Identification of hydrocarbon micro-seeps based on mineral alteration in a part of Cauvery Basin, South India using Hyperion Data

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1 Indian Journal of Geo-Marine Sciences Vol.45(9), September 2016, pp Identification of hydrocarbon micro-seeps based on mineral alteration in a part of Cauvery Basin, South India using Hyperion Data S. Prabaharan, T. Subramani * Department of Mining Engineering, CEG, Anna University, Chennai , India *[ geosubramani@annauniv.edu; geosubramani@yahoo.com] Received 27 January 2014; revised 12 March 2014 Spectral analysis methods were applied for identification of altered minerals by matching the unknown spectra of the purest pixels with the pre-defined (Library) spectra. Three methods, namely Spectral Feature Fitting (SFF), Spectral Angle Mapper (SAM) and Binary Encoding (BE) were used to produce a score between 0 and 1, where the value of 1 equals a perfect match showing the exact mineral type. Four possible altered minerals namely alunite, kaolinite, calcite and siderite were identified based on different scores related to their abundance in the soils. Spectral Feature Fitting (SFF) technique was also used to classify the minerals in the satellite imageries. Based on the spectral analysis and mineral alteration characteristics, the possible hydrocarbon micro seep zones were identified in the Nagapattinam sub-basin of Cauvery Basin, Tamilnadu, India. [Keywords: Hyperion data, Spectral Analysis, Mineral Alteration, Hydrocarbon Seepage, Cauvery Basin, South India] Introduction Hyperspectral imaging in remote sensing is a major breakthrough which has opened the avenues of research in various fields like mineral mapping for oil exploration, mining, hydrology and many more due to its ample spectral information contained in hundreds of co-registered bands. 1, 2 Multispectral images can reveal the mineralogical alterations induced by hydrocarbon seepages such as bleached red bed and secondary carbonates, which represent an open window to petroleum system and provide indirect evidence for the presence of hydrocarbon at depth. [2] Therefore, the presence of seepages documents the first element of the hydrocarbon and reduces exploration risk. Spectral mapping technique namely, Spectral Feature Fitting (SFF) is applied by many researchers in remote sensing studies. 1 The algorithms are implemented based on the comparison of a pixel spectrum with the spectra of known pure materials. Multispectral remote sensing can be used to detect changes in lithology, while hyperspectral remote sensing can potentially be used to identify minerals and differentiate alteration products. Some of the mineralogic changes associated with oil and gas seepages have been identified in the remotely sensed hyperspectral imageries. Bleaching and iron loss in sandstones caused by hydrocarbon seeps have been mapped along the crests of anticlines. 2 Seeps may alter gypsum to carbonate and also, through the reaction of hydrocarbons with carbon dioxide, produce secondary carbonate or delta carbonate. [3] Another potential indicator of hydrocarbons is enrichment of kaolinite due to the alteration and depletion of other clays 3-6. Finally, certain minerals found predominantly in altered areas, including Siderite and Alunite 7, 8, have been used to identify subsurface petroleum reservoirs. Interaction between electromagnetic radiation and the atoms and molecules that make up the rock or minerals produces vibrational-rotational or electronic processes that create spectral features. Reflectance spectra of minerals and rocks in the visible to shortwave infrared wavelength ( µm) region of the Electro magnetic spectrum (EMS) are characterized by absorption features, caused primarily by electronic transitions and vibrational transitions 9. These diagnostic spectral features can provide a way to detect or identify minerals to distinguish lithologic units and to determine wall rock alteration products [10] from the remotely sensed data. The purpose of present study is to employ spectral mapping techniques on the Hyperion data to discriminate

2 S. PRABAHARAN & T. SUBRAMANI: IDENTIFICATION OF HYDROCARBON MICRO-SEEPS 1139 possible mineral alteration zones due to oil micro seeps in a part of Cauvery Basin, Tamilnadu, India (Fig. 1). Spatial distribution of the important altered minerals such as Kaolinite, Calcite, Siderite and Alunite is also focused in this study. Materials and Methods The description of stratigraphy of the Cauvery Basin is attempted by various researchers The surface exposures of sedimentary rocks of Cauvery Basin were mapped and correlated with Cretaceous formations of England and France because of rich 11 abundance of ammonite fauna. Sedimentary formations of cretaceous and palaceocene rocks are exposed in many places in this region. The Cuddalore sandstone is well known by its identity. The sedimentary section contains a number of transgressive-regressive episodes. The lithology varies from conglomerate, sandstone and shale to limestone and dolomites. Lithostratigraphic classification proposed from the generalized stratigraphy of Cauvery Basin based on subsurface well data is given in the Fig The maximum transgressive pulses are seen in mid- Albian, late Albian, middle to late Cenomanian, earlymid-turonian, late Coniacian-Santonian, late Companion and early-late Maastrichtian times. [14] This resulted in finer clastics being deposited all over the regional caprock facies. Interspersed regressive events marked the deposition of reservoir facies of mainly siliciclastic rocks with pronounced unconformities at the top of Pre-Albian, Albian, earlymid-turonian, early-santonian and early- Maastrichtian times. Effective source rock facies remained in the synrift sequence I and II (mostly early Cretaceous) during which anomix conditions prevailed in the basin for better preservation of the hydrocarbon source. 15 The Hyperion data used in this study has been pre-georeferenced to geographical latitude and longitude with WGS-84 datum. The Level 1 radiometric product has a total of 242 bands but only 198 bands are calibrated. Because of an overlap between the VNIR and SWIR focal planes, there are only 196 unique channels. Calibrated channels are 8-57 for the VNIR, and for the SWIR. The reason for not calibrating all 242 channels is mainly due to the detectors' low responsively. The bands that Fig. 1- Location map of the study area Fig. 2- Stratigraphy of the Cauvery Basin

3 1140 INDIAN J. MAR. SCI., VOL. 45, NO. 9 SEPTEMBER 2016 are not calibrated are set to zero in those channels. There are 220 unique spectral channels collected with a complete spectrum covering from nm. 16, 17 A standard images of 8 seconds covering approximately 42 kilometers in the along-track direction, has been collected. The digital values of the Level 1 product are 16-bit radiances and are stored as a 16-bit signed integer. The SWIR bands have a scaling factor of 80 and the VNIR bands have a scaling factor of 40. [18-21] VNIR L = Digital Number / 40 SWIR L = Digital Number / 80. Hyperion data falling in the Cauvery Basin are (i) EO1H pz.Tif Target Path 142, and Target Row 53 (ii) EO1H ky.Tif Target Path 142, and Target Row 52 The VNIR and SWIR data were then resampled so that all 242 bands have the same pixel size. Atmospheric correction was carried out separately for four scenes before mosaicing. Various techniques adopted to process and enhance the data have been presented in the methodology (Fig. 3). Results and Discussion FLAASH is a first atmospheric correction modeling tool for retrieving spectral reflectance from hyperspectral and multispectral radiance images. With FLAASH, it is possible to accurately compensate the atmospheric effects. FLAASH corrects wavelengths in the visible through near-infrared and shortwave infrared regions, up to (2.5µm). Unlike many other atmospheric correction programs that interpolate radiation transfer properties from a pre-calculated database of modeling results, FLAASH incorporates 22, 23 the MODTRAN 4 radiation transfer code. The first step in pre-processing is to convert the image radiance data to apparent reflectance to facilitate comparison with the library reflectance spectra. This process normalizes for solar illumination and suppresses the effect of the atmosphere, including spectral absorption and scattering by diffuse gases and particles. Each image was processed with FLAASH 22, 23, an add-on program for ENVI that uses MODTRAN radiation transfer code and the image spectra themselves to estimate the spectral reflectance conversion factors. To begin, a scale factor was required for the input radiance data. The Hyperion metadata provided this information in an ASCII radiance scale factor file. Hyperion data (VNIR-SWIR) Atmospheric Correction (FLAASH & IARR) Spectral Data Reduction (MNF) ma Spatial Data Reduction (PPI) Visualization & Identification (n-d) (ID) Spectral Mapping Techniques SFF GIS integration of Possible Zones of Alteration due to Hydrocarbon Seepage Hydrocarbon Prospective Zones Fig. 3- Flowchart showing methodology

4 S. PRABAHARAN & T. SUBRAMANI: IDENTIFICATION OF HYDROCARBON MICRO-SEEPS 1141 The individual image navigation files provided the image and sensor information for each image. Because no local radiosonde data were available, the tropical (T) model atmosphere was chosen with default value (4.11) in the water column multiplier field. The navigation file listed the weather condition at the time of flight as clear, so the scene visibility was set to the default value of (40 km). Aerosol model was performed as rural by keeping retrieval as none. Once the radiance was converted to reflectance, Internal Average Relative Reflectance (IARR) calibration tool was then applied in terms of obtaining [22, 23] the optimal atomospheric correction technique. The IARR is used to normalize images to a scene average spectrum. It is found to be effective for reducing image data to relative reflectance in an area where no ground measurements exist and little is known about the scene. It has proven to work best in arid areas with little or no vegetation. [28] The algorithm is designed to calculate an average spectrum from the entire scene and use it as a reference spectrum, which is then divided into the spectrum at each pixel of the image. As the method is found to be the most useful and accurate (similar to the resampled library) one, minerals were mapped using the three following spectral matching methods 22 (i) Minimum Noise Fraction (MNF) Transformation, (ii) Pixel Purity Index (PPI) and (iii) n-dimensional visualization. The minimum noise fraction (MNF) transformation is used to determine the inherent dimensionality of image data, to segregate noise in the data, and to reduce the computational requirements for subsequent processing. 24 Inherent dimensionality of the data is determined by examination of the Eigen values and associated Eigen images. Data base is divided into two parts: one associated with large Eigen values and coherent Eigen images, and a second with near-unity Eigen values and noise dominated images. By using only the coherent portions in subsequent processing, the noise is separated from the data, thus spectral processing results got improved. In a common practice, MNF components with Eigen values less than 1 are usually excluded from the data as noise in order to improve the subsequent spectral processing results. 7 The MNF procedure was applied on the IARR data, since the Eigen values of all MNF Eigen images of the data were greater than 1. Hence all the 232 bands were retained for subsequent data processing. The color composite MNF of bands 1, 2 and 3 is presented in the Fig. 4. Fig. 4- Minimum Noise Fraction (MNF) Transformation

5 1142 INDIAN J. MAR. SCI., VOL. 45, NO. 9 SEPTEMBER 2016 Pixel Purity Index (PPI) is a means to determine automatically the relative purity of the pixels from the higher order MNF Eigen images 24, 1. It is to find out the pure pixels in multispectral and hyperspectral data. PPI is computed by repeatedly mixing end members and repeatedly projecting n-d scatter plots on a random unit vector. The PPI is typically run on an MNF transform result, excluding noise bands. [22] The extreme pixels in each projection are recorded and the total number of times each pixel is marked as extreme is noted. A pixel purity image is thus created in which the digital number (DN) of each pixel corresponds to the number of times that pixel was recorded as extreme. To select the most pure pixels, a projection of the scatter plot and the 23, 24 threshold factor of 3.5 were applied on the data. Pixel Purity Index plot and pixel value image are illustrated in the Fig. 5. Density slice thresholds were used to determine pixels with high DN or pure pixels. These values were also applied to compute the region of interest (ROI) and then, used for n-dimensional visualization. producing 4 pure spectral signatures, which were extracted and plotted in an n-d visualizer plot 23, 24 representing the selected end-members. Spectral analyses and consequently targeted mineral identification could be done by matching the unknown spectra extracted from the 3-D visual with pre-defined (Library) spectra and providing scores with respect to the library spectra. 22 Three weighting methods, i.e. spectral feature fitting (SFF), spectral angle mapper (SAM) and / or binary encoding (BE) were used to identify mineral type by producing a score between 0 and 1, where 1 equals a perfect match. As it is known, some minerals are similar in one wavelength range and yet very different in another. For the best results, a wavelength range that contains the diagnostic absorption features was used to distinguish among the minerals. 22 The output of the spectral analysis is a ranked score or weighted score for each of the materials in the input spectral library as shown in Table.1. Table 1 Spectral Analysis Output Indicating Ranked /Weighted Score of Minerals in the Input Spectral Library Library Spectrum/Mineral Type Weighting Method (score-1) Spectral Angle Mapper (SAM) Score Spectral Feature Fitting (SFF) Score Binary Encoding (BE) Score 1 Kaolinite-KL Siderite-COS Calcite-CO Alunite-AL The n-d visualization was used in conjunction with the MNF and PPI tools to locate, identify and cluster the purest pixels. If spectral signatures are recorded properly and the curve shape is accurate they could be used for remote sensing applications. 25 Spectra can be taken as points in a dimensional scatter plot, where n is the number of bands. 24 The coordinates of the points in n-space consist of n values that are simply the spectral radiance or reflectance values in each band for a given pixel. The distributions of these points in n-space were used to estimate the number of spectral end-members (Fig. 6) The highest score indicates the closest match and shows higher confidence in the spectral similarity. Calcite and alunite scored high values respectively and 0.476, while siderite and kaolinite scored and 0.0, respectively, in SFF weighting. Scores of kaolinite, siderite, calcite and alunite are 0.758, 0.864, and respectively, BE weighting. SAM however, did not recognize any kind of minerals (zero score). The spectra of several clay/carbonate minerals were taken from the IGCP spectral library and those spectra were resampled to

6 S. PRABAHARAN & T. SUBRAMANI: IDENTIFICATION OF HYDROCARBON MICRO-SEEPS 1143 Fig. 5- Spectral library plots as well as end-member collection spectra Hyperion VNIR and SWIR band passes for comparison purposes [22]. The spectrum of advanced argillic mineral alunite is characterized by a diagnostic absorption feature near 2.165µm. When resampled to Hyperion band passes, the gross shape curve is preserved and the diagnostic features are depicted by a significant spectral absorption in band 5 (centered 2.165µm). 29 While Hyperion probably cannot separately identify these minerals species, it should identify the low ph/acid environments in which these minerals occur. IGCP library spectra of kaolinite characterized by a doublet shaped diagnostic absorption feature near 2.175/2.210µm. When resampled to hyperion band passes, the gross curve is preserved and the diagnostic features are depicted by an asymmetric absorption in band 6 (centered 2.210µm). 29 As the Hyperion data probably cannot identify these minerals species directly, kaolinite minerals should be identifiable as a group. The spectra of calcite are characterized by a single diagnostic absorption feature near 2.350µm. When resampled to Hyperion band passes, the gross curve is preserved and the diagnostic features are depicted by a symmetric absorption in band 8 (centered 2.320µm). 23,29 Siderite (FeCO 3 ) found extensively in sedimentary beds and is frequently contaminated with clay or organic matter. Siderite is characterized by a strong and board Fe 2+ band at 1.1µm, and some activity in the longer wavelength due to the carbonate, an opaque mineral, there is an absence of features in the visible region of the electro magnetic spectrum. Siderite is roughly the equivalent of calcite but of the curve is preserved and the diagnostic features are depicted by a symmetric absorption in band 8 (centered 2.320µm). Aluminium in kaolinite has found to substitute for Fe, which causes an infection on the long wavelength shoulder 23, 29 of the 2.2µm absorption. Spectral feature fitting 26 is another spectral library matching technique for classifying unknown image spectra. The SFF technique is to fit of unknown image spectra with reference spectra using a least squares technique. SFF is an absorption feature based methodology. If reference spectra is scaled to match the image spectra after the continuum is removed from both data sets. Brighter pixels in the scale image indicate a better match to the reference material in

7 1144 INDIAN J. MAR. SCI., VOL. 45, NO. 9 SEPTEMBER 2016 Fig.6- Pixel Purity Index (PPI) Plot and Pixel value of Image those pixels. The study area image and reference spectra are compared at each wavelength in a least squares sense, and the (RMS) error calculated for each spectrum. Dark pixels in the RMS error image indicate a low error. The scale image results to locate areas in best match the reference spectrum 22, 23. A particular strength of SFF is that it isolates individual absorption features for comparison, and only the shapes of the features are compared, not the depth of those features. The first step in the SFF analysis is the removal of the overall shape of the spectrum, known as the continuum, from the image and reference spectra. The continuum is formed by connecting the local maxima of the spectrum with straight line segments 22, 23. Without removing the continuum, it is difficult to define distinct absorption features because illumination and particle size differences tend to dominate the spectra. The image and reference spectra are therefore normalized by dividing the radiance or reflectance values by the estimated continuum values. [25] A constant is added to the library continuum-removed spectrum to provide a scaling factor in comparing the library and image data. This scaling is needed because the absorption features in the library data typically have greater depth than in the image spectra. Next, a least-squares-fit is calculated band-byband between each reference endmember and the unknown spectrum, using standard statistical methods. Three types of images are produced with SFF: scale, RMS, and fit image. The scale image, produced for each endmember, is the scaling factor used to fit the unknown spectra to the library spectra. The total root-mean-square (RMS) error is a measure of the average difference between the image spectrum and the library reference spectrum. Low RMS values are equivalent to good spectral matches. The fit image is the ratio of the scale image to the RMS image. 29 The fit image can be used to provide an overall perspective of how the unknown spectrum matches the reference spectrum on a pixel-by-pixel basis. The same endmembers and µm spectral region were chosen for the SFF classification as were used for the Spectral Angle Mapper (SAM) classification. While running SFF, this was the only parameter able to be modified. SFF does not produce a color-coded map, so post classification was required to generalize the classes. The ENVI program rule classifier 22, 23, was used to create a new classified image based on thresholds from the histograms for each endmember. The thresholds, chosen subjectively, represent a scaling factor for comparing the fit values. These thresholds varied between endmembers and even between images.

8 S. PRABAHARAN & T. SUBRAMANI: IDENTIFICATION OF HYDROCARBON MICRO-SEEPS 1145 Spectral feature fitting is a more statistical absorption matching technique than other spectral mapping. However, for this study, SFF was found to be well suited. Without expert knowledge of the distribution of the different minerals in the area, a satisfactory threshold for each mineral could not be determined for the rule classifier by which the mineral maps are 23, 29 combined to form a final classification. Consequently, the final maps appear to be well classified (Fig. 7). Calcite mineral is limited only to the area of the carbonate plain; it is dominated of Vedaranyam limestone formation, which is observed in limited zones associated with the streams in of Talainayar, Thiruthuraipoondi and Vedaranyam. Siderite mineral is observed in areas associated with alunite mineral; these areas in the carbonate plain are known as Madanam formation of limestone component, while in the area of recent deposits siderite located around alunite, this case could be observed in the area of Kalappal and Kovilkalappal. Anomalies Results Hydrocarbon is an important natural resource that finds much use in industries and vehicles. The present study is attempted using Hyperion data to identify hydrocarbon seepages based on mineral alteration in a part of Cauvery Basin. The results indicate that it is possible to identify and recognize certain oil/gas seepages based on the spectral characteristics. The The alunite is characterized by absorption of 2.165µm and Kaolinite absorption is near 2.175/0.210µm. Micro seepage of hydrocarbon along subsurface faults is greatly responsible for the development of alteration minerals such as Kaolinite, Siderite, Calcite and Allunite, which can be identified through remotely sensed data. Hence, the above altered minerals can act as an important tool for hydrocarbon prospecting. Therefore, this study concludes that mineral alteration mapping in petroliferous basins using Hyperion images is very much helpful in identification of new oil fields. This will be further helpful to reduce the exploration risk and to save time and cost. Fig.7- Hydrocarbon Prospective map in Part of Cauvery Basin

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