of
this investigation is illustrated by means of sample results
of spectral and cepstral attribute plots and pseudo-sections
of phase and saphe constructed from the model data. The
cepstral attributes reveal more details in terms of quefren-
cy required for clearer imaging and better interpretation
of subtle edges/discontinuities, sand-shale interbedding,
differences in lithology and generally better delineation
and delimitation of stratigraphic features than the spectral
attributes.
Seismic visibility is enhanced through the change of
the seismic data outlook from the standard amplitude mea-
surement to a new domain in order separate fact from arti-
fact in seismic processing and interpretation. Seismic data
are usually contaminated by noise, even when the data has
been migrated reasonably well and are multiple-free [1]
. In
frequency and quefrency domains, the technique separates
fact from artifact and better geologic picture emerges.
This is necessary in hydrocarbon reservoir characteriza-
tion since a clear knowledge of a reservoir facilitates en-
hanced recovery [2]
. The Cepstrum is the Fourier transform
of the log of the spectrum of the data [3]
.
This paper is an attempt to describe aspect of innova-
tive and unconventional methods and new technology
developed for application in areas of uncertain data or
complex geology such as in deep waters, marginal fields,
fractured zones, etc. for the purpose of their development.
The presentation outline is as follows: Section one, this
section, introduces the concept of edge detection, model
types, and interpretation in more resolving domains rather
than in time, (natural data acquisition domain), and ge-
ology of the study area. In section two, the concepts of
Spectral and Cepstral decompositions are addressed, while
in section three, the methodology adopted is presented.
Section four contains the results and analysis and finally,
in section 5, the conclusions of this study are highlighted.
Geologic Background
The source of our data is the ‘Tomboy’ Basin in Niger
delta region (Figure 1). The region is a prolific hydro-
carbon province formed during three depositional cycles
from middle cretaceous to recent in Nigeria. It is located
in Nigeria between latitudes 30
N and 60
N and longitudes
40
301
E and 90
E and bounded in the west by the Benin
flank, in the east by the Calabar flank and in the north by
the older tectonic elements e.g. Anambra basin, Abakaliki
uplift and the Afikpo syncline. The Niger delta basin is
one of the largest subaerial basins in Africa. It has a sub-
aerial area of about 75,000 km2
, a total area of 300,000
km2
, and a sediment fill of 500,000 km3 [4]
. The region is
a large arcuate delta of the destructive wave dominated
DOI: https://doi.org/10.30564/jgr.v2i2.2046

3
type and is divided into the continental, transitional and
marine environments. In order of deposition, a sequence
of under compacted marine shale (Akata formation, depth
from about 11121 ft, and main source rock of the Niger
delta), is overlain by paralic or sand/shale deposits (Agba-
da formation, depth from about 7180-11121ft, are present
throughout. This is the major oil and natural gas bearing
facies in the basin. The paralic interval is overlain by a
varying thickness of continental sands (Benin formation,
depth from 0-about 6000ft, contains no commercial hy-
drocarbons, although several minor oil and gas stringers
are present) [5,6]
. Growth faults strongly influenced the
sedimentation pattern and thickness distribution of sands
and shales. Oil and gas are trapped by roll-over anticlines
and growth faults [7]
. The ages of the formations become
progressively younger in a down-dip direction and range
from Paleocene to Recent [8]
). Hydrocarbon is trapped in
many different trap configurations. The implication of this
is that geological and geophysical analyses must be so-
phisticated, a departure from the conventional, in order to
unmask hidden/by-passed reserves, usually stratigraphic
and laden with huge hydrocarbon accumulation.
N
(a) Tomboy Field, Niger Delta, cited in[9]
(b) Tomboy Field, Niger Delta: Base map of survey area showing the
arbitrary line (in Red) in the field
Figure 1. Tomboy Field, Niger Delta: (a) Bathymetric
Sea‐floor image of the Niger Delta obtained from a
dense grid of two-dimensional seismic reflection profiles
and the global bathymetric database showing the location
of the Study Area (b) Base map of survey area showing
the Arbitrary line(in Red). The Arbitrary line connects the
entire six wells (black dots). The well under consideration
is TMB 06 is deviated and located at coordinates inline
6009 and crossline 1565, right of the vertical line
2. Theory
2.1 Fourier Transform
Fourier analysis decomposes a signal into its sinusoidal
components based on the assumption that the frequency
is not changing with time (stationary). Fourier transform
allows insights of average properties of a reasonably large
portion of trace but it does not ordinarily permit exam-
ination of local variations) [10]
. This is because the convo-
lution of a source wavelet with a random geologic series
of wide window produces an amplitude spectrum that re-
sembles the wavelet. To obtain a wavelet overprint which
reflects the local acoustic properties and thickness of the
subsurface layers, a narrow window as in STFT can be
adopted. In practice, the standard algorithm used in digital
computers for the computation of Fourier transform is the
Fast Fourier Transform (FFT/DFT).
2.2 Discrete Fourier Transform (DFT)
The Discrete Fourier Transform (DFT) is the digital
equivalent of the continuous Fourier transform and is ex-
pressed as
f w f t iwt
exp
( )
= −
t
w
∑
= −∞
−∞
( ) ( )(1)
While the inverse discrete Fourier transform is
f t f w iwt
exp
( )
t
w
∑
= −∞
−∞
= ( ) ( ) (2)
where, w is the Fourier dual of the variable “t”. If ‘t’
signifies time, then ‘w’ is the
angular frequency which is related to the linear (tempo-
ral frequency) ‘f’. Also, F(w)
comprises both real (Fr(w) and imaginary Fi(w) compo-
nents. Hence
F w Fr w iFi w
( ) ( ) ( )
= + (3)
A w F w F w
[ ]
( )
= +
r i
2 2 1/2
( ) ( ) (4)
ϕ( ) tan
w = −1  
 
 
F w
F w
r
i ( )
( )
(5)
Where A(w) ard φ (w) are the amplitude and phase
spectra respectively [11]
2.3 Cepstral Transform (CT)
Cepstral decomposition is a new approach that extends

4
the widely used process of spectral decomposition. This
measures bed thickness even when the bed itself cannot
be interpreted [12]
. While spectral decomposition maps are
typically interpreted qualitatively using geomorphologic
pattern recognition or semi quantitatively, to infer relative
thickness variability Spectral decomposition is rigorous
when analyzing subtle stratigraphic plays and fractured
reservoirs. The Cepstrum processing technique gives a
solution of other signals which have been convolved or
multiplied in time domain because the operation of the
nonlinear mapping can be processed by the generalized
linear system (Homomorphic system) [13.
Cepstral analysis
is a special case of Homomorphic filtering. Homomor-
phic filtering is a generalized technique involving (a) a
nonlinear mapping to a different domain where (b) linear
filters are applied, followed by (c) mapping back to the
original domain. The independent variable of the Ceps-
trum is nominally time though not in the sense of a signal
in the time domain, and of a Cepstral graph is called the
Quefrency but it is interpreted as a frequency since we
are treating the log spectrum as a waveform. To empha-
size this interchanging of domains, [14]
coined the term
Cepstrum by swapping the order of the letters in the word
Spectrum. The name of the independent variable of the
Cepstrum is known as a Quefrency, and the linear filtering
operation is known as Liftering. The Cepstrum is useful
because it separates source and filter and can be applied to
detect local periodicity. There is a complex cepstrum [15]
and a real Cepstrum. In the “real Cepstrum”, as opposed
to the complex Cepstrum used here, only the log ampli-
tude of a spectrum is used [16].
Complex Cepstrum uses
the information of both the magnitude and phase spectra
from the observed signal. The complex Cepstrum method
is used to recover signals generated by a convolution pro-
cess and has been called Homomorphic deconvolution [17]
.
The applications can be found from seismic signal, speech
and imaging processing. Kepstrum was named by [18]
and
used for seismic signal analysis, although the literature
on its application is limited. The Kepstrum and complex
Cepstrum give almost same results for most purpose.
The Cepstrum can be defined as the Fourier transform
of the log of the spectrum. Given a noise free trace in time
(t) domain as x (t) obtained by convolution of a wavelet
w(t) and reflectivity series r(t) and assuming X (f), W (f)
and R (f) are their frequency domain equivalents, then,
Since the Fourier transform is a linear operation, the Cep-
strum is
F X F W F R
[ln (mod )] [ln(mod ) [ln (mod )]
= + (6)
To distinguish this new domain from time, to which
it is dimensionally equivalent, several new terms were
coined. For instance, frequency is transformed to Quefren-
cy, Magnitude to Gamnitude, Phase to Saphe, Filtering to
Liftering, even Analysis to Alanysis. Only Cepstrum and
Quefrency are in widespread today, though liftering is
popular in some fields [19]
.
3. Methodology
3.1 Field Data Analysis
The 3D seismic and well data used in this study were
obtained over ‘Tomboy’ field by Chevron Corporation Ni-
geria. The field data comprises a base map, a suite of logs
from six (6) wells, and four hundred (400) seismic Inlines
and 220 Crosslines. Some of the log types provided are
Gamma-Ray (GR), Self-Potential (SP), Resistivity, Den-
sity, Sonic, etc. Lithologic logs of Gamma-Ray and Self
Potential were first plotted to identify the sand (hydrocar-
bon) unit of interest and then correlated with Resistivity
logs. This Interval corresponds to 2648-2672 milliseconds
using time-depth conversion. It is important to state that
rather than use measured seismic line near the well (TMB
06) under examination for seismic-to-well tie, as is tradi-
tionally done, a line (arbitrary) connecting the entire wells
was constructed to enhance the seismic data quality for
the tie since it integrates the general geologic information
in the survey.
3.2 Computation and Decomposition of Channel
Model
We computed the frequency attributes of a Channel sand
model of low impedance.. The Channel represents spatial
variation of the distribution of sediments and rocks in
the subsurface and can exist anywhere from river basins
to deep-sea environments. Several of the world’s oil and
gas ﬁelds are developed from channel environments. It
was examined with a zero phase Ricker wavelet of 50Hz
center frequency using the fast Fourier transform (FFT)
convolution technique. The Ricker wavelet was convolved
with a four-layer reflectivity series, where the third layer
is the channel feature. The computed model is presented
as Figure 8. The acoustic velocity values used are 7926.83
ft/s inside the channel and 9031.45 ft/s outside the chan-
nel showing that channel bed, about 35.4 ms thick, is a
low impedance layer (Tables 1.0 and 1.1). The computed
model is inherently noisy since well data was involved in
its computation. Recall that Seismic data are usually con-
taminated by noise, even when the data has been migrated
reasonably well and are multiple-free [20]
.
The effective offset in Figure 8 is 0 to 2T, where T rep-
resents period. The Thickness of the channel is denoted in

5
units of the dominant (center) period corresponding to the
dominant frequency of the Ricker wavelet (zero-phase)
used in modeling. The center frequency used for simulation
is 50Hz implying a period of 20 milliseconds. The spectral
and cepstral properties of the model such as amplitude and
phase spectra as well as and gamnitude and saphe cepstra
highlighting tuning effects are displayed as Figure 9.
The model was data- driven and developed to test the
resolution capability of the transforms algorithms and
to calibrate the model. The transforms employed are the
Discrete Fourier Transform (DFT) and the Complex Cep-
stral Transform (CCT). The SEG Y data was loaded into
Petrel software and a reconnaissance was performed on
the seismic sections of the field. A channel feature was
identified between inlines 5880 and 6190 and crossline
1565. Well 06 penetrated the structure around inline 6009.
From the log data of Well 06, some model parameters
were extracted and then used to compute new parameters
necessary for model computation. The Shale reference
point was set at 60 American Petroleum Institute (API)
units for GR log. Therefore, Formations with less than ()
60API units were read as Sands, while those greater than
() 60 API units were read as Shale. Representative model
parameters were extracted from Well 06 log data at appro-
priate depths. The data consist of the GR, RHOB and ITT
readings. The logs were correlated with Self Potential (SP)
and Resistivity logs. This was followed by the computa-
tion of parameters like velocity, acoustic impedance and
reflection coefficient used for the modeling of the channel
sand structure. The convolution equation used is given by
S t W t R t
( ) ( ) * ( )
= (7)
Where S (t) = Synthetic Seismogram; W (t) = Ricker
Wavelet and R (t) = Reflection Coefficient.
The maximum useful frequency or centre frequency
was set at 50Hz. This frequency was selected on the basis
of apriori information of the general seismic bandwidth
of 5-65Hz and the need to capture some structural events.
Majority of the stratigraphic traps have structural elements
and in some cases the distinction is difficult [21]
. Several
center frequencies were explored (Figure 6). The channel
seismogram consists of 50 seismic traces presented in the
wiggle format.
4. Results and Interpretation
In seismic attribute analysis, amplitude or magnitude, or
envelope indicates local concentration of energy, bright
spots, gas accumulation, sequence boundaries, unconfor-
mities, major changes in lithology, thin bed tuning effects,
etc; phase measures lateral continuity/discontinuity/edge)
or faulting, shows detailed visualization of bedding con-
figuration and has no amplitude information. In the case of
the phase attribute, there is a flip owing to sign reversal [22]
.
The frequency attribute reflects attenuation spots, indicates
hydrocarbon presence by its low frequency anomaly, shows
edges of low impedance thin beds, fracture zone indica-
tion-appears as low frequency zones, and also indicates
bed thickness. Higher frequencies indicate sharp interfaces
or thin shale bedding, lower frequencies indicate sand rich
bedding, sand/shale ratio indicator [23]
. In Cepstral domain,
the Gamnitude, Saphe and Quefrency are interpreted in a
similar manner to Magnitude, Phase and Frequency in the
Spectral domain. Saphe highlights discontinuity/edge and
lithologic changes, while Quefrency indicates fracture zone,
hydrocarbon presence by its low values.
Figure 2. Tomboy Field, Niger Delta: Seismic Section
showing Channel feature. (Petrel Platform)
Figure 3. Well log analysis: Gamma Ray Log of Well
06 showing picked horizons for model computation.
(Gnuplot-General platform)

6
TABLES OF MODEL PARAMETERS
Table 1.0: Extracted Values of Some Well Parameters of Well 06
s/n Depth
(ft)
Layer H
(ft)
TWT
(ms)
TWT (AVE)
(ms)
GR
(API units)
SP
( mV)
RHOB
‘δ’
(g/cm3
)
TT
(µsec/ft)
ɸ
(%)
Vsh
1 5738.0 A Top 37.5 2217.92 2225.16 70.30 346.42 2.17 115.56 35.93 0.3036
5775.5 Base 2232.41 63.75 325.56 2.25 123.86
2 7368.5 B Top 56.5 2855.23 2866.25 59.92 299.66 2.23 110.41 33.41 0.2746
7424.0 Base 2877.28 67.99 283.10 2.32 111.04
3 7435.0 C Top 90.5 2881.39 2899.09 14.11 306.86 2.18 129.64 37.54 0.0364
7525.5 Base 2916.80 29.85 289.31 2.08 122.85
4 9105.0 Top 187.5 3534.57 3571.13 96.25 -49.12 2.40 110.85 32.30 0.6324
9292.5 D Base 3607.70 76.38 -32.58 2.21 103.50
5 9675.0 Top 3757.14 94.68 -20.81 2.26 102.84
Table 1.1: Computed Values of Some Well Parameters of Well 06
s/n Depth (ft) Layer H (ft) TWT
(AVE)
RHOB ‘δ’
(g/cm3
)
Velocity
‘V’
(ft/s)
AV E
‘δ’
AV E
‘V’
Z = δV Zb-Za Zb+Za RC==
𝑍𝑍2−𝑍𝑍1
𝑍𝑍2+𝑍𝑍1
1 5738.0 A 37.5 2225.16 2.17 8653.51 2.21 8363.57 18483.48 Z1 Z2-Z1 Z2 +Z1 0.0517 R1
5775.5 2.25 8073.63 2017.91 38984.87
2 7368.5 B 56.5 2866.25 2.23 9057.15 2.27 9031.45 20501.39 Z2 Z3 –Z2 Z3+Z2 -0.0967 R2
7424.0 2.32 9005.76 -3617.25 37385.53
3 7435.0 C 90.5 2899.09 2.18 7713.66 2.13 7926.83 16884.14 Z3 Z4-Z3 Z4 +Z3 0.1199 R3
7525.5 2.08 8140.00 4601.33 38369.61
4 9105.0 D 187.5 3571.04 2.40 9021.19 2.30 9341.51 21485.47 Z4 Z5-Z4 Z5+Z4 0.0114 R4
9292.5 2.21 9661.83 497.18 43468.12
5 9675.0 2.26 9726.84 2.26 9726.84 21982.65 Z5
Where h = Interval Thickness; Z =Acoustic Impedance; RC= Reflection Coefficient; AVE = Average Values; TWT = Two Way Travel Time; TT =
Transit Time; ɸ = Porosity; Vsh = Volume of Shale; Velocity ‘V’ =
106
𝑡𝑡
where t = Sonic Transit time or Wave Slowness (µsec/ft), RC =
𝑍𝑍2−𝑍𝑍1
𝑍𝑍2+𝑍𝑍1
A schematic diagram incorporating all model parame-
ters of the channel is shown in Figure 4.
Figure 4. A Schematic diagram of the Channel Feature
(Shown in Red)
-80 -60 -40 -20 0 20 40 60 80
-0.5
0
0.5
1
ZERO PHASE RICKER WAVELET,CENTER FREQUENCY:50.0HZ
WAVE
AMPLITUDE
WAVELET TIME INTERVAL,K[MS]:(SAMPLING TIME UNIT)
Figure 5. Zero Phase Ricker Wavelet for Channel Sand
Model with Centre Frequency of 50Hz
-80 -60 -40 -20 0 20 40 60 80
-0.5
0
0.5
1
WAVE
AMPLITUDE
(a) Zero Phase Ricker Wavelet for Channel Sand Model at Centre
Frequency of 5Hz
-80 -60 -40 -20 0 20 40 60 80
-0.5
0
0.5
1
WAVE
AMPLITUDE
(b) Zero Phase Ricker Wavelet for Channel Sand Model at Centre
Frequency of 10Hz
-80 -60 -40 -20 0 20 40 60 80
-0.5
0
0.5
1
WAVE
AMPLITUDE
(c) Zero Phase Ricker Wavelet for Channel Sand Model at Centre
Frequency of 20Hz
-80 -60 -40 -20 0 20 40 60 80
-0.5
0
0.5
1
WAVE
AMPLITUDE
(d) Zero Phase Ricker Wavelet for Channel Sand Model at Centre
Frequency of 25Hz

7
-80 -60 -40 -20 0 20 40 60 80
-0.5
0
0.5
1
WAVE
AMPLITUDE
(e) Zero Phase Ricker Wavelet for Channel Sand Model at Centre
Frequency of 30Hz
-80 -60 -40 -20 0 20 40 60 80
-0.5
0
0.5
1
WAVE
AMPLITUDE
(f) Zero Phase Ricker Wavelet for Channel Sand Sand Model at
Centre Frequency of 40Hz
-80 -60 -40 -20 0 20 40 60 80
-0.5
0
0.5
1
WAVE
AMPLITUDE
(g) Zero Phase Ricker Wavelet for Channel Sand Model at Centre
Frequency of 50Hz
-80 -60 -40 -20 0 20 40 60 80
-0.5
0
0.5
1
WAVE
AMPLITUDE
(i) Zero Phase Ricker Wavelet for Channel Sand Model at Centre
Frequency of 60Hz
Figure 6. Zero Phase Ricker Wavelet Analysis at Various
Center Frequencies and Time Breadths
0 10 20 30 40 50 60 70 80 90 100
0
2
4
6
8
AMPLITUDE AND PHASE SPECTRA(50HZ RICKER WAVELET)
ABS.
MAGNITUDE
0 10 20 30 40 50 60 70 80 90 100
-0.2
-0.15
-0.1
-0.05
0
PHASE
[DEGREES]
FREQUENCY [HERTZ]
(a): Amplitude and Phase Spectra (50Hz Ricker Wavelet)
0 10 20 30 40 50 60 70 80 90 100
0
0.5
1
1.5
2
AMPLITUDE AND PHASE SPECTRA(SAND-REFLECTIVITY)
ABS.
MAGNITUDE
0 10 20 30 40 50 60 70 80 90 100
0
0.02
0.04
0.06
0.08
PHASE
[DEGREES]
FREQUENCY [HERTZ]
(b) Amplitude and Phase Spectra (Sand-Reflectivity)
0 10 20 30 40 50 60 70 80 90 100
0
0.5
1
1.5
AMPLITUDE AND PHASE SPECTRA(SHALE-REFLECTIVITY)
ABS.
MAGNITUDE
0 10 20 30 40 50 60 70 80 90 100
0
0.05
0.1
0.15
0.2
0.25
PHASE
[DEGREES]
FREQUENCY [HERTZ]
(c) Amplitude and Phase Spectra (Shale-Reflectivity)
Figure 7. Amplitude and Phase Spectra (Sand and Shale
Reflectivities)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
-4500
-4000
-3500
-3000
-2500
-2000
CHANNEL SAND MODEL SIMULATED WITH 5OHz RICKER WAVELET
TRACE(TWT)(mSECONDS)
LINE(PERIOD,T(SECONDS))
Figure 8. 50-Trace, 50Hz Field Data-Derived Channel
Model: Original amplitude

8
0 10 20 30 40 50 60 70 80 90 100
0
2
4
6
8
MAGNITUDE AND PHASE SPECTRA OF CHANNEL MODEL
ABS.
MAGNITUDE
0 10 20 30 40 50 60 70 80 90 100
0
2000
4000
6000
8000
10000
12000
PHASE
[DEGREES]
FREQUENCY [HERTZ]
(a) Magnitude and Phase Spectra of Channel Model
0 10 20 30 40 50 60 70 80 90 100
0
100
200
300
400
GAMNITUDE AND SAPHE CEPSTRA OF CHANNEL MODEL
ABS.
GAMNITUDE
0 10 20 30 40 50 60 70 80 90 100
0
0.5
1
1.5
2
x 10
4
SAPHE
[DEGREES]
QUEFRENCY [HERTZ]
(b) Gamnitude and Saphe Cepstra of Channel Model
Figure 9. Spectra and Cepstra of 50Hz Field Data-De-
rived Channel Model. There is more information recovery
in the Cepstra plot as reflected in the attributes shown
0 10 20 30 40 50 60 70 80 90 100
0
5
GAMNITUDE AND SAPHE CEPSTRA OF CHANNEL MODEL
ABS.GAM.
QUEF[Hz]
0 10 20 30 40 50 60 70 80 90 100
0
1
2
x 10
5
SAPHE[DEG.]
QUEF.[Hz]
0 10 20 30 40 50 60 70 80 90 100
0
5
MAGNITUDE AND PHASE SPECTRA OF CHANNEL MODEL
ABS.MAG
FREQ[Hz]
0 10 20 30 40 50 60 70 80 90 100
0
5000
10000
PHASE[DEG]
FREQ[Hz]
Figure 10. 50 Hz Field Data-Derived Channel Model: An
integrated display of Spectral and Cepstral attributes plots
to illustrate their resolving capabilities
Figure 11. Seismic Section Showing Channel Feature.
(Petrel Platform)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
-4500
-4000
-3500
-3000
-2500
-2000
Figure 12. 50-Trace, 50 Hz Field Data-Derived Channel
Model: Original Model Data
(a) Field Seismic Section showing channel feature. (Petrel Platform)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
-4500
-4000
-3500
-3000
-2500
-2000
(b) 50-Trace, 50 Hz Field Data-Derived Channel Model: Original
Model Data
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
-4500
-4000
-3500
-3000
-2500
-2000
PHASE(PERIOD,T(SECONDS))
data1
data2
data3
data4
(c) An abridged four (4)-trace Phase Attribute Section by Discrete
Fourier Transform to indicate improved lithologic change/segmen-
tation. Data1: Shale, data2: Sand, data3: Sand, data4: Shale.

9
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
-4500
-4000
-3500
-3000
-2500
-2000
SAPHE(PERIOD,T(SECONDS))
data1
data2
data3
data4
(d) An abridged four (4)-trace Saphe Attribute Section by Cepstral
Transform to indicate enhanced Lithologic change/segmentation.
Data1: Shale, data2: Sand, data3: Sand, data4: Shale,
Figure 13. 50 Hz: Comparative display of Field Seismic
Section, Data-Derived Channel Model, and an abridged
Phase and Saphe Attribute Sections
. 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
-4500
-4000
-3500
-3000
-2500
-2000
(a) 50 Hz Field Data-Derived Channel Model: Original Model Data
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
-4500
-4000
-3500
-3000
-2500
-2000
PHASE(PERIOD,T(SECONDS))
(b) 50 Hz Field Data-Derived Channel Model: DFT Phase Section
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
-4500
-4000
-3500
-3000
-2500
-2000
SAPHE(PERIOD,T(SECONDS))
(c) 50 Hz Field Data-Derived Channel Model: CCT Saphe Section
Figure 14. Comparative Display of Field Data-Derived
50Hz Channel Model, DFT Phase and CCT Saphe attri-
butes
5. Conclusions
We have investigated spectral and cepstral decomposition
of data driven geologic channel sand, about 35ms thick
obtained by convolution of a 50 Hz zero phase Ricker
wavelet with a four-layer reflectivity series, where the
third layer is the channel bed. The Discrete Fourier and
Complex Cepstral transforms were used to highlight the
channel’s average/response and precise attributes. Our
aim was to develop a practical method for processing and
mapping of stratigraphy which is usually masked after
normal data interpretation. The DFT and CCT were used
to calibrate and analyze a computed channel model with
respect to subtle signal variation as obtained in field strati-
graphic works.
The results obtained(from the samples presented) show
the resolution capability of the Complex Cepstrum in
separating source and filter and the detection of local peri-
odicity which are critical geological parameters in under-
standing stratigraphic details and hydrocarbon fairways
which impact on enhanced recovery. We implemented
it on both standard and general platforms and found the
match, on comparison to be convincing. This technology
has application in the delimitation, delineation and char-
acterization of subtle geologic targets such as thin-bed
reservoir, areas of uncertainty in data and time such as in
complex geologic environments as in deep waters, mar-
ginal fields, etc and and similar geologic situations.
Acknowledgments
The authors wish to thank Chevron Corporation, Nigeria
for making the Seismic and well data available for use.
Thanks are also due to the Authorities of University of
Port Harcourt, Nigeria, Federal University of Petroleum
Resources, Effurun, Nigeria and the Petroleum Training
Institute, Effurun, Nigeria for the use of their computing
facilities.
References
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plication of Complex Seismic Attributes in Thin Bed
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[3] Hall, M. Predicting Stratigraphy with Cepstral de-
composition. The leading Edge 25 (2), February
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[7] Weber, K. J. Hydrocarbon Distribution patterns in
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[8] Merki, P. J. Structural Geology of the Cenozoic Ni-
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[14] Bogert,B.P. Healy, M. J. R., Tukey,: J. W. The Que-
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and Saphe Cracking. Proceedings of the Symposium
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[15] Oppenheim,A.V. Superposition in a Class of Non-
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[17] Oppenheim, A.V., Schafer, R. W. Homomorphic
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11
ARTICLE
Analysis of Heavy Metals Contamination and Quality Parameters of
Groundwater in Ihetutu, Ishiagu
A. G. Benibo*
R. Sha’Ato R. A. Wuana A. U. Itodo
Department of Chemistry and Centre for Agrochemical Technology Environmental Research (CATER), Federal Uni-
versity of Agriculture, Makurdi, Nigeria
Article history
Received: 28 June 2020
The levels of some quality parameters and heavy metals in groundwater in
Ihetutu minefield of Ishiagu were analyzed in four seasons (rainy, late rainy,
dry, and late dry), in order to evaluate the deterioration of the groundwater
qualities in the area. Pb-Zn mining and several other related activities have
been going on for several decades in Ihetutu, and thus render the groundwa-
ter resources in the area less available for consumption, through toxic chem-
ical substances expected to be constantly discharged to the groundwater
bodies from the mines and other domestic wastes. The aim of this study was
thus to determine the levels of heavy metals and other physico-chemical
properties in the groundwater, to assess its suitability for drinking and other
domestic purposes in Ihetutu. Samples were collected from dug-wells and
underground water platforms, and analyzed using standard procedures, for
their physico-chemical properties and heavy metals levels. Results obtained
for the various seasons ranged as pH = 6.80-8.72, EC = 190.00-1120.00 µS/
cm, alkalinity = 4.20-30.60 mg/L, TDS = 105.00-567.00 mg/L, TH = 8.00-
44.00 mg/L, Cl- = 26.00-126.00 mg/L, Cu = 0.00-0.30 mg/L, Zn = 0.00-
0.42 mg/L, Fe = 0.00-3.93 mg/L, Mn = 0.00-0.59 mg/L, and Pb = 0.00-0.43
mg/L. Average levels of analyzed parameters in study area were: pH = 7.56,
EC = 424.06 µS/cm, alkalinity = 17.88 mg/L, TDS = 218.69 mg/L, TH =
21.88 mg/L, Cl- = 54.31 mg/L, Cu = 0.20 mg/L, Zn = 0.51 mg/L, Fe = 2.55
mg/L, Mn = 0.32 mg/L, Pb = 0.38 mg/L. Mean levels of most parameters
were found to be within standard guidelines/limits but were above control
levels, giving an indication of deterioration of the groundwater qualities in
the area. Also, seasonal concentrations of most parameters, including the
heavy metals were in the order of LDSDRSLRSRNS. Heavy metals
mean concentrations also trended in the order of FeZnPbMnCu. Cor-
relations among heavy metals were all positive, with the strongest between
Cu and Pb (r = 0.921) while the least was between Cu and Mn (r = 0.176).
ANOVA showed no statistically significant differences among sampling
stations in study area, as p-values (0.757) was higher than the significance
level (α=0.05). Comparison of the results with control values, indicated
cases of deterioration of the groundwater quality in the study area. This
confirmed that the groundwater resources in the area were adversely affect-
ed by wastes and discharges from the mining activities and several other
sources including domestic wastes.
Keywords:
Contamination
Pollution
Environment
Mining
Groundwater
A. G. Benibo,
Department of Chemistry and Centre for Agrochemical Technology Environmental Research (CATER), Federal University of
Agriculture, Makurdi, Nigeria;
Email: ao_benibo@yahoo.com

12
1. Introduction
M
ining has become an indispensable component
of economic resource at Ihietutu, Ishiagu, in
Ivo River Local Government Area of Ebonyi
State of Nigeria. Ihetutu mine in Ishiagu is the oldest
mine in his study on lead (Pb) mining carried out at four
mining sites in Ebonyi State [1]
. It is thus expected that
various toxic chemical substances including heavy metals,
etc must have accumulated to very high levels in the area,
considering the very long time of existence and operation
of the mines.
Mining operations constitute the most important sourc-
es of pollutants such as heavy metals and many other tox-
ic chemical substances in the environment. It is a business
that seriously damages the environment [2]
. Its operations
and associated industries generate large volumes of waste-
water, drainage wastes and tailings, which plunders the
landscape and contaminate the surrounding environment
with inorganic pollutants, particularly heavy metals. Most
mining operations have serious adverse effect on air, wa-
ter, soil and vegetation [3]
. On a global scale, it was esti-
mated that about 3000 billion tons of mine overburden is
dumped annually, and that about 386,000 hectares of land
is disturbed by mining activities [4]
.
Activities of mining are well known for their danger-
ous impact on the environment due to deposition of large
volume of waste on the soil and water. Adverse environ-
mental consequences of open pit mining include sediment
and water qualities degradation due to destruction of veg-
etation, exposure of the soil to surface run-offs, as well as
dumps that have been confirmed to accommodate harmful
minerals and chemicals that contaminate the soil, plant,
water and air quality [5]
.
Various chemicals used during ore processing cause
high degree of pollution of groundwater bodies. Through
wrong application, faulty disposal system, poor storage
system and several other conditions prevalent at the time
of operations, these chemicals used at mine sites could
also cause intense pollution of the environment [6]
. Water
pollution increases due to human population, industrial-
ization, the use of fertilizers in agriculture and man-made
activity[7]
, which include mining operations, artisan activi-
ties; and natural sources such as weathering of rocks.
The objective of this research was to evaluate the qual-
ity of groundwater available for drinking and other do-
mestic purposes in Ihetutu where several mining activities
have been ongoing for several decades now. Groundwater
resources were only some few kilometers away from the
numerous Pb-Zn mining sites, and were thus expected to
be seriously polluted by wastes leachates and discharges
from the mines and its wastes dumps and tailings; and
other point and non-point sources including domestic
wastes and run-offs from farms. This suspicion made it
imperative to carry out this study. Huge amount of toxic
chemical substances constantly discharged into ground-
water bodies have become sources of contamination and
threat to human health, thus making assessment of their
levels and impacts a necessary one.
2. Materials and Methods
2.1 The Study Area
The Ihetutu Hill is located in Ishiagu, Ebonyi State of
Nigeria, and is within the Lower Benue trough. Lead-zinc
and hard rock (aggregate) mining has been ongoing in the
area since the 1950s. The Ishiagu area covers an expanse
of about 450 km2
and supports an estimated population
of over two hundred and fifty thousand persons [8,9]
. The
study area falls within latitudes 5o
51/
N and 5o
59/
N and
longitudes 7o
24/
E and 7o
40/
E covering an area of over
450 km2
. The area is accessible through the Enugu - Port
Harcourt Railway line, the Enugu-Port Harcourt oil pipe-
line, the Enugu - Port Harcourt Express Road, the Lekwe-
si-Obiagu Road which, and the Okigwe - Afikpo Road [10]
(Figures 1).
Figure 1. Map showing sampling stations in study and
control areas
2.2 Sample Collection and Analysis
Samples were collected in four seasons including rainy
season (May), late rainy season (September), dry season
(December), and late dry season (April) from both study
and control areas (which is about 12 km away from the
study area). Four groundwater samples were collected from
the study area, each season, directly from dug-wells and
underground spring water platforms and labeled as SGW9,

13
SGW10, SGW11, SGW12, while one sample was collected
from the control area and labeled as CGW2 each season also.
Collected samples were digested and analyzed to determine
the physico-chemical parameters and heavy metal concen-
trations, using standard methods and procedures[11]
. pH and
Electrical Conductivity were determined in-situ (on site).
Table 1. Sampling Field Data Summary
Sampling
Stations
Sampling Dates
Sampling
Seasons
Station
Locations
Latitude Longitude
CGW2
(Control)
13/05/2018;
29/09/2018;
29/11/2018;
12/04/2019
RNS;
LRS;
DRS;
LDS
Ukwu
Okwe
Well, Utu-
ru.
N 5o
50'54 E 7o
29'32
SGW9
13/05/2018;
01/10/2018;
01/12/2018;
14/04/2019
RNS;
LRS;
DRS;
LDS
Ogwu
spring
well, Ihetu-
tu.
N 5o
57'3 E 7o
33'4
SGW10
13/05/2018;
01/10/2018;
01/12/2018;
14/04/2019
RNS;
LRS;
DRS;
LDS
Idu Com-
pound
Well, Ihet-
utu.
N 5o
57'7 E 7o
33'6
SGW11
13/05/2018;
01/10/2018;
01/12/2018;
14/04/2019
RNS;
LRS;
DRS;
LDS
Amaog-
wute
Well, Ihet-
utu.
N 5o
57'11 E 7o
33'8
SGW12
13/05/2018;
01/10/2018;
01/12/2018;
14/04/2019
RNS;
LRS;
DRS;
LDS
Amaukwa
Well,
Ihetutu.
N 5o
57'12 E 7o
33'15
Note: RNS = Rainy Season, LRS = Late Rainy Season, DRS = Dry Sea-
son, LDS = Late Dry Season
3. Results and Discussion
3.1 Physico-chemical Properties of Groundwater
in Ihetutu
3.1.1 pH
pH peaked during the dry season (DRS) at CGW10,
CGW11, CGW12 but during the late dry season (LDS) at
CGW9 and the control station (CGW2); while the lowest
values at all sampling stations were recorded during the
rainy season (RNS) (Figure 2). Mean pH values range was
7.46-7.67, with SGW10 having the highest and SGW12
the lowest. However, the control groundwater (CGW2)
with a mean value of 7.32 is lower than the mean pH
values of all the samples from the study area (Table 2).
Average pH value in study area was 7.56 (Table 3). This
value was within the standard guidelines of USEPA,
SON, NESREA and WHO (Table 4). The increased pH
values in the groundwater samples could be due to the in-
creasing buffering capacity of alkaline minerals leaching
from surrounding underground and surface rocks/soil, to
the groundwater. The increase in pH could also be due
to the reduction in the rate of photosynthetic activities in
the well, and absorption of carbon dioxide and bicarbon-
ates[12]
. Discharge of domestic waste and other organic
pollutants into the water bodies that run through the farms
and located along the paths of the villagers could also be
responsible for the increase in pH[13]
.
Table 2. Mean values of physico-chemical parameters and
Heavy Metals in groundwater
Parameter (CGW2) SGW9 SGW10 SGW11 SGW12
pH 7.32 7.53 7.67 7.58 7.46
EC (µS/cm) 184.75 251.25 475.50 662.00 307.50
TDS (mg/L) 128.50 136.50 245.00 333.50 159.75
TH (mg/L) 22.00 13.90 25.15 23.53 24.95
Alkalinity (mg/L) 19.03 11.88 26.28 19.95 13.40
Cl-
(mg/L) 70.75 42.25 56.25 79.00 39.75
Cu (mg/L) 0.25 0.14 0.27 0.18 0.27
Fe (mg/L) 3.39 1.86 3.52 3.47 2.23
Zn (mg/L) 2.40 000 0.41 0.38 0.74
Mn (mg/L) 0.07 0.10 0.37 0.54 0.22
Pb (mg/L) 0.33 0.00 0.42 0.30 0.41
3.1.2 Electrical Conductivity
Mean EC ranged from 251.25 to 662.00 µS/cm with
SGW11 having the highest value while SGW9 had the
lowest. All study area values were higher than that of con-
trol (CGW2) (Table 2). Seasonal conductivity values for
groundwater samples from the study area also increased in
the order of RNSLRSDRSLDS (Figure 3), exception
of SGW12 which peaked during the dry season (DRS).
Average conductivity value in study area was 424.06 µS/
cm (Table 3). This was above EU standard value of 250
µS/cm but below SON standard value of 1000 µS/cm (Ta-
ble 4). High concentration of dissolved salts due to poor
irrigation management, minerals from rain water runoffs,
or discharges (leachates) from mines could lead to in-
crease in conductivity[14]
.
3.1.3 Total Dissolved Solids (TDS)
Mean TDS values ranged from 136.50 to 333.50 mg/L,
and were all higher than the mean value of the control sam-
ple (CGW2) which was 128.50 mg/L (Table 2). Seasonal
TDS values for the samples also increased in the order of
RNSLRSDRSLDS, exception of SGW12 which rather
peaked during the dry season (DRS) (Figure 4). Average
TDS value in study area was 218.69 mg/L (Table 3), and
was below USEPA, SON and NESREA guidelines (Table
4). The groundwater samples mean values were all below
standard reference values indicating a rating of no overall
pollution. Decrease in mean TDS concentration in ground-

14
water samples could also result from high dilution effect
from the rain water during the rainy seasons. The low con-
centration of TDS especially in the groundwater, and some
surface water samples could also be due to the presence of
granitic materials which resists dissolution in that area[15]
.
3.1.4 Alkalinity
Alkalinity increased from rainy to dry season in the sam-
ples, though there was a decrease in the late dry season
(LDS) at SGW10, SGW11 and SGW12 (Figure 5). Mean
values also ranged from 11.88 to 26.28 mg/L, with SGW9
having the lowest value and SGW10 the highest. Com-
pared with control (CGW2) value of 19.03 mg/L, SGW9
and SGW12 values were lower while those of SGW10
and SGW11 were higher (Table 2). Increase in alkalinity
could be due to the discharge of carbonate and bicarbon-
ate salts from surrounding rocks/soils to the water bodies.
Average alkalinity value in study area was 17.88 mg/L
(Table 3). It has been reported that, in the Ishiagu mining
area, there is significant volume of mine waste and large
scale presence of carbonate minerals, especially dolomite
and siderite, which makes the acid mine drain (AMD) in
the area to tend towards a neutral or alkaline state [16]
.
-
2.00
4.00
6.00
8.00
10.00
S
G
W
9
S
G
W
1
0
S
G
W
1
1
S
G
W
1
2
C
G
W
2
pH
Value
Sampling Stations
pH
RNS
LRS
DRS
LDS
Figure 2. Seasonal levels of pH
-
200.00
400.00
600.00
800.00
1,000.00
1,200.00
S
G
W
9
S
G
W
1
0
S
G
W
1
1
S
G
W
1
2
C
G
W
2
Conductivity
(µS/cm
)
Sampling Stations
EC
RNS
LRS
DRS
LDS
Figure 3. Seasonal concentrations of EC
-
100.00
200.00
300.00
400.00
500.00
600.00
S
G
W
9
S
G
W
1
0
S
G
W
1
1
S
G
W
1
2
C
G
W
2
Concentration
(mg/L)
Sampling Stations
TDS
RNS
LRS
DRS
LDS
Figure 4. Seasonal concentrations of TDS
-
5.00
10.00
15.00
20.00
25.00
30.00
35.00
S
G
W
9
S
G
W
1
0
S
G
W
1
1
S
G
W
1
2
C
G
W
2
Concentration
(mg/L)
Sampling Stations
Alkalinity
RNS
LRS
DRS
LDS
Figure 5. Seasonal concentrations of alkalinity
-
10.00
20.00
30.00
40.00
50.00
S
G
W
9
S
G
W
1
0
S
G
W
1
1
S
G
W
1
2
C
G
W
2
Concentration
(mg/L
Sampling Stations
Total Hardness
RNS
LRS
DRS
LDS
Figure 6. Seasonal concentrations of Total Hardness

15
-
20.00
40.00
60.00
80.00
100.00
120.00
140.00
S
G
W
9
S
G
W
1
0
S
G
W
1
1
S
G
W
1
2
C
G
W
2
Concentration
(mg/L)
Sampling Stations
Chloride
RNS
LRS
DRS
LDS
Figure 7. Seasonal concentrations of Chloride
-
0.50
1.00
1.50
2.00
2.50
SGW9
SGW10
SGW11
SGW12
CGW2
WHO
USEPA
EU
SON
NESREA
Concentration
(mg/L)
[Cu]
Figure 8. Mean Conc. of Cu in Groundwater, with Con-
trol and Standard guidelines
-
1.00
2.00
3.00
4.00
5.00
6.00
SGW9
SGW10
SGW11
SGW12
CGW2
WHO
USEPA
EU
SON
NESREA
Concentration
(mg/L)
[Zn]
Figure 9. Mean Conc. of Zn in Groundwater, with Con-
-
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
SGW9
SGW10
SGW11
SGW12
CGW2
WHO
USEPA
EU
SON
NESREA
Concentration
(mg/L)
[Fe]
Figure 10. Mean Conc. of Fe in Groundwater, with Con-
-
0.10
0.20
0.30
0.40
0.50
0.60
SGW9
SGW10
SGW11
SGW12
CGW2
WHO
USEPA
EU
SON
NESREA
Concentration
(mg/L)
[Mn]
Figure 11. Mean Conc. of Mn in Groundwater, with Con-
0.00
0.10
0.20
0.30
0.40
0.50
SGW9
SGW10
SGW11
SGW12
CGW2
WHO
USEPA
EU
SON
NESREA
Concentration
(mg/L)
[Pb]
Figure 12. Mean Conc. of Pb in Groundwater, with Con-

16
3.1.5 Total Hardness
Hardness is a measure of the capacity of water to form
precipitates or foam with soap and scales with certain
ions present in the water[17]
. It is defined as the sum of
the concentrations of calcium (Ca2+
) and magnesium
(Mg2+
) ions expressed as mg/L of CaCO3, since soap
is precipitated mostly by these ions[18]
. Mean levels in
groundwater ranged from 13.90 mg/L at SGW9 to 25.15
mg/L at SGW10 (Table 1). Seasonal concentrations were
highest during late dry seasons (LDS) at SGW9, SGW10,
SGW11 and SGW2 (control station) but during dry sea-
son (DRS) at SGW12 (Figure 6). Average total hardness
value in study area was 21.88 mg/L (Table 3), and was
below SON, NESREA and WHO guidelines (Table 4).
Total hardness values of all samples were within standard
limits/guidelines and thus satisfactory. Also according to
some standard classifications[]19]
, the water samples were
classified to be soft, as their concentrations were all within
the range of 0 - 60 mg/L.
3.1.6 Chloride
Mean concentration ranged from 39.75-79.00 mg/L. Ex-
ception of SGW11, all study area samples had concentra-
tions lower than control (CSW2) value (Table 2). Chloride
levels in samples also increased from rainy to dry season,
exception of SGW10 and SGW12 whose concentrations,
Table 3. Seasonal levels of physico-chemical parameters and Heavy Metals in groundwater
Sample
Station
Sample
Season
pH EC (µS/cm)
TDS (mg/
L)
TH (mg/L) Alk (mg/L)
Cl-
(mg/L)
Cu
(mg/L)
Fe (mg/L) Zn (mg/L)
Mn (mg/
L)
Pb (mg/L)
RNS 6.80 190.00 105.00 10.00 10.60 26.00 0.08 0.44 0.001 0.09 0.001
SGW9 LRS 7.00 232.00 109.00 8.00 11.00 30.00 0.09 0.45 0.001 0.10 0.001
DRS 8.15 285.00 143.00 18.70 12.00 55.00 0.18 3.11 0.001 0.10 0.001
LDS 8.17 298.00 189.00 18.90 13.92 58.00 0.20 3.43 0.001 0.11 0.001
RNS 7.00 360.00 198.00 14.00 26.00 52.00 0.001 0.001 0.001 0.17 0.001
SGW10 LRS 7.80 382.00 201.00 15.00 26.40 56.00 0.001 0.001 0.001 0.21 0.001
DRS 8.72 578.00 289.00 31.90 30.60 58.60 0.30 3.11 0.41 0.59 0.42
LDS 7.16 582.00 292.00 39.70 22.10 58.40 0.24 3.92 0.41 0.51 0.43
RNS 6.90 220.00 121.00 12.00 19.00 38.00 0.06 0.001 0.001 0.49 0.001
SGW11 LRS 7.60 258.00 121.00 13.00 20.00 42.00 0.11 0.001 0.001 0.53 0.001
DRS 8.60 1 050.00 525.00 30.80 22.40 110.00 0.24 3.01 0.34 0.59 0.29
LDS 7.20 1 120.00 567.00 38.30 18.40 126.00 0.30 3.93 0.42 0.54 0.30
RNS 6.80 210.00 116.00 12.00 4.20 32.00 0.001 0.29 0.001 0.001 0.001
SGW12 LRS 7.50 225.00 110.00 18.00 4.40 33.00 0.001 0.001 0.001 0.001 0.001
DRS 8.43 483.00 241.00 44.00 27.00 55.00 0.27 2.75 0.42 0.23 0.42
LDS 7.09 312.00 172.00 25.80 18.00 39.00 0.27 3.66 1.06 0.21 0.41
AVER-
AGE
7.56 424.06 218.69 21.88 17.88 54.31 0.20 2.55 0.51 0.32 0.38
RNS 5.80 13.00 72.00 10.00 11.60 44.00 0.001 3.60 0.001 0.07 0.001
CGW2 LRS 6.10 15.00 75.00 16.00 12.50 45.00 0.001 3.96 0.001 0.06 0.001
(control) DRS 8.67 352.00 176.00 31.00 25.00 98.00 0.25 2.87 0.40 0.06 0.32
LDS 8.70 359.00 191.00 31.00 27.00 96.00 0.24 3.12 4.40 0.08 0.33
Note: RNS = Rainy Season; LRS = Late Rainy Season; DRS = Dry Season; LDS = Late Dry Season.
Table 4. Standard Guidelines for Drinking Water
Parameter USEPA[20]
SON[21]
NESREA[22]
WHO[23]
EU[24]
pH 6.5 - 9.5 6.5 - 8.5 6.5 - 9.2 6.5 - 9.5 NM
EC (µS/cm) NM 1,000.00 NG NG 250.00
TDS (mg/L) 500.00 500.00 1,500.00 NG NM
Chloride (mg/L) 250.00 250.00 600.00 250.00 250.00
TH (mg/L) NM 150.00 500.00 200 NM
Alkalinity (mg/L) NG NG NG NG NG
Cu (mg/L) 1.30 1.00 0.075 2.00 2.00
Zn (mg/L) 5.00 3.00 0.80 NG NM
Fe (mg/L) 0.30 0.30 1.00 NG 0.20
Mn (mg/L) 0.05 0.20 0.50 NG 0.05
Pb (mg/L) 0.015 0.01 0.075 0.01 0.010
Note: NG = No guidelines; NM = Not mentioned

17
like that of the control sample (CGW2), decreased during
the late dry season (LDS) (Figure 7). Average chloride
level of 54.31 mg/L obtained in the study area (Table 3)
was below referenced standard guidelines (Table 4). High
presence of chloride in water could be due to pesticides
from farms, continuous discharge of mine wastes, and ef-
fluents containing chloride salts from chloride rich rocks
in the area. However, the lower chloride concentrations
observed during the rainy reason could be due to dilution
of the water by rain water[7]
. High chloride content in
water causes eye and nose irritation, stomach discomfort,
increase in corrosive character of the water[12]
.
3.2 Heavy Metals in Groundwater
3.2.1 Copper
Mean copper concentrations in groundwater ranged from
0.14 mg/L at SGW9 (Ogwu spring well) to 0.27 mg/L at
both SGW10 and SGW12 (Table 2). SGW9 and SGW11
were lower in mean concentrations than that of control
(CGW2). Average level of Cu in study area was 0.20 mg/
L while seasonal concentrations were also higher in the
dry seasons than in the rainy seasons, and in the order of
RNSLRSDRSLDS (Table 3). All samples were with-
in the standard guidelines of USEPA, SON, WHO, and
EU[20][21][23][24]
but higher than that of NESREA[22]
(Figure
8).
3.2.2 Zinc
Mean concentrations of Zn ranged from 0.00 mg/L at
SGW9 (seasonal concentrations 0.001 mg/L) to 0.74
mg/L at SGW12 (Table 2). All stations had lower mean
concentrations than the control groundwater (CGW2) in
Uturu. Average Zn concentration in study area was 0.51
mg/L, and seasonal concentrations were higher in the dry
seasons than in the rainy seasons (Table 3). Zn concen-
trations were below USEPA, SON, and NESREA lim-
its[20][21][22]
(Figure 9). The percentage of zinc in the earth
crust is approximately 0.05 g/kg, and its major common
mineral is sphalerite (ZnS), which usually unites with
other sulfides[19]
, and could infiltrate underground water
resources.
3.2.3 Iron
Mean Fe concentration ranged from 1.86 mg/L at SGW9
to 3.52 mg/L at SGW10. SGW9 and SGW12 had lower
mean concentrations than the control sample (CGW2) at
Uturu (Table 2). Groundwater samples in the study area
were observed to be polluted with iron, as they all had
mean concentrations well above USEPA, SON, and NES-
REA limits[20][21][22]
(Figure 10). Average Fe concentration
in study area was 2.55 mg/L, while seasonal levels were
also higher in the dry seasons than in the rainy seasons,
in the order of RNSLRSDRSLDS (Table 3). Iron in
groundwater could result from natural sources such as
minerals from sediments and rocks; or from mining, in-
dustrial wastes, and corroding metals in the surrounding
soil[25]
3.2.4 Manganese
Groundwater samples in the study area had mean man-
ganese concentrations ranged of 0.10 mg/L at SGW9 to
0.54 mg/L at SGW11. All samples from the study area had
higher mean manganese concentrations than the control
(CGW2) sample (Table 2). Only SGW11 has higher Mn
concentration than NESREA recommended value of 0.50
mg/L (Figure 11). Average level of Mn in the study area
was 0.32 mg/L, while seasonal concentrations were also
higher in the dry seasons than in the rainy seasons (Table
3).
3.2.5 Lead
Lead mean concentrations ranged from 0.00 mg/L at
SGW11 (0.001 mg/L seasonal concentrations) to 0.42
mg/L at SGW10. However, control (CGW2) value
was higher than that of SGW9 and SGW11 (Table 2).
Average Pb concentration in study area was 0.38 mg/
L and seasonal levels higher in the dry seasons than in
the rainy seasons (Table 3). All samples also had higher
mean values than referenced standard limits of USEPA,
SON, NESREA, WHO, and EU[20][21][22][23][24]
(Figure 12),
exception of SGW9 (Ogwu Spring well). This indicated
a situation of lead pollution of the underground water
bodies at the affected stations in the study area, which
could be due to high concentrations of lead ore deposits
in the area[26]
. Water-soluble zinc in soils can contami-
nate groundwater[27]
through leaching from the soil to the
water body.
3.3 Correlations of Heavy Metals in Groundwater
There were positive correlations among the heavy metals.
However, strongest positive correlation was between Cu
and Pb (r = 0.921) while the least was between Cu and
Mn (r = 0.176) (Table 5). The positive correlations could
be an indication of the same source of heavy metals pollu-
tion [28]
, which could be natural sources including weath-
ering of rocks, the Pb-Zn mining activities in several parts
of the Ihetutu area, and other sundry point and non-point
sources such as leachates from domestic wastes dumps.

18
Table 5. Correlation of heavy metals in groundwater sam-
ples from Ihetutu hills
Cu Zn Fe Mn Pb
Cu 1
Zn 0.829069551 1
Fe 0.347291232 0.223578813 1
Mn 0.175777928 0.287296924 0.917268323 1
Pb 0.921198549 0.883311653 0.597831537 0.525299475 1
3.4 Analysis of Variance (ANOVA)
ANOVA was carried out on the means of the different
stations, using Microsoft Office Excel (2007), at a signif-
icance level, α = 0.05. The results showed no statistically
significant differences in means of the parameters among
sampling stations in study area, as p-values was higher
than the significance level (p = 0.757).
4. Conclusion
This research was undertaken to analyze heavy metals
contamination and quality of groundwater within Ihetutu
mining areas in Ishiagu. The study has revealed that the
quality of groundwater available in the area was poor,
though most of the results obtained were within standard
guidelines/limits of USEPA, SON, NESREA, WHO, and
the EU. Also, exception of SGW9 (Ogwu spring well),
mean levels of Pb, Cu, Fe, Zn and Mn in the study area
were higher than the control (pre-mining/background)
level; and were in the order of FeZnPbMnCu. This
indicated a case of quality deterioration of the groundwa-
ter available at these stations/locations when compared
to the control values obtained; and also confirmed that
groundwater resources in the study area have been ad-
versely impacted upon by leachates/discharges from the
mine wastes, tailings, surrounding rocks, and several oth-
er point and non-point anthropogenic sources including
domestic wastes and run-offs from farms. Seasonal levels
of most of the parameters analyzed including TDS, EC,
pH, total hardness, chloride, and the heavy metals were
also higher in the dry seasons than in the rainy seasons,
and in the order of RNSLRSDRSLDS. However, it is
recommended that adequate measures must be urgently
taken by the mining companies operating in the area to
ensure that wastes and other toxic substances generated
from their operations are not discharged into the ground-
water bodies which serve as the main sources of drinking
water to the people. The government must through its
regulatory agencies including NESREA urgently ensure
proper monitoring of the activities of mining companies
and other waste disposal processes in the area; and also
enforce compliance with laid down standards/regulations.
This will safeguard the groundwater resources in the area,
and consequently human lives that depend on it.
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20
REVIEW
Review of Groundwater Potentials and Groundwater Hydrochemistry
of Semi-arid Hadejia-Yobe Basin, North-eastern Nigeria
Saadu Umar Wali1*
Ibrahim Mustapha Dankani2
Sheikh Danjuma Abubakar2
Murtala
Abubakar Gada2
Abdulqadir Abubakar Usman1
Ibrahim Mohammad Shera1
Kabiru
Jega Umar3
1. Department of Geography, Federal University Birnin kebbi, P.M.B 1157. Kebbi State, Nigeria
2. Department of Geography, Usmanu Danfodiyo University Sokoto, P.M.B. 2346. Sokoto State, Nigeria
3. Department of Pure and Industrial Chemistry, Federal University Birnin kebbi, P.M.B 1157. Kebbi State, Nigeria
Article history
Received: 13 July 2020
Understanding the hydrochemical and hydrogeological physiognomies of
subsurface water in a semi-arid region is important for the effective man-
agement of water resources. This paper presents a thorough review of the
hydrogeology and hydrochemistry of the Hadejia-Yobe basin. The hydro-
chemical and hydrogeological configurations as reviewed indicated that the
Chad Formation is the prolific aquifer in the basin. Boreholes piercing the
Gundumi formation have a depth ranging from 20-85 meters. The hydro-
chemical composition of groundwater revealed water of excellent quality,
as all the studied parameters were found to have concentrations within
WHO and Nigeria’s standard for drinking water quality. However, further
studies are required for further evaluation of water quality index, heavy
metal pollution index, and irrigation water quality. Also, geochemical, and
stable isotope analysis is required for understanding the provenance of sa-
linity and hydrogeochemical controls on groundwater in the basin.
Keywords:
Hydrogeology
Sedimentary aquifers
Basement complex terrain
Physical parameters
Chemical parameters
Saadu Umar Wali,
Department of Geography, Federal University Birnin kebbi, P.M.B 1157. Kebbi State, Nigeria;
Email: saadu.umar@fubk.edu.ng
1. Introduction
The hydrochemical assessment of subsurface for local,
industrial, and agricultural uses required a valuation of the
hydrochemical and hydrogeologic configurations of the
subsurface aquifers [1]
. In a typical semi-arid region like
north-eastern Nigeria, groundwater is the most important
source of water supply for households, irrigation agricul-
ture, and industrial demands [2]
. The quality and availability
of subsurface water have been impacted by increased an-
thropological activities associated with urbanization, indus-
trialization, increased irrigated agriculture, and population
growth [3-6]
. Groundwater protection and conservation pro-
cedures have been largely ignored in mainstream practices
[2]
. Agriculture is the primary and major source of subsur-
face water pollution in arid and semi-arid areas [7,8]
. Results
indicated that pesticides, irrigation water quality, and nitro-
gen fertilizers as major sources of pollutants in aquifers [9]
.
In arid and semi-arid regions like the Hadejia-Yobe ba-
sin, salinization of groundwater is the major cause of the
decline of water quality impacting the sustainable use of
water resources. It limits the use of water for industrial,

21
domestic, and agricultural uses [10]
. The problem intensifies
in arid regions where the anthropological activities accel-
erate the deterioration of groundwater quality by a range of
issues which include: (a) subsurface movement of effluents
from irrigation fields; (b) upward flow of groundwater that
has infiltrated the aquifer during irrigation; (c) seepage of
highly effluent-rich surface flows concentrated in urban
and/or municipal effluents during inundation event(s); (d)
overexploitation of aquifer or recycling of wastewater; and
irrigation return flows from irrigated fields [10]
.
In drylands, the salinization, and anthropological activ-
ities are often followed by some natural processes such as
the dissolution of soluble salts and rock-water interactions
in the unsaturated zone which gradually salinizes ground-
water. All these aforementioned factors necessitate con-
tinued analysis and monitoring of groundwater resources
in arid environments for improved water resources man-
agement [10]
. Consequently, several studies were conduct-
ed to evaluate the physical and chemical composition of
groundwater in different parts of the world [9,11-23]
, results
indicated that groundwater is influenced by both anthro-
pological and lithological factors.
Groundwater analysis in some parts of the Hadejia-Yobe
basin showed major variations are correlated to natural and
anthropogenic processes [24]
. Evaluation of groundwater
chemistry using multivariate statistics by Garba, Ekanem
[25]
, inferred that the status of water quality in Hadejia is fit
for human consumption. Similarly, analysis of groundwa-
ter chemistry, dynamics, and storage in parts of Jigawa by
Hamidu, Falalu [26]
revealed water of low hardness and dis-
solved salts that are within the WHO and Nigerian standard
for drinking water quality. Evaluation of fluoride distribu-
tion, geogenic origin, and concentration in groundwater
in some parts of Yobe showed that the area had fluoride
concentrations slightly above WHO reference guidelines
[27]
. Appraisal of toxicity and trace elements concentrations
in Yobe revealed anthropological inputs [28]
. While there is
a significant reporting on the hydrochemistry of aquifers in
the Hadejia-Yobe basin, there is a need for reviewing the
extent of hydrogeological and hydrochemical analysis in
the basin. This is attempted in this study.
2. The Hadejia-Yobe Basin
2.1 Location and Climate
The Hadejia Yobe Basin (also known as Yobe-Jamaare
floodplain), is a trilateral basin, with its summit in
north-eastern Nigeria as depicted by Figure 1 [29-33]
. The
basin coincides roughly with the western Chad basin (un-
confined aquifer) groundwater area. It is underlain by both
the sedimentary formation and basement complex rocks.
The basin is drained in the southwest and northeast by
the tributaries of the River Komadugu Yobe, comprising
mainly Rivers Kano, Gaya, Hadejia, Katagum, Jamaare,
and Gama. These rivers link up at a different point to
form the drainage system of the Komadugu Yobe, flowing
towards the north-eastern summit of the triangular basin
[29-34]
. Together with the eastern Chad basin of Nigeria, it
covers the southwestern part of the Lake Chad.
The major town in the basin includes Kano, Hadejia,
Azare, Potiskum, and Katsina while Bauchi is just outside
the southern boundary. It is bounded to the north by the
Niger Republic. It is situated along with the latitude 10o
N
and has a very hot and dry climate (Figure 1). The annual
rainfall is comparatively low, and annual evaporation is
also very high, reaching up to 1500mm. The scenery is
wide-ranging, extending from the rocky hills and insel-
bergs of the basement complex rocks of the southwest, to
less protuberant, low lying dull rolling dunes of sedimen-
tary formations to the northeast, along Azare, Geidam,
and Gumel. A line of massive granitic mountains, which
perhaps indicate the contact between the two formations
marks the basement-sedimentary frontier.
Figure 1. Hadejia-Jamaare Floodplain [33]
2.2 Relief and Drainage
In terms of drainage, the Hadejia-Yobe-River System con-
trols the entire basin. The tributaries of this river system
rise from near western parts of the North-Central Plateau
(Kano, Katsina, and Jos plateau), with comparatively
higher precipitation than the rest of the province. The De-
limi River, with its headwaters on the Jos Plateau and the
River lgi flowing from the Mingi Hills, the River Kano
from Liruwe Hills, and the Hadejia River from western
Kano, all donates to Hadejia-Yobe-River System [31,33,34]
.
The Hadejia-Yobe or Komadugu-Yobe, as it is sometimes
described, collects water from entire tributaries before
flowing to the Lake Chad. Most of the tributaries of the
Haqdejia-Yobe River System are mechanically measured.
The river flow from the area of high precipitation in the
southwestern axis to lesser precipitation in the direction of

22
the lake chad. The intensity of rainfall displays a progres-
sive fall from the southwest to the northeast. The average
annual estimate of rainy days varies from around eighty
days in the southwest to less than the forty days near Lake
Chad. The temperatures are generally high and vary from
20o
C to 28o
C from southwest to northeast. The river Hade-
jia-Yobe is one of the most exploited and monitored river
system in Nigeria [30-32,35,36]
.
Many gauging stations are set along its sequence, from
its source area, in the southwest, to Gashua, near Lake
Chad, where the river empties its headwaters. The river
system is affluent, in the upland basement complex ar-
eas. The river also influent in the Lower Hadejia-Gashua
sedimentary dispersal or wetland area, where the river
valley is exposed to seasonal run-offs and flooding. Con-
sequently, losing a substantial volume of its flow to the
riparian alluvial groundwater aquifers. Owing to the high
evaporation rates exceeding 90%, only about 10% or less
of this flow is accessible by the river as it squalls through
its course of the lake. This flow is induced to recharge into
the underlying aquifers of this part of the Chad basin [37-
40]
. Later, with the increase in evapotranspiration, down-
stream, and the losses into the underlying groundwater
aquifers, as the river feasts out and winds its sequence
towards the Lake Chad, the flow drops considerably.
The river flow, between 1964 to 1965, along the Ha-
dejia - Yobe dropped from 5.6x10m in the upland area, to
0.63x10m in Yau, after flowing through the wetland areas,
51.5 km to the lake. The situation is believed to have wors-
ened. There is also a rise in the groundwater input to the
river flow downstream, 35% at Challawa, and over 50%
towards Wudil. Generally, the river system contributes very
little to the water of the current Lake Chad, which added to
the drying of the lake. Most of its water is lost, seemingly in
the wetland swamps and pools between Hadejia and Geid-
am. The Hadejia-Yobe River System with its large alluvial
expanses is seasonal and only starts flowing around June to
July, after the onset of the rainy season.
2.3 Geological setting
The geology of the Yobe-Hadejia basin is comprised of
the basement complex and sedimentary formations [38,41,42]
.
The Chad Formation is the newest in the Hadejia-Yobe
Basin. A detailed stratigraphical description of the Chad
Formation is not common literature compared to the other
older formations in the basin. The sedimentology of the
formation, which segregates the deposits into three mem-
bers based on color and claystone/sandstone sections were
described in detail by [43]
. The sedimentation of the Chad
Formation has been an incessant process that began in the
Late Miocene to the present, whereby river and aeolian
sand and clay elements are still being added.
Some of the detailed stratigraphies of the Chad Forma-
tion indicated that the lithostratigraphy of Chad Formation
encountered in Korowanga borehole, Dogara borehole
and outcrop section at Abakire, represent numerous het-
erolithic sandstone and claystone in varying proportions.
These sands range from silty, medium, and coarse-grained
in size. In the Tuma well, for instance, the Chad Forma-
tion is characterized by light grey colossal claystone, mi-
nor sand particles, and some occasional pebbly horizons,
and indicating some ferruginization in the deposits [43]
.
Eight lithofacies components were defined based on their
physiognomies such as structure, facies type, grain size,
boniness, sorting, color, and compaction [43]
. The account
of the faces components (summarized in three parts) is
shown in Figure 2.
2.3.1 The Lithofacies Part 1-3
Part 1 comprises greyish sandy claystone. These facies
component range from 50 to 70 meters and also is en-
countered between 305 m and 345 meters below the sur-
face. It is highly rich in organic matter with insignificant
sand particles ranging between silt and minor pebbles.
The lithofacies is also accompanied by lignite. The lower
interlude has filthy claystone displaying roughening-up-
ward sequences and sorting from clayey granite through
to sandy claystone, and weakly-sorted sandstone at the
uppermost [43]
. Part 2 is comprised of micaceous claystone
which occurred only in the interval of 70-90 meters. The
carbonaceous clay is mainly related to mica flecks partic-
ularly muscovite with negligible silt particles. The exis-
tence of muscovite proposes a felsic parent rock source
and lengthy-distance transference. Its high content in or-
ganic matter signposts a lacustrine depositional scenery[43]
.
Part 3 is comprising mainly of lithified claystone. The
lithofacies occurred at the interval of 90 to 195 meters, also
exist as reedy-bedded interpolated deposits at the interme-
diate interlude of the entire unit. The claystone is sturdily
lithified and marginally ferruginized. It is comprised of
slight mica flecks with no sign of biological opulence. Near
the lower part of this interlude, the claystone contrasts from
bright to murky grey, signifying cumulative organic abun-
dance and accumulation in a reducing condition [43]
.
2.3.2 The Kerrikerri Formation
This geologic formation is characterized by horizon-
tal-laying to moderately plummeting basal conglomerate,
grit, sandstone, siltstone, and clay which unconformably
rests above the Maastrichtian Fika Shale and Gombe
Sandstone [44,45]
. Five stratigraphic units (including the
type section at Kadi) and lithology were reviewed. The

23
formation attained a depth of about 200 meters at Duku
[44,45]
. The substantial mineral suite is comprised of rutile,
zircon, kyanite, staurolite, limonite, tremolite, sillimanite,
pyroxene, hornblende, and tourmaline, which are sugges-
tive of origin from the adjoining basement complex and
previous alluvial rocks [44,45]
.
Figure 2. Lithofacies type and depositional/precipitated
palaeoenvironment of the Chad Formation [43]
The occasional basal deposits, well-sedimented silt-
stones and the occurrence of contraction fissures, clay-rein-
forced pebbles, local channel sandstones, and tinny vistas
of coal and carbonaceous clay propose a distinctive deltaic,
peripheral and deltaic lacustrine depositional environment.
The occurrence of the pollens Monocolpites marginatus
and Spinizonocolpites baculatus confirmed a Paleocene age
for the formation. The explanation of the superficial and
subsurface information is constant with an irregular graben
edifice of the Kerrikerri basin. The western boundary of the
basin was fault-controlled and active during the deposition
of sediments through the Early Tertiary [44]
. Borehole data
showed that the intermittent nature of the Paleocene age
Kerri-Kerri Formation as an aquifer in Darazo [45]
.
The series, parasequences, and their borders are be-
lieved to have been formed in reaction to cycles of vir-
tual fall and increase of sea level. Within strings, several
systems bands can be notable and developed all through
an explicit component of a full cycle of virtual sea-level
transformation [46]
. The source of this layered congrega-
tions was the consequence of the interface between the
ratios of variation of basin settling, residue contribution,
and eustasy[46]
. The stratigraphic sequences recognized are
truncated stand system bands, transgressive system bands,
high stand systems bands, and a sequence boundary. The
base of the Gombe Sandstone was not encountered in the
Fika area perhaps owing to lack of outcrops. The uncon-
formity between these two formations (Gombe Sandstone
and Kerri-Kerri Formation), shows a most important top-
most series edge [46]
.
Based on previous investigations, the Gombe Formation
was dated as Late Maastrichtian in age, whereas the Ker-
ri-Kerri Formation age data is not available, nonetheless,
Palaeocene pollens were traced [46]
. The formation of pro-
gression frontiers can be credited to tectonics. However,
there is some indication for Santonian-Campanian folding
simultaneously with the existence of a sharp unconformity
[46]
. The major stratigraphic sequence of the Kerrikerri For-
mation is presented in Figure 3. It is dominated by thick
limestone and sandstone which are Palaeocene in age. The
stratigraphic sequence occurred under erratic conditions
with each sediment correspond to one full cycle of trans-
gression and regression [47]
. The Kerri-Kerri Formation
superimposed a slight area in the southeast, toward Azare.
The formation, containing a succession of grits sandstones
and clays, lies against the crystalline rock in this area. It
is usually not easy to differentiate the formation from the
younger superimposing Chad Formation, as both seem to
be in contact and present the same lithological physiogno-
mies. The formation is up to 200 meters thick in its core
area of existence in the upper Benue and thins out to the
northwest near Azare in the Hadejia-Yobe basin.
0
5
10
15
20
25
30
35
40
45
50
60
70
80
90
Meters Lithology
Paleocene
Continental
Age
Depositional
Environment
Explanation
Sandstone
Limestone
Figure 3. Stratigraphic section of Kerrikerri Formation [47]
2.3.3 The Gundumi Formation
The Gundumi Formation is characterized by the river and
lacustrine deposits, which include moderately grainier
materials (Figure 4). The formation is also characterized
by intermittent lenses of quartz and feldspar pebble grav-

24
el, which are interbedded with the richer clay and clayey
sand [48]
. However, the formation contained a great deal
of melded clay. The sandy beds decline, and clay beds
upsurge with depth down to the contact with the pre-Cre-
taceous basement rocks. Near the base of the Gundumi
Formation, a conglomerate of smoothed quartz stones up
0.0381 meters in diameter occurred in an outlier [48]
. The
sand and gravel beds are comprised of sharp to sub-an-
gular quartz particles, but several beds are abundant in
feldspathic and micaceous substance and rock fragments.
Colors in the Gundumi varied widely. Brown, red, pink,
yellow, white, and even purple are regular, and in some
clay layers, some of these colors may exist in spotted
forms. The sedimentary formations lie above the Precam-
brian basement complex formation. The formation ranged
in age from Palaeozoic to Quaternary. It is assumed to be
a tectonic cross point between the northeast and southwest
trending the “Tibesti-Cameroun Trough” and a north-
west-trending Aïr-Chad Trough”. It has been estimated
that over 3600 m sediments have been deposited[49]
.
Figure 4. Stratigraphic section of Gundumi Formation
The outcrops of the basement complex formation in the
east, southeast, southwest, and the north of the basin are
noticeable. The configuration below the sediments across
the lake was similar to the graben and horst zone [49]
. The
hydrogeology and hydrochemistry of the basement com-
plex section of Chad formation are characteristic of Nige-
ria’s basement complex terrain. There are some published
data on Nigeria’s basement complex [50-60]
. Results indicat-
ed that the groundwater evolution hangs on reactivity and
pH. The hydrochemistry of aquifers is a direct signal of
the catchment geology [58]
.
3. The Sedimentary Aquifers
Hydrogeologically, the Chad Formation is a profound
aquifer in the Hadejia-Yobe basin [26,61,62]
. The aquifer
comprises of a series of clays, sandy clays, and silt, in
which bands and lenses of silt and grit appear at several
spots. The coarse sand and gravel are well developed. In
this area, the Chad Formation superimposes the Kerrikerri
Formation which lies on a more stable basement rock [37]
.
The Chad Formation does not exceed 165 meters in thick-
ness and thins out erratically, nonetheless gently towards
the southern and western borders where it seems to over-
step the basement complex terrain [37,63]
. At Gumel (Jigawa
State), the sediment is reported to attain a thickness of
132 meters, 115 meters at Nguru (Yobe State), 132 meters
at Marguba, and 76 meters at Kunshe. In this province,
groundwater is found an underwater table or sub artesian
conditions depending on the existing hydrogeological
condition (Figure 5).
Figure 5. Lithologic section of boreholes penetrating
Chad Formation.
Borehole yields ranged from 3.3 to 5 lits/sec on aver-
age. However, transmissivity ranged from 6.87m2
/day and
429.4m2
/day, with a mean value of 65.7m2
/day [37]
. The
parting of the Chad Formation into the Upper, Middle,
and Lower aquifers cannot be defined in this part of the
basin. Alternatively, it shows recurrences of clays, sand,
and silts, the sandy layers intersecting the aquiferous
layers. The upper 20-30 meters of the grainy sands of the
Hadejia-Yobe basin, store a lot of water as bank storage,
directly recharged from the river flows [37]
. A lithological
unit along the river outline, based on available borehole
records, displays a top 0-10 meters silty fine-grained
sands, underlain by a thick sequence of coarse sand and
gravel, with two main interbedded clay/shale deposits.
Some of the borehole drilled in this area gave the follow-
ing lithological sections and output [37,64]
.

Advances in Geological and Geotechnical Engineering Research | Vol.2, Iss.2 April 2020

Advances in Geological and Geotechnical Engineering Research | Vol.2, Iss.2 April 2020

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