Detection and Mapping of Hydrocarbons Microseepages by using
Principal Component Analysis on TM bands near Thano Bola khan
in Laki formation
Abdul Waheed Kandhro1, Dr Qamar ul Islam1 and Aijaz Ahmed Bhutta2
(ab.waheedk@ist.edu.pk)
1
Department of Electrical Engineering, Institute of Space Technology, Islamabad, Pakistan
2
Pakistan Space & Upper Atmosphere Research Commission, Islamabad
Abstract:
Hydrocarbons escaping from reservoirs of subsurface geology can be caused of various
environmental changes. In this research, hydrocarbon seepages induced anomalies in Thano Bola
khan Laki formation is mapped. TM Landsat band ratio of 7/5 and 3/1 are interesting to detect
ferric minerals, clay minerals, ferrous iron minerals bearing sedimentary rocks respectively,
which are applied to distinguish the altered and rocks. The results express that Landsat images
can expose effectively mineralogical alteration induced by hydrocarbons seepage like red
bleached bed and carbonates. Which encourage an opportunity for petroleum exploration
structure that provide indirect indication for the presence of hydrocarbon system at strength. The
occurrence of seepages is the first element of a hydrocarbon system and reduces exploration cost.
Spectral characteristics of mineral assemblage near Thano Bola khan in Laki formation of
khirthar range is examined in this study through image enhanced techniques of Principal
Components Analysis (PCA). The reflectance and absorption features of diverse ferric minerals,
clay minerals ferrous iron minerals and calcite on various TM bands of satellite are calculated by
using PCA algorithm. it is revealed that Spectral reflectance curve signify that the clay minerals
and carbonates have enormous reflection in band- 5 and absorption capacity in TM band- 7 . The
approach contributes significantly towards the exploration and mapping of hydrocarbons in the
study area.
Introduction:
The reflection and emittance behavior of every material is different in the electromagnetic
spectrum system. Which can be employed for identification of those materials on the earth’s
surface either from space or air, Sensors are designed especially for capturing this behavior. The
captured data is then studied for interpreting different materials using remote sensing software’s
like ERDAS imagine, Arc Maps etc.
Hydrocarbon seepages are categorized basically in two categories, which are, Macroseeps and
Microseeps. the observable occurrence of gas and oil seeping out at the earth’s surface boundary
of the area where hydrocarbon seep dynamically in large absorption and containing high
molecular weight and low molecular weight hydrocarbons that may leave traces on the surface
material are referred to as Macroseeps, whereas, Microseeps are the non-active seeps and usually
contain low molecular weight concentration.
Principal Components Analysis (PCA) is a data reduction method which can potentially be
applied for image enhancement purpose. The information received from dissimilar bands of
Landsat is compress into a few principal components which represent the inconsistency in
various image bands. TM Landsat band ratio of 7/5 and 3/1 are interesting to detect ferric
minerals, clay minerals ferrous iron minerals bearing sedimentary rocks respectively The
location is situated in southern Indus Basin with a latitude of 27.67 (27° 40' 0 N) and a longitude
of 67.28 (67° 16' 60 E) 371 kilometers away in south west (226°) of the approximate centre of
Pakistan and 875 kilometers south west (222°) of the capital Islamabad. Generally the area is
composed of detrital and non-detrital rocks unequal in age from Early Eocene to Pleistocene.
Hydrocarbon microseepages are dominantly composed of propane (C3H8), butane (C4H10)
methane (CH4), ethane (C2H6), and pentane (C5H12). These hydrocarbons can strongly
interrelate with the surface chemistry and generate sufficient variety of mineralogical, botanical,
chemical, physical, and microbiological alterations at deep.
A spectral reflectance curve signifies that the clay minerals and carbonates have strongest
reflection in band5 and enormous absorptions capacity in Band7. Whereas Iron oxide
demonstrate low reflectance in band 1and high reflectance values in band -3 and higher
absorption value in band-4. Consequently, iron oxide might be improved in between 400 nm to
600 nm. Likewise ferrous iron illustrates intense absorption in band 5 and high reflectance values
in band 1 and 3.
Fig (1) : Map of Study Area in Red Color
Discussion:
Clay minerals Spectral reflectance curves reveal the maximum reflection in band 5 and strongest
absorption in band 7. Generally band 5 and 7 have high load of PC analysis with inverse signs.
Iron oxide minerals show high reflected value in TM band- 3, lower value into band 1. Whereas,
higher absorptions into TM band- 4. So iron oxide can be improved between 400nm ~600nm.
Correspondingly, ferrous minerals shows absorption in band 4 high reflectance value in band 1
and 3 (table -1). Clay minerals and carbonates have reflection in band 5 and absorption feature in
band 7.
The reflectance characteristics of various ferrous iron and ferric minerals, calcite and clay
minerals are given in (Tab-1). Ferric iron (in hematite) exhibits its intense absorption
characteristics at 0.56 µm and strongest reflectance at wavelengths 0.7-1.0 µm. on the contrary,
the ferrous iron is recognizable minerals like magnetite and pyrite which has lower reflectance.
while transparent minerals like siderite has broad shallow reflectance at 1.0-1.1 µm level .Visible
and NIR region of the bands (0.4-1.3 µm) are characterized by wide spectral absorptions feature
like yellowish ferrous iron reflections feature at 0.43~0.55 µm and absorptions feature at near to
1.0 µm, than increase reflectance spectrum of the bands in between wavelength of 1~2 µm.
Absorption characteristics at 0.850 and reflections at 1.20 show conversion of hematite.
However Kaolinite minerals display very tough absorptions features centered at 2.185-2.25 µm
(Yang et al., 2000). 2.235 to 2.285 μm. The spectral gap is the maximum reflectance values of
Kaolinite mineral as shown in below table (1).
Mineral
Band1
(0.450.52)
Reflected
Reflected
Band3
(0.520.60)
Reflected
Reflected
Band4
(0.760.90)
Absorbed
Absorbed
Band5
Band7
(1.55(2.081.75)
2.35)
Reflected Absorbed
Ferrous iron
Red bed
Clay minerals and
Reflected Absorbed
carbonates
Table (1) Spectral features of hydrocarbons on TM band ratio analysis
Mapping of Hydrocarbons on TM Bands
The Eigen vector matrix is applied to estimate PCA for every image. The principal components
that contain targeted (mineral) spectral information are identified and examined. An iron oxide
mineral gives low value in TM band-1 and high reflectance value in TM band-3. The principal
components in which the variation of reflectance is larger (Table 2). In PC-3 these band have
affirmative Eigen values that are not useful for separating the bands 3 and 1. In PC-2 the
contradictory Eigen values signs in bands 3 and 1 which build the bands divisible. The larger
reflectance dissimilarity on 0.411 as compared to PC1 viewing larger value of absorptions, So
PC2 is chosen for mapping and detection of iron oxide. In PC2 the negated brightest pixel is
liable for iron-oxide minerals and the darkest pixel is liable for iron oxide minerals.
PCA
I
II
III
IV
Band-1
Band-3
Band-5
Band-7
Eigen value
Variance
0.1699
0.2787
0.8070
0.4924
-0.4946
-0.6897
0.4790
-0.2243
0.4898
0.1385
0.3334
-0.7937
0.6977
-0.6540
-0.0909
0.2784
352.64
13.04
3.80
1.66
95.10%
3.52 %
1.02%
0.44%
Table (2) Mapping of Hydrocarbons on TM Bands
Fig(2) Mapping of Hydrocarbons on TM Bands
Clay Mineral Alteration Mapping
The spectral signatures of the clay minerals have shown higher reflectance significance in TM
band 5 and lower reflectance in TM band 7. The analysis and the results of required principal
components in which the difference of reflectance is larger are tabulated in Table (3). Eigen
values of bands 7 and 5 hypothetically has inverse signs for imitation in the spectral difference.
To separate the principal components of band 7 & 5 containing contradictory assistance by way
of Eigen values. In PC 2, band 7 has positive Eigen value and band 5 has negative value . The
brightest pixels are liable for hydroxyl minerals in PC4 and in case it is not true than the pixels
which are darkest are hydroxyl minerals.
PCA
I
II
III
Band3
Band5
Band7
Eigen
values
0.280
-0.790
0.545
342.607
0.819
0.492
-0.292
10.379
0.499
-0.364
0.785
3.056
Table (3) Alteration of Clay Mineral Mapping
Variance
96.212%
2.913%
0.851%
Fig (3) Clay Mineral Mapping in study area
Ferrous Mineral Mapping
The reflectance curves of ferrous minerals have strong reflections in TM band-5 and sharp
absorptions features in TM band- 4. These bands have huge loadings throughout PC analysis
with opposed signs. Ferrous minerals will be revealed darker in concluding image. The ferrous
minerals shown in bright pixels, the opposite PC of this range is obtained through PC3 as
tabulated in table (4).
PCA
I
II
III
Band3
Band4
Band5
Eigen Variance
value
0.296
-0.536
-0.791 289.611 92.701%
0.337
0.834
-0.439 15.189
4.902%
0.894
0.137
0.428
7.387
2.400%
Table (4) Ferrous Mineral Mapping
Fig(4) Ferrous Mineral Mapping by PCA
PCA Colour composites
Various groups of color composite were development on PCA images to identify different
inconsistent minerals gatherings. Images of False color composite were rectified for visual
analysis. The highlighted region is fully occupied with anomalous iron oxide, ferrous, and clay
(Kaolinite). The most excellent PCs established in image are 345-PC 2 (B), 357-PC 2 (G), 1357PC 2 (R) and the color composites formed by using these components are easy to understand as
compared to the grey scale PCs images. PC in fig (5) is show in blue, green and red channel.
Dark to Medium reddish area is
controlled by iron oxide minerals. Green color displays clay
minerals and medium blue to purple blue zones mark ferrous minerals. Whitish pixels in fig (6)
demonstrate vastly distorted region, reddish brown to rust color shows alteration of clay
minerals. Whitish blue pixels represent ferrous containing sites and bluish green zones are iron
oxide dominated area.
Fig (5): PCA color composite of Ferrous, clay and iron oxide
Fig (6) Principal Components showing spectral hydrocarbons anomalies
Conclusion
The study area is investigated on the basis of formations and analysis of spectral anomalies. The
geology of the study area is commonly strong warped by faults and fractures. These faults have
made possible way for hydrocarbon seeps and demonstrate a greater possibility in change of
rocks anomalies. In this study enormous anomalies were revealed in the area for carbonates,
clays and sandstones of Eocene and Pleistocene period. The surrounding area of Thano bola
khan is considered as strong anomalies regions as compared to other part of Laki formation for
ferrous minerals.
Acknowledgement:
The authors are grateful to Pakistan national Space agency SUPARCO, management team for
provision of satellite data of study area.
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